Eden Smith Group Ltd

Talent & Culture in the AI Era - Key Takeaways from the Data Decoded Roundtable

Mon, 27 Apr 2026 12:38:21 GMT

At Data Decoded LDN, our roundtable - moderated by Nicholas Deveney , Director of Consulting - brought leaders together for a practical discussion on what it really takes to build leadership capability for the AI era. The conversation reinforced a core truth: technology alone won’t unlock AI’s promise . Leadership behaviours, organisational culture, and the “how we work” foundations will determine whether AI becomes a force-multiplier or a source of risk and disappointment.

In line with the session framing - “The Talent and Culture Challenge: Building Leadership Capability for the AI Era” - discussion centred on how executives build data‑literate leadership teams, attract and retain scarce talent, and foster cultures that encourage adoption and trust.

Innovation needs a “portfolio mindset”

The room explored why AI initiatives stall after pilots: there’s a gulf between “low‑cost experimentation” and the true cost of productionising. Leaders often demand ROI too early, and teams struggle to justify investments when outcomes are uncertain - even though that uncertainty is inherent to innovation.

A compelling reframing was discussed: treat innovation as a sunk cost or strategic debt - like financial leverage - where the problem is not debt itself, but uncontrolled, ungoverned, untracked debt.

There was also discussion of structured experimentation models (e.g., small teams testing ideas rapidly and only moving proven outcomes into production pipelines), helping organisations sustain learning without betting everything on a single “big win”.

Executive takeaway: build a visible innovation portfolio with clear stages, decision gates, and transparent costs - rather than forcing every initiative to justify itself as guaranteed ROI from day one.

Power skills are now “data skills”: storytelling, collaboration, prioritisation

A consistent conclusion: the skills most organisations under‑invest in are the ones that make data and AI usable at scale - storytelling, communication, collaboration, and prioritisation .

Participants discussed practical methods that work because they reflect how humans actually learn:

Gamified learning and problem‑solving (including “escape room”‑style exercises)
Presentation constraints like PechaKucha to force clarity and narrative discipline
Cross‑functional sessions that bring “technical + business + storytellers” into the same room, rather than persona‑siloed training

Executive takeaway: if enablement is only technical, it will always lag the pace of change; invest in power skills that compound across tools, platforms, and waves of AI capability.

The workforce impact is real - and hiring signals must change

The discussion addressed concerns about AI reshaping career pathways, especially for junior roles, and what that means for future capability-building. There was frank debate about the consequences of automation removing early-career “learning through doing” work - and the risk of hollowing out pipelines if organisations don’t redesign development routes.

Recruitment practices were also questioned: CVs and applications are increasingly AI‑optimised on both sides, reducing their value as a signal. The group highlighted a growing premium on human interaction, judgement, and demonstrated thinking , suggesting interview processes and assessments need to evolve accordingly.

Executive takeaway: rethink hiring and progression around evidence of thinking (context, judgement, communication), not just polished artefacts.

Closing reflection: AI may make organisations more human - if leaders choose to lead

If there was one unifying message, it was this: the organisations that succeed with AI won’t be those who deploy the most tools fastest - they’ll be those who deliberately shape culture, decision quality, and capability while technology evolves around them.

Next Steps

These conversations reinforced why leaders value space away from the hype - to think clearly, challenge assumptions, and learn from one another.

If you’d like to explore these questions with peers who are grappling with the same realities -innovation trade‑offs, workforce shifts, and leadership in the AI era - we’d love you to join our leadership community so that you can be among the first to know of future events.

If these themes resonate, the question isn’t whether to adopt AI - it’s how deliberately you choose to lead through it.

The most resilient organisations are building portfolios, power skills, and human‑centred decision‑making alongside technology - not after it.

If you’d like to continue this conversation with our Director of Consulting or explore what this looks like in practice, let’s talk . You can also take a look at our value proposition to learn more about Eden Smith and how we work.

The Eden Smith Exec Lounge Summary

Tue, 21 Apr 2026 10:12:13 GMT

From Data Delivery to Durable Value: What Leaders Are Really Grappling With

At the latest Eden Smith Exec Lounge, senior data and technology leaders came together to explore a question that continues to challenge organisations across sectors:

Why does value from data and AI still feel so hard to realise - despite record investment, tooling and talent?

Rather than focusing on platforms or architectures, the conversation explored something deeper: how leadership behaviours, incentives and operating models shape whether data and AI ever translate into meaningful outcomes . What followed was one of the most open, energetic and honest discussions we’ve seen in the Lounge to date.

From delivery focus to outcome partnership

Helen Mannion opened the discussion by challenging a long‑standing assumption: that strong data foundations alone are enough to unlock value. While data quality and platforms clearly matter, the group reflected on a missing layer beneath the technology - a true outcome-focused partnership between data teams and the business .

Many organisations believe they have this partnership in place. In practice, the discussion surfaced a familiar pattern: data teams positioned as highly responsive delivery engines, while business teams assume value will emerge “once the foundations are built”. The result is activity, not outcomes - dashboards delivered, platforms migrated, models deployed - but limited behavioural change or decision impact.

A recurring insight was that delivery success is often mistaken for value creation , and that projects frequently stop just short of embedding, adoption and meaningful change.

When goals aren’t real goals

One of the most resonant themes was the idea that many initiatives begin with goals that aren’t truly shared outcomes . Leaders discussed how programmes often start with requests (“we need a dashboard”, “we need to move platforms”, “we need to implement AI”) rather than decisions, behaviours or business results that should change as a consequence.

Without a genuinely shared outcome, assumptions quickly creep in: assumptions about what stakeholders need, how success will be measured, and when work is “done”. This makes it difficult for data leaders to challenge direction and even harder to hold mutual accountability for results.

The pull back towards technology ownership

The group also reflected on why so many organisations appear to be drifting back towards technology‑led ownership of data and AI. Several contributors noted that increasing AI adoption is reinforcing this shift, with AI seen primarily as a technical capability rather than a strategic, organisational one.

The consensus wasn’t that where data sits matters most - but rather that without clarity of purpose, data leadership risks being measured on delivery speed rather than business impact , regardless of reporting lines.

AI, judgement and the risk of hollow progress

Building on these themes, Paul Parker introduced the people dimension of AI adoption - not just skills, but judgement, accountability and long ‑ term organisational resilience . While AI adoption is accelerating, the discussion highlighted growing concern that short‑term efficiency gains may be coming at the expense of capability building and future leadership pipelines.

Leaders shared lived experiences of AI outputs being accepted without sufficient challenge, context or understanding - creating new dependencies rather than better decisions.

A clear message emerged: AI can accelerate insight, but it cannot own judgement . That responsibility remains human and must be designed deliberately.

The human challenge beneath the technology

Throughout the session, participants repeatedly returned to one idea: delivery does not create value unless it changes behaviour . Whether discussing data platforms, AI tools or operating models, the group agreed that outcomes only materialise when organisations invest in the conditions that allow people to think differently, act differently and decide differently.

This includes how early‑career talent is developed, how experience is passed on, and how leaders balance urgency with stewardship. The conversation was notably candid on the risks of prioritising short‑term results at the cost of long‑term capability.

A community conversation, not a finished answer

As always, the Exec Lounge didn’t end with a neat conclusion - and that was the point. The strength of the evening was in surfacing tensions leaders are actively living with:

Outcome versus delivery
Speed versus sustainability
Automation versus judgement
Experimentation versus adoption

What united the discussion was a shared recognition that value from data and AI is not a technology problem alone - it is a leadership one .

We’re grateful to Jez Clark for hosting, and to Helen Mannion and Paul Parker for catalysing such a thoughtful and energising conversation - and to our community for continuing to show up with honesty, curiosity and generosity.

The dialogue will continue at our next Exec Lounge in July. If you’re a data leader and want to be part of the conversation, you can register your interest or join our closed LinkedIn group .

Transformational Leadership in the Age of AI

Wed, 25 Mar 2026 14:24:39 GMT

Executive Summary

Our Recent Leaders Advisory Board (LAB) event featured three sessions on business transformations in the new age of AI, with a human focus at the centre of the discussion.

Here's what we leant:

1. Leading AI Transformation: Building Intelligence with Human Capability at Core

Speaker - Sonia Aslam

Sonia’s session taught us about the three key considerations of AI transformation: Integrating human expertise, empowering employees with AI, and ethical and effective adoption . Without all three of these being fulfilled, AI transformation is being set up to fail.

Also raised was the question, “Are we Transforming, or just Piloting?” which began discussions on whether egos and clarity were getting in the way and distracting from actual transformation. We also looked at what else stalls transformation, which we found typically falls at the first hurdle of enacting the change that was decided in C-suites.

We rounded off this session with a breakout discussion, exploring a framework - developed by Sonia – which helps to break down each step of AI transformation so nothing is forgotten.

Within the discussion, it was largely decided that this kind of framework was hugely beneficial to building:

Expectations,
clarity, and
accountability

Sonia then left us with something to think about – to go revisit our own “zombie projects” and apply her framework to them, to either get the ball rolling on them again or to close them out. There’s not much better than being given a tool to clean up the workload.

2. Your Best Problem-Solvers Are Holding Your Business Back

Speaker - Alex Patient

Next up, we had Alex Patient, who led us through the story of his career and how he got to be where he is today – a leadership coach running his own business. What was important to his story was the lack of training and coaching that he received on leadership as he climbed the ranks in his career. Alex felt he didn’t belong in leadership, despite his success, which he pinned down to a lack of confidence. A lack of confidence, which could’ve been resolved by some coaching on how to lead. After a meeting with a new boss, Alex found a new way of thinking, and his mindset shifted, which began his later shift into coaching.

It all started in this meeting, where he was given the time and space to work through a simple question. Alex found this silence so helpful that it became the framework of his coaching, which we had a good go at in the session.

Alex’s framework uses 8 questions which the ‘coach’ should ask the ‘coachee’, with the key being for the ‘coach’ not to try and fix the problem or provide solutions, but to give the ‘coachee’ the space to work forward on their problem.

In the session, we gave the framework a go, and the response was so positive. Our own co-CEO, Jez, said that “it felt like a therapy session” , and others found it relieving to be given the space to work through their problems without feeling like they were being rushed into finding a solution.

3. 3D Transformation – Discipline, Decisions & Delivery

Speaker - Darren Minshall

Our final speaker, Darren Minshall, took us on a thought-provoking journey through AI transformation, focusing on the 3 Ds: Discipline, Decisions, and Delivery . Which he claims is the key to successful transformation.

Starting with where most organisations fail –spoiler alert - it’s not in the strategy. Darren has found that transformation fails when there is a lack of clarity within leadership, decision velocity has slowed (or stopped), and when there is a lack of discipline in the execution.

Darren had us think back to all the charts we’d seen of year-long plans of how AI transformation was going to fix or change the whole organisation, with big plans to lower costs and redesign operating models, but then 6 months ( or even less) down the line, the transformation had become stagnant. Why? Because the leaders at the top had passed the change over to the L2 group and left it to them – here’s that lack of execution discipline that Darren talked about earlier.

To have a successful transformation, you must first identify which dimension is the weakest in your organisation:

Discipline?
Decisions?
Or Delivery?

And then take three executive actions:

Define what stops
Align incentives to the future states, and
Reset leadership rhythm

At the heart of all we discussed is that PEOPLE come first – even when were talking AI.

We’ve had such great feedback from this first session of the year, a great start and platform for us to launch from.

We will be hosting a new LAB event every quarter this year, so if you’d like to join the next one, join our Leadership Advisory Board on LinkedIn , and if you’d be interested in being a future speaker, get in contact with us .

The Unexpected Perils of Rural Litter Picking (and the Adidas-Wearing Sheep Responsible)

Wed, 11 Mar 2026 09:30:48 GMT

If I’m honest, sometimes we all just need a bit of a kick up the bum .

Not a dramatic life overhaul. Not a silent retreat in the mountains. Just a gentle nudge to stop talking about doing good things and actually… doing them.

For me, ES10 — Eden Smith’s 10-year anniversary volunteering initiative — has been exactly that. A very welcome kick up the bum.

It’s also helped me combine two goals I’ve been meaning to prioritise :

Getting outside and walking every day
Doing something that gives back with purpose

So I decided to start litter picking on my daily walks .

Now, I live in the countryside. The sort of place people escape to in order to get away from the pollution of the city. Rolling fields. Quiet lanes. Fresh air. The kind of setting that suggests litter would be a rare and mysterious phenomenon.

Naturally, I assumed the culprits were obvious.

Clearly it’s the Adidas-wearing, cider-swilling, crisp-popping sheep that roam the surrounding fields causing all the havoc.

I mean, have you seen them? Suspiciously casual about the whole thing.

Unfortunately, after several weeks of investigation (and the sheep maintaining a firm policy of plausible deniability), it turns out the responsibility probably lies a little closer to home with the humans passing through.

But rather than dwelling on that, I’ve been quietly enjoying the process of doing something small that makes a visible difference.

Because here’s the interesting thing: litter picking is oddly satisfying .

There’s a strange joy in turning a walk into a mini mission. Spotting something in a hedge, retrieving it, and leaving the place just that little bit better than you found it. It’s simple. It’s tangible. And it reminds you how small actions add up.

That said, like any good field experiment, there have been a few learnings along the way.

Three Key Litter-Picking Learnings

1. Add brambles and barbed wire to the risk assessment.
Countryside hedgerows are essentially nature’s version of an obstacle course. What appears to be a harmless crisp packet will almost certainly be guarded by aggressive brambles or suspiciously well-placed barbed wire . Approach with caution.

2. Always tip the liquid out of cans before bagging them.
This lesson was learned the hard way. Nothing quite ruins the triumphant moment of adding a can to your bag like discovering it contains half a pint of mysterious roadside liquid that then empties itself directly into your litter sack (and occasionally onto your shoes).

3. Bring a bigger bag than you think you’ll need.
You start thinking you’ll pick up “just a few bits.” Twenty minutes later you’re carrying a surprisingly heavy sack wondering how a single stretch of road managed to generate three cans of cider, two crisp packets, and something that used to be part of a sandwich wrapper in 2007 .

Why This Matters

The point of ES10’s volunteering drive isn’t about grand gestures. It’s about creating impact in lots of small ways .

For me, this one has been perfect. It gets me outside every day , combines well-being with purpose, and leaves the local area a little cleaner than before.

It’s also a reminder that doing something is infinitely better than doing nothing , even if it starts with a small act like picking up a discarded crisp packet.

And if we all did a little bit more of that, we might even put those Adidas-wearing sheep out of business.

How Data, AI and Governance Are Reshaping the Lloyd’s Market

Mon, 09 Mar 2026 11:21:43 GMT

A Market Commentary for the Lloyd’s and Specialty Insurance Community

The Next Era of Innovation in the Lloyd’s Market

The Lloyd’s insurance market has never lacked innovation. From insuring maritime trade in coffee houses to underwriting cyber risk and space launches, its history is defined by adaptation. Yet the changes now emerging across data, artificial intelligence (AI) and governance represent something fundamentally different: not simply a new class of risk, but a new operating model for insurance itself.

Over the next five years, competitive advantage in specialty insurance will increasingly depend not on balance sheet strength alone, but on how effectively firms transform data into decision-making capability.

For managing agents, brokers and cover holders operating in the Lloyd’s ecosystem, the ability to harness structured data, deploy explainable AI models and maintain robust governance frameworks will become central to underwriting performance and regulatory confidence.

From Digital Market to Intelligent Market

Over the past decade, the Lloyd’s market has invested heavily in digitisation and market modernisation. Initiatives such as electronic placement platforms, structured bordereaux and improved data standards have laid important foundations for a more efficient insurance ecosystem.

However, these efforts were never the final destination.

Artificial intelligence is now moving directly into underwriting workflows. Rather than replacing underwriters, modern AI tools augment human expertise by rapidly synthesising submissions, historic exposure data and external intelligence sources.

For complex specialty risks, this enables:

Faster submission triage
More consistent risk evaluation
Improved underwriting efficiency
Better visibility across portfolios

Human judgement remains central, but AI enables underwriters to operate with significantly enhanced analytical capability.

In a subscription market like Lloyd’s , where multiple carriers participate in complex risks, the ability to interpret and evaluate exposures faster than competitors may become a defining competitive advantage.

The Structural Challenge for Specialty Insurers

Despite technological progress, many syndicates continue to wrestle with fragmented exposure data, inconsistent bordereaux formats and legacy system dependencies.

This challenge is becoming increasingly visible as firms attempt to scale AI capabilities.

Across the insurance industry, organisations are discovering that AI performance depends less on model sophistication and more on data reliability. Poor data lineage, unclear ownership and inconsistent definitions can significantly undermine automation initiatives.

As a result, data governance in insurance is evolving from a compliance requirement into a strategic capability.

Leading Lloyd’s market participants are prioritising:

Standardised exposure data structures
Improved data ownership and stewardship
Consistent bordereaux formats
Robust data lineage and auditability

Trusted and standardised exposure data not only improves operational efficiency but also strengthens capital modelling, risk aggregation analysis and regulatory reporting .

For specialty insurers navigating complex global risks, data integrity has become a competitive asset.

Regulatory Expectations and the Rise of Accountable AI

Alongside technological change, regulatory expectations across the UK and European insurance markets are evolving rapidly.

Supervisors are increasingly focused on AI explainability, model oversight and fairness in automated decision-making.

For Lloyd’s managing agents and specialty insurers, this introduces new responsibilities. Organisations deploying AI in underwriting, pricing or claims workflows must ensure they can clearly demonstrate:

How models reach their conclusions
How bias and fairness risks are mitigated
How model drift is monitored over time
How decisions remain auditable and explainable

While these expectations introduce additional governance requirements, they may ultimately strengthen the global reputation of the Lloyd’s market.

Trust has always been the foundation of the Lloyd’s brand. As artificial intelligence becomes embedded in underwriting decisions, accountable AI frameworks extend that trust into algorithmic decision-making.

Lloyd’s as a Shared Data Ecosystem

One of Lloyd’s most distinctive advantages is its ecosystem structure . Brokers, cover holders, managing agents and capital providers operate within a shared marketplace that enables collaboration across complex risks.

As structured data standards continue to mature, the market has an opportunity to evolve into something even more powerful: a shared intelligence ecosystem .

By improving data interoperability across the market, participants could unlock powerful benefits, including:

Enhanced catastrophe modelling
Improved portfolio aggregation management
Faster detection of emerging risks
More accurate pricing across specialty classes

In this environment, the competitive battleground shifts.

Success is no longer defined simply by access to information, but by the ability to interpret, contextualise and act on data faster than competitors.

New Risks Emerging from Artificial Intelligence

Artificial intelligence is not only transforming insurance operations; it is also generating entirely new categories of risk.

Across industries, organisations are deploying AI-driven systems in decision-making processes ranging from financial services to autonomous technologies. This rapid adoption introduces new forms of liability and exposure.

Examples already emerging include:

Deepfake documentation and identity fraud
Algorithmic decision liability
Autonomous system failures
AI-generated misinformation risks

Specialty insurers are therefore navigating a unique dual challenge.

They must deploy AI internally to enhance underwriting and fraud detection capabilities, while simultaneously developing new products to insure the risks created by AI adoption across the global economy .

The industry is entering a period in which insurers must increasingly underwrite the consequences of the very technologies reshaping their own operations .

The Emergence of the Real-Time Insurer

Looking ahead, the long-term trajectory for the insurance industry points toward continuous, real-time operations .

Traditional insurance models rely on periodic risk assessments and static pricing structures. Emerging technologies are enabling a more dynamic approach.

In a real-time insurance environment , firms may eventually operate with:

Continuous risk monitoring
Dynamic pricing models
Proactive risk prevention strategies
Data-driven claims prediction and intervention

For the Lloyd’s market, whose core strength lies in underwriting complex and evolving risks, this shift aligns naturally with its heritage.

However, achieving this vision requires faster data flows, deeper collaboration across the ecosystem and robust governance frameworks that maintain trust while scaling automation.

Preserving Trust While Scaling Intelligence

The Lloyd’s market has endured for centuries because it adapts without abandoning its core principles.

The current transformation driven by data, AI and governance in insurance will test that balance once again.

Success will not come from adopting AI tools alone. It will depend on building trusted data foundations, embedding governance frameworks into technological innovation and enabling underwriters to operate with augmented intelligence rather than automated replacement .

Insurance has always been about managing uncertainty.

The next chapter will require managing intelligent systems with the same discipline and rigour the market has long applied to risk itself .

The transformation of the Lloyd’s and specialty insurance market is accelerating as data, artificial intelligence and governance reshape underwriting, risk management and regulatory expectations.

If your organisation is exploring how to implement AI responsibly, strengthen data governance frameworks or unlock value from insurance data , we would welcome the opportunity to share insights and discuss the practical implications for the market.

Get in touch with James Mairs to continue the conversation on the future of intelligent insurance and click here to learn more about our 'Industry In Focus' event. This month we are taking a deep dive into the insurance industry so join us in London on the 25th March.

Delivering Business Value Through Data Migration and Transformation Projects

Mon, 09 Mar 2026 10:42:26 GMT

Delivering Business Value Through Data Migration and Transformation Projects

Data migration and transformation initiatives have become a critical component of modern organisational strategy. As companies adopt new technologies, retire legacy systems, and invest in digital transformation, the ability to move, structure, and interpret data effectively becomes essential for maintaining operational efficiency and enabling informed decision-making.

However, the success of these projects rarely depends solely on technology. Instead, they rely heavily on collaboration between multiple roles within an organisation, particularly Business Analysts, Data Engineers, and Business Intelligence developers, such as Power BI specialists .

When these roles work effectively together, organisations can move beyond simple data migration and unlock meaningful insights that drive business value.

Understanding the Business Need

Many organisations initiate data migration projects to address challenges such as fragmented data sources, outdated reporting systems, or inconsistent data definitions across departments. Without a unified view of data, decision-makers often struggle to gain reliable insights into performance and strategic opportunities.

At the early stage of such initiatives, working with stakeholders across different business units to understand how data is currently being used and what improvements are required. This process often involves s takeholder workshops, interviews, and process analysis to document the current state of operations.

Through this engagement, analysts identify inefficiencies, reporting gaps, data governance and inconsistencies in data usage. These insights help define the future state vision , which outlines how data should be structured, accessed, and used to support decision-making.

Collaboration with Technical Delivery Team

This typically involves building data pipelines, integrating multiple data sources, and applying transformation logic to ensure data is structured and consistent. Defining the business rules that govern how data should be interpreted, while Data Engineers focus on implementing these rules within the technical environment.

Activities during this stage often include:

Defining data mapping between legacy systems and new platforms
Establishing transformation rules to standardise datasets
Identifying data quality issues such as missing or duplicated records
Validating data sources and structures

In many cases, analysing the underlying data through SQL queries or exploratory analysis helps identify inconsistencies before migration begins. Addressing these issues early reduces the risk of transferring flawed data into new systems and ensures that the final datasets are reliable.

Turning Data into Meaningful Insights

While migrating data successfully is important, the real value of these initiatives lies in the ability to transform data into insights that support business decisions.

Once the data has been migrated and structured, another important role will be in creating dashboards and reporting solutions that allow stakeholders to interpret and act on the data.

Working alongside Business Analysts, these developers design visualisations that reflect the organisation’s key performance indicators (KPIs) and operational priorities. This collaboration ensures that dashboards are not only technically functional but also aligned with the questions stakeholders need answered.

Typical outputs may include interactive dashboards that track:

Sales pipeline performance
Revenue and profitability trends
Operational performance metrics
Product or customer insights

Supporting Agile Delivery

Many data transformation projects are delivered using Agile methodologies . In these environments, requirements evolve as new insights emerge during development.

Agile delivery by maintaining structured backlogs, defining user stories, and ensuring that development teams have a clear understanding of priorities .

Regular sprint reviews and stakeholder feedback sessions ensure that the evolving solution continues to align with business objectives.

Ensuring Quality Through Testing

Before a new data platform or reporting system is released, it must be validated to ensure it meets business expectations. User Acceptance Testing (UAT) provides stakeholders with the opportunity to confirm that the system works correctly and delivers accurate information.

During this phase, Business Analysts often coordinate testing activities, develop test cases, and support stakeholders in validating system outputs.

This process helps confirm that:

Data has been migrated accurately and reconciled .
Business processes function correctly within the new system
Reports and dashboards generate reliable insights

Successful testing builds confidence among users and ensures that the system can be adopted effectively across the organisation.

Unlocking the True Value of Data

Data migration projects are often perceived as technical infrastructure initiatives. However, their true impact lies in enabling organisations to become more data-driven and insight-focused.

Through well-defined requirements, reliable data pipelines, and meaningful reporting tools, organisations can improve operational efficiency, identify strategic opportunities, and respond more effectively to changing market conditions.

Ultimately, the success of a data migration project is measured not by the movement of data alone, but by the business value created from the insights that data provides .

If you’re planning a data migration or transformation project and need an experienced team or people to support your business goals, we’d love to book in a strategy call to discuss your unique challenges.

To learn more about how we can help, get in touch , or read more of our articles .

Ethics As People, Not Compliance

Thu, 05 Mar 2026 12:34:27 GMT

Ethics As People Change,

Not Compliance

Why Ethical AI Keeps Failing in Organisations

As AI is being adopted by more organisations, it has become clear that there needs to be some guardrails for its implementation… Welcome, ethical AI.

Ethical AI is the practice of designing, deploying and governing AI systems in ways that are fair, accountable, transparent, and aligned with human values. It is typically implemented through technical safeguards and internal policies, like the document your HR sent you about using ChatGPT that you skimmed.

Ethical AI is important for organisations to prevent the perpetuation of biases and to ensure that AI systems are fair. But here’s what organisations often miss: ethical AI should be embedded into an organisation, rather than added on as a tick box. Failure to embed ethical AI into an organisation creates the familiar pattern of distrust in AI systems.

The Compliance Trap

When an organisation attempts to implement ethical AI, it often falls into ‘The Compliance Trap’. This occurs when there has not been sufficient embedding into the workflow, but instead, added as an afterthought or for compliance, and actual transformation cannot occur.

But why do we keep seeing this happen? Organisations fall into the compliance trap because the initial implementation of ethical AI creates a false sense of control. Policies are written, frameworks are approved, and sign-offs are completed; everyone settles into a comfortable routine again. But nothing really changes.

Compliance doesn’t change how:

Teams make decisions
Trade-offs are prioritised, or
How pressure is handled in delivery

This results in ethical AI being something to get through, instead of something to work with.

Ethical AI as a change problem

So, if ethical AI is always treated as a compliance issue, then it’s becoming a change problem.

Implementing AI reshapes the way people in the organisation view authority, accountability and trust. This should be expected when changing the fundamentals of a workplace, but it’s important to remember that this is a human and organisational change, and not a technical one, and that the solution to these issues lies with the people.

These failures are more likely to occur when organisations:

don’t prepare people for these shifts,
don’t redesign decision-making, and
don’t align incentives with responsible behaviour.

Ethical AI as a change problem

So, if ethical AI is always treated as a compliance issue, then it’s becoming a change problem.

These failures are more likely to occur when organisations:

don’t prepare people for these shifts,
don’t redesign decision-making, and
don’t align incentives with responsible behaviour.

Where Ethical AI breaks down during transformation

So what about the people and the organisations make this fail?

Firstly, there is often a distance from leadership, which delegates ethics downwards and creates a culture where ethics are perceived as less important. Leaders approve an AI strategy but then fail to model ethical-decision making, so when it becomes someoneelse’s responsibility, there aren’t frameworks or guidelines to work from, and ethics gets passed over.

Next, there is an issue when it comes to delivery pressure. Teams are rewarded for the scale and speed of a project, whether that reward be commission, promotions or just praise, taking things slower often puts teams at a disadvantage. But ethics thrives when the time is slowing things down, and when the time is taken, ethical AI can thrive.

As with many other aspects of AI adoption, there are capability gaps when it comes to ethical AI. People are expected to do the right thing, especially when it comes to ethics; however, not much more than an AI policy is given in terms of training, leaving gaps in what people are prepared to complete.

Finally, and possibly one of the biggest factors, is late engagement. By late engagement, we mean that ethical considerations are being discussed only after key decisions are already made, leaving little to no room to adapt or account for them. This not only means that in that project, ethics isn’t being considered, but then it gives leeway for ethics to be sidelined for other projects as well.

Ethics as a transformation capability:

Looking forward, ethics must be considered as a transformation capability, meaning that there must be a whole transformation of the workflow for ethical AI to be successful.

Ethical AI works when it is treated as ;

Leadership Capability : meaning that ethics are shaping how problems are defined a.
Delivery Capability: meaning that ethical reflection is built into delivery systems; and a cultural norm, where everyone is on the same page of how ethical AI should work and challenges AI-driven decisions.

Making this transformation changes ethical AI from an external control, to just the way things are done.

Practical checklist

Leadership & Direction
Leaders discuss the ethical trade-off in AI decisions
Accountability for AI outcomes is clear and owned
Ethics is framed as value creation
Design and delivery
Ethical considerations are included from the beginning
Assumptions about users, data and ‘normal’ behaviour are challenged
Ways of working
Teams are empowered to question AI outputs
Escalation paths can be used without penalty
Culture and capability
Training is given on role-specific ethical risks
Challenge is rewarded
Ethics is talked about in real scenarios.

What changes when ethics is treated as change:

When these changes have been made, and the checklist has been complete ethical AI will allow organisations to thrive. People will have greater trust in the processes and decisions made by AI, leading to greater adoption because people now believe and understand the systems.

When more people are more comfortable using ethical AI, it reduces reputational risk and the need to rework or reverse previous work. This turns ethics from a blocker to an enabler of transformation.

Want to learn more? Join our Leaders Advisory Board here , or explore our other articles.

Nurture Programme Student Spotlight: Charles Adeiye

Tue, 03 Mar 2026 17:13:21 GMT

Celebrating 10 Years of Eden Smith & Building the Next Generation of Data Talent

As Eden Smith celebrates its 10-year anniversary , it’s inspiring to reflect on how initiatives like the Nurture Programme are shaping the next generation of data and analytics talent. Charles ’ journey is a strong example of how early industry exposure can accelerate careers, while helping employers build a future-ready pipeline of junior talent with real-world experience. Charles joined the Nurture programme and worked on a live project with CLEO on predicting whether users regret what they spend their money on.

Please tell us a bit about yourself

I was born and raised in Lagos, Nigeria, where I completed my early education. During secondary school, I developed a strong interest in Mathematics and Physics, which inspired me to pursue Civil Engineering. I went on to earn a Second-Class Upper Division in B.Eng. Civil Engineering from the University of Ilorin.

After graduating, I joined KPMG as a Technology Analyst in the Emerging Technology, Data, and Analytics Unit. Working there sparked my interest in data and analytics and motivated me to pursue a deeper career in the data space. To build on this passion, I moved to the UK to complete a Master’s degree in Data Science.

While studying, I took part in a three-month project-based internship at Cleo through the Eden Smith Nurture Programme. After completing my master’s, I joined Barclays, where I now work as a Data Analyst.

Why he applied to the Nurture Programme

A few weeks into my studies at Salford, I realised that academic learning alone wouldn’t be enough to stand out in a competitive job market. I wanted meaningful, hands-on experience that would allow me to apply my academic knowledge in real business environments.

When I discovered the Nurture Programme , it immediately stood out. The opportunity to work on real-world challenges, contribute to an organisation, and translate theory into practice was exactly what I was looking for. The programme offered the perfect balance of learning, application, and impact.

Live Project Experience at Cleo

If I had to describe my live project experience in one word, it would be “challenging.” I worked at Cleo , a fast-paced and inspiring environment filled with intelligent and supportive colleagues across different roles.

The project pushed me beyond what I had learned academically. I worked with new tools and explored creative problem-solving approaches that I hadn’t encountered in a classroom setting. This experience strengthened my analytical thinking, collaboration, adaptability, and technical confidence, and played a major role in my professional development.

Life after the project

A few weeks after completing my live project, I joined Barclays through the Technology Analyst Explorer Graduate Programme. After finishing the programme, I transitioned into a permanent Data Analyst role, where I currently work.

Impact on his career

My live project had a significant impact on my career path. I worked on complex analytical tasks using advanced SQL and Python, skills that I now use regularly in my role as a Data Analyst.

I also gained experience working with machine learning models and large datasets, and developed confidence using cloud-based technologies such as Amazon Redshift. These skills have been directly transferable to my current work and have helped shape my confidence, career decisions, and technical growth.

The experience opened doors, strengthened my technical foundation, and gave me real-world credibility that continues to support my career today.

Reflecting on Eden Smith’s Impact

As Eden Smith marks its 10-year milestone, programmes like Nurture highlight the organisation’s commitment to developing next-generation talent and supporting employers with a pipeline of industry-ready junior professionals.

By connecting ambitious students with real business challenges, Eden Smith is not only transforming individual careers, it’s helping organisations future-proof their teams, accelerate innovation, and invest in sustainable talent strategies.

It’s exciting to see how the next generation of students and employers continue to benefit from this impactful Nurture Programme.

Looking to secure future-ready junior talent with real industry experience?
Join the growing list of organisations partnering with Eden Smith’s Nurture Programme. To explore partnership opportunities or submit a project for an upcoming Nurture cohort, contact Marie May or complete the Client Project Scope Application here: https://www.edensmith.group/nurture%20project%20scope

What a Decade of Nurture Taught Us About Closing the Skills Gap

Tue, 10 Feb 2026 21:40:52 GMT

When Eden Smith launched in 2016, the UK data landscape looked very different. Demand for data science, AI and analytics capability was accelerating, but the pipeline of job ready talent simply couldn’t keep up. Universities were producing bright, technically capable graduates - yet employers continued to cite the same challenge: “We can find smart people, but not people ready for real world delivery.”

The Nurture Programme was born to bridge the gap between academia and industry.

Ten years later, the Nurture Programme has evolved into an award-winning ecosystem connecting universities, students and employers through live industry placements, real world project experience, soft skills development and industry aligned learning .

Across this decade, one truth has become clear: closing the skills gap isn’t about teaching more tools - it’s about bringing academia and industry together in a structured, human-centred way.

This article explores the key lessons Nurture has taught us.

1. The skills gap is real - but it is not purely technical

In every university partnership, one pattern emerged: technical skills alone don’t guarantee employability . Employers consistently seek graduates who can think critically, collaborate effectively, communicate insights, and apply their knowledge in ambiguous, real-world contexts.

Nurture’s core design intentionally blends technical learning with structured human skill development. In person workshops focus on critical thinking, curiosity, storytelling, stakeholder engagement, communication, creativity and business mindset , delivered alongside industry specialists.

Virtual webinars reinforce this with topics such as data governance, ethical data, sustainability, visualisation psychology, and the intersection of business and data - all areas where industry need moves faster than traditional curriculum cycles.

The lesson?
Employers don’t just need coders. They need translators - people who can bridge data and the business.

2. Industry must play an active role in shaping academic learning

Universities consistently tell us that one of their greatest challenges is keeping content aligned to rapidly evolving industry needs. The Nurture model addresses this by harnessing industry partners to co-develop and deliver relevant course content, webinars and workshops.

This co-delivery approach elevates academic programmes by:

Ensuring students understand real-world expectations
Bringing modern tools, frameworks and challenges directly into the classroom
Providing industry feedback loops that help universities refine courses
Increasing course attractiveness by offering LIVE project experience

The outcome is clear: programmes connected to industry attract stronger cohorts and produce more employable graduates.

3. Live projects are the most powerful catalyst for employability

The Nurture Programme’s signature feature - the 3-month live industry placement - has proven to be the most effective bridge between education and employment. Students apply newly developed technical skills to real business problems, working as part of a professional data team.

These projects are not theoretical exercises. They involve:

Real datasets
Real stakeholders
Real deadlines
Real organisational constraints

And crucially, they often lead to real jobs .

One standout example:
A Nurture student at National Highways built the foundations for a customer facing chatbot using Microsoft PowerApps and Dataverse. The prototype saved thousands in operational costs and impressed leadership so much that the student was introduced to the CEO and offered a Data Scientist role.

This isn’t an isolated story - it’s a pattern repeated year after year.

4. Structured employability preparation matters just as much as technical training

Alongside live projects, Nurture includes tailored employability preparation: from 1:1 interviews to help students understand recruitment expectations, to sessions focused on career positioning and the modern workplace .

Workshops such as “Positioning for Employment” have even been used in community bids, highlighting their value beyond university cohorts.

This preparation ensures students enter the job market not just with skills, but with confidence , awareness of professional norms, and readiness to articulate their value.

5. Closing the gap requires inclusive opportunity, not elitism

A consistent theme across Nurture partnerships is inclusivity. The programme prioritises creating opportunity for all MSc Data Science, IoT and AI students . The universities we work with attract a diverse cohort – those who are attracted to the opportunity to take part in Nurture. Nurture is open to everyone – not only the ones with industry connections nor the most experienced or technically advanced. It's about levelling the playing field to identify the brightest minds.

This inclusive approach:

Broadens access to industry exposure
Supports a diverse pipeline of emerging talent
Promotes social mobility
Helps employers discover exceptional candidates they might otherwise overlook

Real world outcomes reinforce this: students who may not have traditional industry connections secure placements - and often permanent roles - because the programme creates a level playing field.

6. The skills gap is both a tech challenge and a people challenge

Nurture’s decade of learning underscores a fundamental truth: data and AI transformation is as much about people as technology . Industry frequently cites shortages in applied experience, business understanding, communication, and adaptability - areas where university curricula cannot easily keep pace.

By integrating people-centred learning - collaboration, curiosity, communication and business awareness - Nurture produces talent that is not just employable, but impactful .

The data industry’s needs will continue to evolve, especially with rapid developments in generative AI and data enabled decision making. But one constant remains: graduates must be prepared not only to do the work , but to understand the work , explain the work , and work with others to make change possible.

7. A scalable model for national skills development

The Nurture Programme’s decade-long track record shows a scalable blueprint for bridging academia and industry at a national level:

Industry-aligned learning
Live projects that directly benefit employers
Soft skills development embedded alongside technical learning
Inclusive cohort design
Proven employment pathways
Direct feedback loops reshaping academic content

This model supports national objectives to upskill the next generation of bright minds , while giving employers immediate access to additional capacity, fresh thinking and a future talent pipeline.

In short: it works - for students, universities and employers.

"It’s a milestone worth celebrating! Ten years of Eden Smith Group means ten years of driving innovation, nurturing talent, and making an impact across the data and AI industry. We’re proud of what we’ve achieved and even more excited for what’s next as we continue to innovate, collaborate, and grow together.’’
Marie May – Head of Graduate Success and Communities

What the next decade demands

As data and AI adoption accelerates, the UK cannot afford a talent pipeline defined by misalignment. The next decade requires:

Deeper partnerships between universities and industry
More live, applied project experience
Human-centred training that complements technical expertise
Greater inclusivity in access to opportunity
Programmes that evolve at the pace of industry change

The Nurture Programme demonstrates that when academia and industry collaborate meaningfully, the skills gap narrows - not through theory, but through experience, connection and real world learning.

After ten years, the lesson is simple:

To prepare future data leaders, we must nurture people, not just teach content.

If you’re ready to play an active role in developing confident, capable and diverse data talent through real-world experience, we’d love to begin the next chapter together. Get in touch with marie.may@edensmith.co.uk and we can create inclusive opportunities that open doors for all emerging talent, connect students with meaningful projects, and ensure the next generation of data professionals are prepared to make an immediate impact, today and for years to come.

Why AI Transformation Fails

Tue, 10 Feb 2026 11:03:28 GMT

Why AI Transformation Fails

AI is everywhere – but is it creating value?

The introduction of AI into businesses has dominated the conversation over the last 18 months, increasingly it is growing more relevant. More and more businesses are adopting AI technology in an attempt to improve, enhance and evolve - but we keep seeing it fail time and time.

Why is it important that we’re getting AI right?

With the rollout of generative AI (GenAI) to the public through tools such as ChatGPT and CoPilot, the use of AI has skyrocketed. Recent studies suggest 70% of Brits use GenAI in their daily lives, and 44% admit to using it in their workplace. We know people are using it - and some are even relying on it to get the job done. That’s why it’s crucial that it’s being done right, because people aren’t going to stop using it , even when it’s not working.

So why is AI failing for so many? And how can we fix that?

The failures of AI adoptions are clear. While exact figures vary by study, the trends consistently show us that large-scale adoption of workplace AI initiatives is not reflected by the investment. Despite $30-40 billion being invested into GenAI in 2025, there was only a 5% success rate of AI adoption. This means that up to 95% of AI initiatives are losing companies money.

The Missing Piece: PEOPLE

But why? Because the people are being ignored

AI adoption is proven to be more successful when training has been provided on how to best use it, and when there is cohesion and collaboration in a team when implementing it.

A MIT study shows that those 95% of businesses that have failed to adopt AI technology failed due to:

brittle workflows,
lack of contextual learning, and
misalignment with day-to-day operations.

This misalignment in AI training and adoption is reinforced by a BCG study. The study identified that there was a significant skills gap between the corporate leaders and the frontline employees. The study found that 83% of managers and leaders use AI at work regularly, but only 21% of employees reported that they received sufficient training to use AI effectively.

A glimmer of hope

It’s not looking all bad for AI and businesses; however, small-scale use of AI has proven to be successful. On an individual employee level, tools such as AI calendars, email dictation, and the automation of mundane, repetitive tasks have increased employee performance. A recent LSE study found that GenAI can save on average 7.5 hours a week of working time. This is almost a whole day that can be spent doing something much more worthwhile!

Fear, Fiction and the Trust Gap

A question that arises when we look at these studies is, why are people so hesitant to use AI?

A major issue is distrust. Although 82% of people have used AI, only 57% feel comfortable with it - one of the lowest confidence rates in the study. The topics of concern are typically centred around trust, privacy and control.

We have embedded a recurring cultural reaction to innovation, often rooted not in technology itself but in fears about its social consequences (job loss, dehumanisation, loss of control). Just think Mary Shelley’s Frankenstein, 2001: A Space Odessy and Ex Machina. As we move forward, there needs to be much more education on changing long-established mindsets and behaviours.

While film and TV dramatise AI, news media have also played a role in reinforcing concerns. Journalists have been on top of the ball when it comes to reporting on the failures of AI. While it is important for people to understand the limitations and failures that the technology has experienced - and not place all the blame for the failures on people - it has also been criticised for over-reporting on the subject for click-bait.

A People-First Path Forward

Let’s go back to the beginning. Why is AI adoption failing?

It’s because of the people using it - or more accurately - the lack of support they receive. AI technology being introduced into businesses is rarely introduced properly within organisations, and the goals it is meant to achieve are often unclear. This lack of clarity and training, combined with fears fuelled by media narratives, creates resistance from the very start. This ultimately leads to failure.

So, what do we do moving forward? Work with people.

The key takeaway is that successful AI integration requires a people-focused approach. Change management helps ensure teams are prepared, supported, and confident from the outset.

Through role-specific training and open discussion around concerns, AI is far more likely to become a positive and productive addition to the workplace. Change management can also help dismantle harmful narratives around AI by creating space for honest conversation and education.

Here’s How To Best Implement Change Management

Clarity : Ensure everyone understands what AI is meant to solve and what its goals are
Capability : Training should be tailored to how individuals will actually use AI, rather than being generic.
Confidence : Create space to discuss the concerns about risks to job safety and privacy
Continuity : Avoid unnecessary disruption - AI should be embedded into workflows, not bolted on

Want to dive deeper into the discussions around AI adoption failures? Check out our AI Horizons podcast or get in touch to see how we can work together.

Looking Back with Pride, Looking Forward with Purpose

Mon, 09 Feb 2026 13:45:15 GMT

In 2016, Eden Smith launched as a specialist data recruitment startup with a simple ambition: to help organisations find the talent they needed to unlock the value of data. Over the past decade, that mission has grown far beyond its original scope. We’ve evolved into a data and AI consulting partner supporting organisations across every sector - from FTSE 100 giants to ambitious charities - all united by a shared desire to transform through data.

That evolution is part reflection, but mostly momentum. Because the story of our first 10 years is really the foundation for what comes next.

From Recruitment Roots to Transformation Partner

Our early years taught us what organisations were truly grappling with: not just finding skilled people, but building the environments, capability, and confidence to use data and AI meaningfully. That insight reshaped us. We expanded from talent acquisition into strategic consulting, capabilitybuilding, and handson implementation.

In doing so, we didn’t just support the industry, we grew with it.

And now, in 2026, data has become the critical infrastructure of every modern organisation. AI is reshaping how we work, innovate, and deliver value. The landscape is bigger, faster, and more dynamic than ever before.

Which is exactly why Eden Smith’s next decade will be even more transformative than the first.

A Forward Vision for Eden Smith and the Data & AI Industry

AI Becomes Operational - and We Help Make It Real

Most organisations are no longer asking why AI matters; they’re asking how to implement it safely, successfully, and sustainably .

Our focus will be on:

Embedding AI into everyday workflows
Helping clients modernise legacy systems
Ensuring governance, ethics, and responsible AI adoption
Delivering measurable outcomes, not experimentation

AI capability will become a baseline, and we are on a mission to make that baseline achievable for everyone.

Data Foundations Will Be NonNegotiable

As AI accelerates, strong data governance, quality, architecture, and interoperability become missioncritical.

We’ll support organisations to:

Build resilient, futureproof data foundations
Transition from siloed datasets to connected intelligence
Adopt modern cloudfirst, privacyfirst infrastructures

Without this groundwork, AI cannot scale. We’re here to help close that gap.

Skills Will Shape the Future

Talent remains the heartbeat of transformation. But the talent landscape is shifting:

AIaugmented roles
Crossdisciplinary data literacy
Greater emphasis on ethical and humancentred design
Ensuring a pipeline of talent who can navigate the gap created by hybrid working and genAI

Our commitment is to keep supporting organisations in developing the teams they need - through recruitment, training, learning pathways, and community development. We’ll continue to advocate for accessible data education and inclusive career pipelines through our work with Nurture & NextGen .

Impact Matters For Business and Society

The next era of data and AI is not just about efficiency and profitability. It’s about meaningful impact.

We see enormous potential to:

Help charities and public bodies use data to strengthen services
Support sustainability goals with predictive insight
Drive responsible innovation that serves communities, not just shareholders

We believe transformation should have purpose. We’re committed to delivering work that improves outcomes at every level.

Partnership Will Define the Next Decade

Transformation is never a solo effort.

Eden Smith will deepen partnerships across:

FTSE 100 enterprises modernising at scale
Highgrowth businesses building their first data functions
Education and community initiatives developing tomorrow’s data professionals
Emerging tech ecosystems driving the future of AI innovation

Our aim is to grow our relationships and be a longterm, trusted partner to more organisations. Supporting leaders to see impact and ROI, at pace.

We’re Ready for What Comes Next

As we celebrate our 10year milestone, we’re proud of where we’ve come from but even more excited about where we’re going.

The next decade will be defined by rapid change, humancentric innovation, and the responsible integration of AI across every sector. And Eden Smith will be right at the heart of it: guiding organisations, empowering people, and shaping a future where data and AI unlock opportunity for all.

Here’s to the next chapter.
PEOPLE & DATA

mailto:info@edensmith.co.uk

Defining Moments: The Eden Smith Journey

Thu, 05 Feb 2026 20:26:16 GMT

10 Moments That Shaped Our Journey

Reaching a 10-year milestone gives you pause. It invites reflection, not just on growth and success, but on the moments that truly shaped who you are and where you’re heading.

As we celebrate a Decade of Impact , we’ve looked back at just 10 moments that helped define our journey and shape our destination. They’re moments of belief, learning, resilience, community and, above all, people.

April 2016 – Our very first deal
We celebrated in the pub with no money in the bank, but huge pride in being officially up and running. It was a recruitment deal, delivered through collaboration between my customer and Matt’s candidate. A perfect first win and an early example of how strong teamwork would underpin everything we do.

May 2016 – Becoming the exclusive talent partner to Big Data LDN
Positioning ourselves alongside what would become Europe’s largest data event was a huge moment. Ten years on, we sit on the steering committee, are part of the BDL Hall of Fame, and continue welcoming delegates into the VIP Lounge. A partnership we’re incredibly proud of.

January 2017 – Growth in action
When our Data Science recruiter walked in on a Monday and casually mentioned he’d been learning Python, we were blown away. Today, Nicholas Deveney is a shareholder, leads our consulting projects, and advises clients on data strategy, implementation and adoption. That’s what growth really looks like.

May 2017 – The birth of Nurture
Born from spotting a problem in the talent pipeline, Nurture was designed to create value for students, universities and industry alike. Watching Marie May lead Nurture with such passion and purpose remains one of our proudest achievements.

April 2019 – Learning the hard way
We won our first consulting project… and failed. Miserably. But we learned, fast. That failure led us to build an ecosystem of exceptional data and AI advisors and partners, enabling us to later support FTSE 100 organisations with confidence and credibility.

March 2020 – The Covid decision
One of the toughest moments in our history. Furloughing everyone except the shareholders, Matt , Jez and Nick was painful. But it forced adaptation. We rethought our operating model, strengthened community, embraced technology, and became fully remote.

June 2021 – Leadership evolves
When Christa Swain chose to close her own business and join Eden Smith full-time, it marked a shift. Today, she’s a key member of our Executive Leadership Team and plays an influential role in group decision-making.

February 2022 – Proof of Nurture’s impact
One of our Nurture students built a chatbot for National Highways that made national news for saving thousands of pounds. He was introduced to the CEO and offered a job. One story, but one of many that show the real-world impact of what we’re building.

November 2024 – Community, amplified
We’ve always believed in bringing people together. Our first Winter Party tested that belief, cold weather, nerves, and no idea who would turn up. The queues outside said it all. No sales. No pressure. Just connection.

February 2026 – A decade of Eden Smith
Ten years in, this milestone stands among our proudest moments. A decade shaped by resilience, reinvention, relentless learning and extraordinary people. We’ve navigated market shifts, technological leaps and the rapid evolution of AI, delivering complex programmes and helping clients turn ambition into execution.

And we’re only just getting started.

In our full article, we share how we’re celebrating #ES10 , what this milestone means to us, and how our community will be part of the next chapter.

Read the full article here:
https://www.edensmith.group/celebrating-a-decade-of-impact

Celebrating a Decade of Impact

Thu, 05 Feb 2026 13:42:02 GMT

Eden Smith Turns 10!

Ten years ago, Eden Smith set out with a clear ambition: to empower organisations through data, accelerate capability, and build a stronger, more connected digital community. Today, as we celebrate our 10‑year anniversary , that ambition has not only been realised - but it has also evolved, expanded, and helped shape the careers, strategies, and success stories of thousands across the UK.

Our #ES10 campaign marks more than a birthday. It marks a decade of partnerships, transformation, and impact - and the beginning of an even more ambitious chapter ahead.

A Decade of Impact - And We're Just Getting Started

Over the past ten years, Eden Smith has grown from a specialist staffing business into a multi‑dimensional data & AI partner supporting the full lifecycle of data‑driven transformation . Across industries - financial services, public services, built environment, energy, consumer markets and more - we’ve been trusted allies to leaders navigating complexity, innovation, and change.

We’ve seen first‑hand how data, AI, and digital capability shift from niche disciplines to critical engines of growth . And we’ve been proud to stand alongside organisations as they’ve built, scaled, and matured the teams, cultures, and operating models that power real transformation.

“10 years ago, we we’re sitting in a room no bigger than a shoebox. 2 friends, 2 phones and 1 idea. From those early days - working out of small spaces, wearing every hat, chasing every opportunity - we’ve grown into a fully‑fledged Data and AI transformation consultancy. A team of over seventy specialists, each bringing expertise, energy, and a commitment to helping organisations build real, sustainable capability.” Matt Smith, Co-Founder & CRO

Introducing #ES10: A 10 Month Celebration of Leadership, Community & Progress

Across the next ten months, we’ll shine a light on the people, partners, ideas, and innovations that have defined the Eden Smith journey - and those shaping its future.

We’ll be rolling out a series of flagship initiatives designed to connect leaders, elevate talent, and spotlight the ideas shaping the next decade of data‑driven transformation.

These include:

The Legendary Data Lounge – exclusive data leadership networking & insight exchange
Leaders Advisory Board (LAB) – cross‑functional leadership insights & collaboration
Industry in Focus – sector‑driven discussions across our core verticals including #FinancialServices & #insurance, #brands, #retail & #ecommerce, #PublicSector & #charities and #CleanTech, #utilities & #energy
Content Series – ten months of stories, lessons & thought leadership
Year of the Volunteer # GivingBack – we’re stepping up our efforts and shining a light on purpose and community contribution
Data for Good Sprint – supporting mission‑driven organisations with pro‑bono capability
Community Events – we want to get active whether that’s golf days, cycle rides, or something entirely different?!
FLAGSHIP CONFERENCE & WINTER PARTY - The next few months are all leading to one big end of year celebration of innovation and our biggest event ever – a cross-functional transformation conference and our much-loved winter party!

“When Matt and I were working opposite each other in a small office on Cheapside, with just two laptops and a printer to our name, I didn’t envisage what our business would look like today.

From a data recruitment company to a data & AI consultancy. I want to extend huge gratitude to those who have helped us on the journey, the long hours, market challenges, fantastic ideas, loyalty and grit that every team member has shown. Our team and our community have made Eden Smith what it is today. You are the best and we have many more exciting years to come, thank you so much.” Jez Clark, Co-Founder & CEO

The Next Ten Years

As we enter our second decade, Eden Smith is focused on the future:

Deeper cross‑industry collaboration
Bold innovation across data and AI
Stronger partnerships with leaders shaping organisational transformation
Continued investment in talent, capability, and community impact

And above all, we remain anchored in our core belief: great people, empowered by data, change everything . This anniversary is not just a celebration of where we’ve been, but a launchpad for where we’re going.

“The last decade has been shaped by resilience, reinvention, and relentless learning. We’ve navigated market shifts, technological leaps, pandemics and the rapid evolution of AI. We’ve built partnerships, delivered complex programmes, and helped clients move from ambition to execution. What a journey!

What Jez and I are most proud of is that we’ve built a strong team and a culture that is really special. Thank you to everyone (and there are many) who have been part of the journey so far and continue to believe in and support the Eden Smith way.” Matt Smith, Co-Founder & CRO

Join Us for #ES10

Whether you’re a client, partner, student, collaborator, or member of our wider professional community, #ES10 is a celebration we share with you.

Thank you for being part of our story.

Here’s to the next decade of impact.

Keep an eye on our socials and your inbox!

Want to learn more? Somethings change but there is no substitute for getting on the blower! Give us a ring or email us to set up a strategy call to build your advantage .

Levers for Change - Exec Summary

Mon, 02 Feb 2026 11:40:42 GMT

Executive Summary

Data Transformation Insights | Data Leaders Executive Lounge | 29th January

This Data Leaders Executive Lounge brought together senior data leaders to explore the real drivers of successful data transformation , moving beyond technology adoption to focus on operating models, leadership, culture, and capability .

The session featured insights from Zoe Kelly , Head of Data & Insights at Serco , and Pedro Cosa , CDAIO at Data Minds , drawing on Pedro’s experience across Channel Four , Warner Bros , and News UK .

A consistent message emerged: data transformation fails not because of tools, but because of unclear ownership, misaligned operating models, and unrealistic expectations of data leadership roles .

Both speakers reinforced that sustainable transformation requires deliberate design of decision rights, strong executive sponsorship, and ongoing engagement with the wider business.

The discussion also explored the evolving and often misunderstood role of the Chief Data Officer , highlighting tensions between centralised and embedded data models, persistent hiring challenges, and the risk of reverting data functions back into IT when business alignment is weak.

Key Learnings & Insights

1. Operating Model Beats Tooling

Investing in modern platforms enables scale, but does not create transformation on its own . Without clear ownership, decision rights, and ways of working, organisations simply recreate old problems on new technology.

“Changing the tooling doesn’t create the transformation - it just enables it.”

2. Clarity of Ownership Is Critical

Ambiguity around who owns what, who decides, and how conflicts are resolved is one of the biggest blockers to progress - especially in hybrid or hub-and-spoke models. These decisions must be designed early, not left to evolve organically.

3. Change Fatigue Is Real - and Predictable

Continuous transformation without pause leads to disengagement. Leaders must plan deliberate pause points, communicate them clearly, and allow teams time to embed new ways of working before introducing further change.

4. Capability Must Precede Use Cases

Building data capability purely in response to immediate use cases creates fragility. Sustainable value comes from investing in foundational capability first , with AI acting as an accelerator rather than a shortcut.

5. The CDO Role Remains Poorly Understood

The session highlighted widespread confusion around what CDOs are accountable for versus what they are empowered to change. Many data leaders are given responsibility without authority , limiting their ability to drive enterprise-level change.

6. Hiring the “Wrong” Data Leaders Is a Systemic Issue

Organisations often seek unicorn roles - leaders who are simultaneously deep technologists, hands-on engineers, strategists, and business partners. This misalignment contributes to failure and, in some cases, the decision to fold data back into CIO functions.

7. Data Is a Team Sport

Successful transformation depends on strong partnerships across CIO, CTO, CFO, HR, and business leadership . Data leaders must be effective storytellers, able to translate data strategy into business value narratives that resonate beyond the data function.

8. Culture Change Is the Hardest - and Most Important - Part

Both speakers reinforced that data transformation is ultimately a human and cultural challenge , not a technical one. Continuous stakeholder engagement, honest conversations, and visible leadership commitment are essential to maintaining momentum.

Practical Next Steps

Finalise and operationalise a hybrid hub-and-spoke operating model with clear ownership and access rights
Define and publish decision forums and escalation paths early
Introduce intentional pauses in transformation roadmaps
Strengthen cross-functional collaboration and leadership dialogue

How We Show Up: Perception, Assumptions, and the Bias

Mon, 02 Feb 2026 11:06:23 GMT

"How does the world see you? Gen AI see's me as a sparkly eyed, smiley male. Am I offended? No. AI amplifies human bias. AI isn't the bad guy here. We are."

Christa Swain

Recently, I asked an AI to generate a portrait of what it thought I looked like, based purely on how I communicate.

The result?

A confident, casual, professional-looking man .

Here’s the interesting part: outside of gender, it was pretty accurate.

The energy, posture, and “presence” felt right.

But I’m not a man.

And that gap - between accuracy and assumption - is where things get fascinating.

Perception Is Fast. Assumptions Are Faster.

We like to think we experience the world objectively. We don’t.

Whether it’s humans or algorithms, perception works by pattern recognition. We fill in gaps using:

past experience
statistical likelihood
social conditioning

In this case, the AI combined:

my communication style
the leadership contexts I operate in
the sectors I work with

…and made a gendered assumption.

Not maliciously.
Not consciously.
Just efficiently.

That’s exactly how bias usually works.

Communication Style ≠ Gender (But We Still Treat It That Way)

What surprised me most wasn’t that the AI assumed a gender - it was why (yes, I did ask).

The reasoning wasn’t “aggressive” or “dominant”. It was subtler:

decisiveness without hedging
outcome-first thinking
comfort taking up intellectual space
warmth without over-explaining or apologising

Those traits are often described as masculine.

But here’s the truth we don’t talk about enough:

These aren’t masculine traits.
They’re secure communication traits .

Historically, men were rewarded for developing them. Women were trained to soften them. So over time, we stopped recognising the difference.

AI Bias Is Just Human Bias at Scale

It’s tempting to frame AI as “biased” in isolation. But AI doesn’t invent bias - it amplifies it.

It learns from:

existing leadership norms
historical representation
dominant communication patterns

If leadership still looks a certain way, AI will reflect that - unless we deliberately intervene.

Which makes AI a mirror, not a villain.

And mirrors can be uncomfortable.

Rethinking “How We Show Up”

So what do we do with this?

First, we get curious instead of defensive.

Questions worth asking:

What assumptions do I make when someone is decisive?
Do I interpret warmth and authority differently depending on who’s speaking?
Who am I unconsciously rewarding for “confidence”?

Second, we separate behaviour from identity .

Direct ≠ masculine
Empathetic ≠ feminine
Authoritative ≠ male
Collaborative ≠ female

These are human skills.

Finally, we create spaces where people don’t have to choose between being effective and being accepted.

That’s real inclusion.

What Can Organisations Do Differently?

Awareness is a start. Design is where change actually happens.

If perception and bias are shaping who gets heard, trusted, and promoted, then organisations can’t rely on “good intentions” alone. They need structural counterweights.

Here are four places to start.

1. Redefine What “Leadership Presence” Actually Means

Many organisations say they value diverse leadership styles — but still assess people against a narrow, unspoken template.

Ask explicitly:

What behaviours are we rewarding in performance reviews?
Do those behaviours favour how something is said over what is achieved?
Are we mistaking confidence for competence, or polish for potential?

Make leadership criteria observable and outcome-based:

clarity of decision-making
ability to move others forward
quality of judgement under uncertainty

Not tone. Not style. Not similarity to existing leaders.

2. Train for Bias Where It Actually Shows Up

Bias training often focuses on intent. That’s rarely the problem.

Bias shows up in:

hiring debriefs
promotion discussions
succession planning
“readiness” conversations

Organisations should:

audit language used in talent discussions (“abrasive”, “not quite ready”, “strong personality”)
compare feedback patterns across gender, background, and communication style
pause and ask: Would we say this if someone else behaved the same way?

This isn’t about blame — it’s about calibration.

3. Design AI and Data Systems With Bias in Mind

For data and AI-led organisations, this is critical.

AI systems learn from:

historical data
past decisions
existing power structures

If leadership has historically looked a certain way, AI will reinforce that unless challenged.

Practical steps include:

diverse teams reviewing training data and outputs
bias testing in talent, performance, and recommendation systems
governance that treats AI as a decision partner, not an authority

AI should surface patterns — not lock them in.

4. Create Psychological Safety Without Demanding Self-Editing

Many people don’t lack confidence — they lack permission.

High-performing individuals often spend energy:

softening their language
second-guessing their tone
managing how they’re perceived

Organisations can counter this by:

rewarding challenge and dissent when it improves outcomes
modelling multiple leadership styles at senior levels
explicitly naming that effectiveness doesn’t have a single “look”

When people don’t have to perform acceptability, they perform impact.

A Final Thought

The AI got a lot right about me.

What it got wrong tells a much bigger story.

How we show up is only half the equation.
How we’re perceived — filtered through assumptions and bias — does the rest.

If we want better leaders, better systems, and better decisions, we need to start there.

Not with blame.
With awareness.

Microsoft Fabric in 2026

Tue, 27 Jan 2026 12:41:13 GMT

Unified Analytics, Built for Speed, Trust, and Scale

Performance Without Architectural Compromise

Performance has historically forced trade-offs: flexibility versus speed, freshness versus simplicity. Fabric addresses these challenges by integrating compute engines with shared storage. Direct Lake , now a core capability, enables Power BI to query Lakehouse data directly while maintaining interactive performance and near-real-time freshness.

Analysts no longer wait for scheduled imports, and BI teams avoid managing parallel datasets for speed. Performance is delivered as part of the platform, not as a separate optimisation exercise.

Engineering Productivity as a First-Class Goal

Fabric’s fully managed SaaS approach abstracts away infrastructure overhead, freeing teams to focus on data quality, logic, and outcomes . For organisations modernising from legacy SQL Server or complex cloud architectures, this simplification accelerates onboarding, reduces moving parts, and increases delivery velocity..

Cost Efficiency Through Consolidation

In 2026, cost efficiency is measured not just by compute pricing, but by architectural waste. Many analytics stacks still duplicate storage, transformations, and semantic models across tools. Fabric’s shared storage and unified semantic approach reduce this redundancy. By consolidating workloads into a single platform, organisations lower total cost of ownership while gaining clearer visibility into usage and consumption.

Governance and Trust at Scale

As analytics estates grow, governance becomes a limiting factor. Fabric embeds governance directly into the platform, rather than layering it on later. Centralised access control, shared semantic models, and integrated monitoring help maintain a single version of the truth across engineering and BI workloads. This native approach to governance has become a key differentiator, particularly for large and regulated organisations.

Materialised Views: Performance by Design

Materialised views allow teams to define precomputed results using SQL, reducing repeated computation for frequently queried logic. These views improve consistency, bridging the gap between flexibility and performance across Spark, SQL, and BI workloads.

In Lakehouse environments, this capability bridges the gap between flexibility and performance. Heavy joins and aggregations that once strained interactive analytics can now be served from optimised, precomputed representations. Importantly, materialised views also improve consistency.

Business logic defined once can be reused across Spark, SQL, and BI workloads, reducing duplication and the risk of divergent calculations.

Materialised views fit naturally into Fabric’s broader philosophy: performance should be a platform responsibility, not an engineering burden. Teams gain faster queries and more predictable behavior without adding operational complexity.

In this article, our newly appointed Technical Consultant | Lead Data Engineer, Urvik , explores how Microsoft Fabric has evolved into a production-ready analytics operating model.

By January 2026, Microsoft Fabric has transitioned from an ambitious vision to a proven, enterprise-grade analytics platform. In a market long defined by fragmented tools and architectural trade-offs, Fabric’s defining contribution is cohesion: one data foundation, multiple workloads, and a single path from raw data to trusted insights.

A Platform Built Around Unification

Fabric is designed to unify analytics workflows rather than assembling disconnected services. At the heart of the platform is OneLake , a single organisation-wide data lake that eliminates duplication and reduces friction between teams. Data engineering, SQL analytics, real-time processing, and business intelligence operate over the same data, using tools best suited to each role, without repetitive reshaping or copying.

Impact on Team Productivity

Traditional stacks require coordination across pipelines, transformation layers, warehouses, and BI extracts. Each boundary introduces latency and overhead. Fabric collapses these boundaries, allowing Spark notebooks, SQL endpoints, and Power BI to work directly over the same Lakehouse data. The result: a shorter path from ingestion to insight and fewer opportunities for inconsistency.

A Platform Setting the Market Baseline

By 2026, Microsoft Fabric competes as a cohesive platform , not just a collection of features. Its strength lies in unified storage, integrated compute, native BI, built-in governance, and intelligent performance optimisation .

The shared environment reduces friction, shortens feedback loops, and supports broader self-service analytics. Engineering, analyst, and business teams can collaborate effectively without duplicated logic, enabling faster, more trustworthy data-driven decision-making.

Fabric’s Lasting Impact

Microsoft Fabric has redefined expectations for modern analytics. By providing speed, trust, and scale , Fabric enables organisations to unify technical workflows and empower broader teams with reliable, self-service insights—setting a new standard for 2026 and beyond.

A unified view of Microsoft Fabric, showing how OneLake, Lakehouse, and Power BI work together to turn diverse data sources into governed, enterprise-ready insights.

Curious how Microsoft Fabric could transform your analytics capabilities? Get in touch with Eden Smith to explore unified, high-performance, and governed analytics solutions designed to scale with your organisation.

Delivering a Unified Oracle Fusion Cloud HR Platform

Tue, 27 Jan 2026 12:31:31 GMT

Unifying HR After a Large-Scale Telecoms Merger

In the wake of a significant corporate merger between two telecommunications giants (referred to, as Entity V and Entity O ), a strategic transformation programme was launched to unify HR operations across the newly formed enterprise. The core objective was to implement Oracle Fusion Cloud ERP as a single, scalable HR platform capable of harmonising processes, improving data quality, and enabling AI-driven efficiencies.

This article outlines the end-to-end lifecycle of the HR workstream , from stakeholder engagement and BPMN 2.0 process mapping through to functional design, testing, and integration with the Enterprise Data Warehouse (EDW). It highlights the governance, discipline, and collaboration required to deliver a successful post-merger HR transformation.

Oracle Fusion Cloud ERP implementation services

Phase 1: Stakeholder Engagement and Governance Alignment

The programme began with a detailed stakeholder analysis , critical in a multi-stream transformation involving Finance, Sales, Marketing, and HR. For the HR workstream, it was essential to distinguish between decision-makers and influencers to avoid ambiguity during design and approval stages.

Key stakeholders included:

Director of HR – final sign-off authority for all process and policy decisions
Head of People Analytics (Spain) – responsible for reporting outputs and data quality
Heads of Payroll and Recruitment – subject matter experts for specialist HR modules
HR Business Partners (HRBPs) – primary operational users of the future system

A formal RACI matrix (Responsible, Accountable, Consulted, Informed) was established to define ownership across deliverables. This ensured that all artefacts, from process maps to functional designs, followed a clear approval pathway, significantly reducing delays and rework during later phases.

Phase 2: Current-State (As-Is) Process Mapping Using BPMN 2.0

To design an effective future-state HR operating model, it was essential to fully understand the existing landscape across both legacy organisations. This phase focused on capturing the As-Is processes across Entity V and Entity O, which differed significantly due to historic systems, policies, and operational practices.

Using BPMN 2.0 standards and Microsoft Visio, current workflows were documented in detail. The approach included:

Process discovery workshops to surface manual workarounds and pain points
Stakeholder walkthroughs to validate accuracy and completeness
Formal sign-off to confirm the As-Is state as the transformation baseline

This rigorous validation process ensured alignment across HR, payroll, recruitment, and analytics teams, creating a shared understanding of complexity before any redesign work began.

Phase 3: Gap Analysis and Future-State (To-Be) Design

With the As-Is baseline agreed, the programme moved into gap analysis and future-state design . The objective was to align Entity V’s legacy processes with Entity O’s more mature HR framework, while fully leveraging the native capabilities of Oracle Fusion Cloud .

Key focus areas included:

Reducing manual effort through AI-enabled automation
Standardising processes to improve scalability and governance
Minimising customisation to protect long-term maintainability

Where business-critical requirements justified configuration, these were clearly documented and approved. The resulting To-Be process maps were reviewed in detail with HR leadership, culminating in formal sign-off by the Director of HR.

Phase 4: Business Requirements and Functional Design

To translate business vision into technical execution, two core documents were produced:

Business Requirements Document (BRD)

The BRD converted future-state process maps into detailed functional requirements, explicitly identifying:

Standard Oracle Fusion functionality
Required configurations
Exception handling and reporting needs

Functional Design Document (FDD)

The FDD served as the technical blueprint for development teams and included:

Low-fidelity wireframes to visualise user interactions
Use case statements describing system behaviour
Logical rules defining system processing
A comprehensive security matrix to protect sensitive HR and payroll data

Given the presence of personally identifiable information (PII), access controls were carefully designed to ensure role-based visibility across recruitment, HR, and payroll functions.

HR transformation and people systems services

Phase 5: Development, Testing, and User Acceptance

Once development commenced, focus shifted to quality assurance and testing governance . This phase ensured that system build aligned precisely with the approved functional design.

The testing lifecycle included:

QA testing to validate functional accuracy
Defect management using Jira, with structured re-test cycles
User Acceptance Testing (UAT) involving HR end-users

UAT workshops were supported by detailed user stories and test scenarios, ensuring the system met real-world operational needs before deployment approval.

Phase 6: Deployment and Enterprise Data Integration

The final phase focused on production deployment and integration with the Enterprise Data Warehouse (EDW) . This ensured that HR data flowed seamlessly into downstream analytics and reporting platforms, supporting:

Consistent people metrics
Trusted workforce reporting
Executive-level insights

By aligning Oracle Fusion Cloud with the wider data ecosystem, the programme delivered a single source of truth for HR information across the organisation.

Lessons from Post-Merger HR Transformation

This Oracle Fusion Cloud HR implementation demonstrated that successful post-merger transformation depends on more than technology alone. Clear governance, disciplined process design, stakeholder alignment, and rigorous testing were essential to delivering a scalable, trusted HR platform.

By combining structured methodology with modern cloud ERP capabilities, the organisation established a future-ready HR operating model, one capable of supporting growth, automation, and data-driven decision-making long after the merger was complete.

Welcoming our New Lead Data Engineer!

Wed, 21 Jan 2026 16:45:36 GMT

We Welcome Urvik Patel to Our Eden Smith Team

We are delighted to announce that Urvick Patel has joined Eden Smith as a Technical Consultant | Lead Data Engineer.

Urvick joined the team at our monthly Collab, in London, earlier this month and is working on a couple of internal projects before being deployed onto a client project.

Learn more about Urvick below. Please do connect with him on LinkedIn and help us create the warmest of welcomes.

➢ How did you first learn about Eden Smith?

My journey toward Eden Smith began with a simple but important question: where does technology truly make a difference? As my career developed, I became increasingly drawn to work at the intersection of data and AI, technology, and people. When I first came across Eden Smith and explored the organisation in more depth, it immediately felt different. The emphasis on human-led technology, long-term capability building, and meaningful outcomes really stood out and aligned closely with how I view my own work.

➢ What are you most looking forward to in your new role?

I’m most excited about working on data and AI initiatives that have real, lasting impact . Being part of a collaborative, forward-thinking environment where contribution and learning go hand in hand is hugely motivating for me. I’m looking forward to working alongside experienced, passionate people, learning from them while also bringing my own perspective, curiosity, and experience to the team.

➢ What are your three most overused words/phrases that we can expect to hear?

“Let’s understand the problem first”
“Long-term value”
“It depends…” (usually followed by lots of questions!)

➢ What are three career lessons you’ve learned thus far?

First, deep understanding matters far more than quick solutions, rushing without context almost always leads to fragile systems.
Second, scalable systems are built through discipline and simplicity , not unnecessary complexity.
Third, communication and trust are just as important as technical ability. The best solutions come from strong collaboration, not working in isolation.

➢ What has been the most important innovation you have witnessed in your lifetime?

For me, it’s been the evolution of data platforms into unified, cloud-native ecosystems . While AI often takes the spotlight, the convergence of data engineering, analytics, governance, and AI into integrated platforms has dramatically reduced complexity while increasing capability, allowing teams to move faster without sacrificing trust or control.

➢ What are your hopes for our industry?

I hope the industry continues to prioritise people-first, responsible use of data and AI . That means building capability, trust, and understanding, not just delivering technology. Long-term value, ethical governance, and human-centred outcomes should remain at the heart of everything we do.

➢ Before joining Eden Smith, what was the most unusual or interesting job you’ve ever had?

One of the most interesting aspects of my previous roles was reverse-engineering legacy systems where critical business logic had been embedded for years, sometimes decades, with little or no documentation. Understanding not just the data, but the people and processes behind it, was both challenging and fascinating.

➢ How do you balance your career life and personal/family life?

Balance comes from being intentional. I focus on clarity, prioritisation, and setting realistic boundaries, while staying flexible when needed. I’ve learned that being present, whether at work or with family, is far more important than trying to do everything at once.

➢ What are your top 3 life highlights?

Building a career around meaningful, impactful work
Being part of teams that value trust and collaboration
Personal growth that’s come from continuously learning and adapting

➢ Do you have a favourite quote or personal mantra?

Yes, “Aham Brahmāsmi”, a Sanskrit phrase meaning “I am part of the greater whole.” To me, it represents self-belief, responsibility, and continuous growth. It’s a reminder that our mindset and actions shape the systems, teams, and environments we’re part of.

➢ What is the most important thing you have learned in the last five years?

That technical skills alone are never enough. The most meaningful progress happens when technology, people, and processes are aligned, and when curiosity, humility, and listening are valued as much as expertise.

➢ Tell us something about yourself that would surprise us.

Despite working in highly technical roles, one of my greatest strengths is my enjoyment of translating complex ideas into clear, accessible conversations, especially with non-technical stakeholders.

➢ What is your hidden talent?

Patience. Whether it’s untangling legacy systems, supporting others, or navigating complex problems, staying calm and methodical has served me well.

➢ What have you always wanted to try but haven’t… yet?

I’d love to spend time working or learning abroad, experiencing different cultures and perspectives while continuing to grow professionally.

How Adaptability in Delivery Reduces Risk and Builds Confidence

Wed, 14 Jan 2026 14:56:54 GMT

Why flexible, human-centred approaches lead to stronger outcomes in data and transformation projects

The Risk of Rigid Delivery Models

Many transformation programmes fail not because the technology is wrong, but because the delivery approach is too rigid.

Traditional delivery models often assume certainty: fixed requirements, clearly defined outcomes, stable teams, and predictable timelines. In reality, data and AI projects rarely operate in this way. Business priorities shift, data quality issues emerge, stakeholders’ understanding evolves, and external pressures, from regulation to market change, can alter direction overnight.

When delivery models don’t allow for adaptation, risk increases. Teams feel pressure to “stick to the plan” even when evidence suggests the plan needs adjusting. Issues get hidden rather than addressed. Confidence erodes as people worry about being blamed for deviations instead of supported in solving problems.

This rigidity also impacts people. Stakeholders who feel unheard disengage. Delivery teams lose autonomy. Leaders become cautious, delaying decisions because change feels expensive and risky.

Ironically, the attempt to reduce risk through tight control often creates the very conditions that cause projects to stall or fail.

Adaptability flips this dynamic. Instead of seeing change as failure, it treats it as insight. Instead of protecting plans, it protects outcomes.

Adaptability as a Risk-Reduction Strategy

Adaptable delivery is not about being unstructured or vague. It is about building in feedback loops, decision points, and the freedom to respond to what the work reveals.

In practice, this means:

Testing assumptions early and often
Delivering in small, meaningful increments
Creating space to pause, reflect, and course-correct
Involving stakeholders throughout, not just at milestones

This approach reduces risk by surfacing problems sooner, when they are easier and cheaper to fix. Data gaps, unclear ownership, unrealistic expectations, or capability constraints become visible early rather than late.

Adaptability also spreads ownership. When teams and stakeholders are involved in shaping the direction, they are more invested in the outcome. Decisions feel shared rather than imposed, which increases commitment and momentum.

Crucially, adaptable delivery acknowledges that uncertainty is normal in complex work. Rather than pretending everything is known upfront, it designs for learning.

Over time, this creates a culture where raising concerns is encouraged, not penalised. Risk becomes something to manage collectively, not something individuals feel they must carry alone.

How Adaptability Builds Confidence at Every Level

Confidence is often overlooked in transformation, yet it is one of the strongest predictors of success.

When delivery is adaptable, people gain confidence because:

They see progress quickly and tangibly
Their input influences direction
They are trusted to make decisions within clear boundaries
Mistakes are treated as learning, not failure

For delivery teams, this builds professional confidence. They move from executing tasks to solving problems, strengthening judgement and ownership.

For stakeholders, adaptability builds trust. They don’t feel locked into decisions made too early or too far removed from reality. Instead, they gain confidence that the programme can respond to their evolving needs.

For leaders, adaptable delivery reduces the fear of backing the “wrong” decision. When course correction is expected, leadership becomes about enabling learning rather than defending certainty.

This confidence compounds. As teams experience successful adaptation, they become more willing to engage, experiment, and innovate. Adoption improves because people understand not just what has been delivered, but why it works.

Ultimately, adaptability doesn’t just protect projects, it strengthens the organisation’s ability to change again in the future.

From Delivery Method to Cultural Advantage

Organisations that embrace adaptable delivery don’t just deliver better projects; they build stronger cultures.

They develop people who are comfortable with ambiguity, confident in decision-making, and skilled at collaboration. They reduce dependency on heroics and create repeatable ways of working that scale.

In a world where data, AI, and business needs evolve constantly, adaptability is no longer a “nice to have”. It is a core capability, one that reduces risk, builds confidence, and leaves teams better equipped for whatever comes next.

Get in touch with the Eden Smith Team. We’d love to explore how adaptable, human-centred delivery could support your teams and outcomes.

Responsible by Design - Keeping AI Human

Wed, 03 Dec 2025 16:45:26 GMT

Executive Summary:

AI, Ethics, and Human-Centred Design

Our recent Leaders Advisory Board event - designed in partnership with Corndel - featured three engaging sessions that explored how AI impacts human cognition, customer experience, and fairness.

Here's what we learnt:

1. Think or Sink – Are We Using AI to Enhance or Reduce Cognitive Ability?

Speaker:

Rosanne Werner , CEO at XcelerateIQ & ex Transformation Lead at Coca-Cola

Roseanne opened the day with an interactive and thought-provoking session, firmly positioning AI: “AI should be your sparring partner, not your substitute for thinking.”

Her research revealed a striking insight: 83% of people using LLMs couldn’t recall what they wrote, compared to just 11% using traditional search . The message? It’s not about avoiding AI, but using it in ways that strengthen thinking , not outsource it.

Roseanne explained how our brains form engrams - memory footprints that enable creativity and critical thinking. Over-reliance on AI risks weakening these pathways, reducing retention and problem-solving ability.

She introduced the Mind Over Machine Toolkit , six strategies to use AI as a thinking partner:

Provide Context First – Frame the problem before asking AI.
Use AI as a Challenger – Stress-test ideas and uncover blind spots.
Iterative Co-Creation – Collaborate, refine, and evaluate.
Document Your Thinking – Keep reasoning visible.
Reflective Prompts – Support reflection, not replace judgment.
Sparring Partner – Test assumptions and explore risks.

Roseanne summed it up with a simple rule: use Sink for low-value, repetitive tasks, and Think for strategic, creative decisions.

2. Designing Chatbots with Human-Centred AI

Speaker:

Sarah Schlobohm , Fractional Chief AI Officer

Sarah brought a practical perspective, drawing on experience implementing AI across sectors - from banking and cybersecurity to rail innovation.

She began with a relatable question: “Who’s been frustrated by a chatbot recently?” Almost every hand went up.

Through a real-world example (redacted out of politeness), Sarah illustrated how chatbots can fail when designed with the wrong priorities. The chatbot optimised for deflection and containment , but lacked escape routes , sentiment detection, and escalation paths - turning a simple purchase into a multi-day ordeal.

“Don’t measure success by how well the chatbot performs for the bot—measure it by how well it performs for the human.”

Sarah introduced principles for better chatbot design:

Human-Centred Design – Focus on user needs and emotional impact.
Systems Thinking – Consider the entire process, not just chatbot metrics.
Escalation Triggers – Negative sentiment, repeated failures, high-value intents.
Context Awareness – Detect when a task moves from routine to complex and route accordingly.

The takeaway? Automation should remove friction from the whole system - not push it onto the customer.

3. Responsible AI and Bias in Large Language Models

Speaker:

Sarah Wyer , Professional Development Expert in AI Ethics at Corndel

“When we create AI, we embed our values within it.”

She shared her journey tackling gender bias in large language models , from GPT-2 through to GPT-5, and highlighted why responsible AI matters. AI systems reflect human choices - what data we use, how we define success, and who decides what is fair.

Real-world examples brought this to life: facial recognition systems failing to recognise darker skin tones, credit decisions disadvantaging women, and risk assessment tools perpetuating racial bias.

Even today, LinkedIn engagement patterns show gender bias!

Sarah made the point that simple actions - like testing prompts such as “Women can…” or “Men can…” - can reveal hidden disparities and spark vital conversations.

To address these issues, Sarah introduced the D.R.I.F.T framework , a practical guide for organisations:

D – Diversity : Build diverse teams to challenge bias.
R – Representative Data : Ensure datasets reflect all user groups.
I – Independent/Internal Audit : Test outputs regularly.
F – Freedom : Create a culture where employees can challenge AI decisions.
T – Transparency : Share processes without exposing proprietary code.

Wrapping up the final session - before we opened the floor to panel questions and debate - Sarah created the opportunity to discuss how we address AI bias within our organisations by stepping through the DRIFT framework.

Shared Themes Across All Sessions

AI is powerful, but context matters .
Human oversight and ethical design are critical .
Use AI to augment thinking , not replace it.
Measure success by human outcomes , not just automation metrics.

We've had such great feedback from this event series - especially around the quality of speakers and the opportunity to have meaningful conversation and debate outside of functions. Definitely more in the events plan for 2026!

If you'd like to be part of the conversation please navigate to our LAB events page to register your interest .

The Psychology of AI Adoption

Wed, 03 Dec 2025 13:43:06 GMT

Why Teams Resist and How Leaders Can Drive Real Buy-In

AI transformation isn’t failing because of technology.

It’s failing because of foundational maturity & people .

Across industries, organisations are investing heavily in AI tools, automation, and data-driven decision-making, yet many of these projects struggle to scale or gain traction.

If we put governance to one side for a moment - the common thread isn’t a lack of capability, budget, or ambition... it’s resistance, uncertainty, and behavioural friction inside teams.

To move from experimentation to real impact, organisations must treat AI adoption as a human change journey , not a technical upgrade. Understanding the psychology behind resistance is the first step. Leading for trust, confidence, and clarity is the second.

Why Teams Resist AI

Resistance to AI rarely stems from a dislike of technology. It comes from deep-rooted psychological triggers, many of which are completely natural.

1. Fear of Job Loss or Skill Redundancy

AI triggers anxiety about being replaced, automated, or left behind. Even when leaders insist AI is there to "enhance, not replace," employees may still interpret new tools as a threat, especially if communication is unclear or inconsistent.

2. Loss Aversion

People are more motivated by avoiding loss than gaining benefit. AI often feels like a loss of control, autonomy, or expertise, making even minor changes feel disproportionately risky.

3. Overwhelm and Cognitive Load

AI can feel abstract, complex, and inaccessible. When employees don’t understand how it works or how it helps their role, their natural response is avoidance.

4. Identity and Professional Pride

For many people, their value comes from experience and intuition.
AI challenges that by introducing automation and data-driven insights that appear to "know better." This can create subtle defensiveness.

5. Habit and Comfort with the Familiar

Behavioural science shows that human's default to what feels familiar, even when it’s inefficient. Old processes feel safe because they’re predictable.

These barriers are not signs of poor culture or resistance to innovation. They’re psychological, not personal. And that means leaders need to adopt strategies rooted in behavioural change , not technical adoption.

Turning Resistance into Confidence and Engagement

Successful AI adoption isn’t driven by technology teams, it’s driven by leadership.

1. Start With Purpose, Not Platforms

Teams need to understand why AI matters, not just what it does.
Leaders should frame AI in terms of problems solved, stress reduced, service improved, and opportunities created.

When people see the purpose, they see the value.

2. Remove Fear Through Transparency

The more ambiguous AI feels, the more threatening it becomes.
Be explicit about:
• which tasks will change
• how roles will evolve
• what new skills will matter
• how employees will be supported

Clarity shrinks fear.

3. Involve Employees Early

AI adoption fails when people feel it’s being "done to them."
It succeeds when employees help design, test, and improve solutions.

Early involvement increases ownership and reduces anxiety.

4. Build AI Literacy, For Everyone

AI is no longer a specialist skill.
Workforces need essential AI literacy:
• what AI can and can’t do
• how to question outputs
• how to apply it ethically
• how it affects workflows

This boosts confidence and builds trust.

5. Show Quick, Human-Centred Wins

Behavioural science tells us that people change when they see fast, meaningful impact.
Small, practical use cases, not grand 5-year blueprints, create momentum.

6. Celebrate Human + AI Collaboration, Not Competition

Shift the narrative away from replacement.
Highlight cases where AI enhances expertise, reduces admin, improves wellbeing, or elevates decision-making.

Culture follows the stories leaders choose to amplify.

Creating a Culture Where AI Becomes a Natural Part of Work

Lasting AI adoption is cultural, not technical. It emerges when:

• employees trust the purpose
• leaders communicate clearly and consistently
• the organisation builds literacy, not fear
• people experience real improvements in their work
• learning becomes more important than perfection

The future belongs to organisations that understand AI is as much about human behaviour as it is about algorithms.

Teams don’t resist AI because they don’t care.

They resist because they haven’t been shown how AI cares for them .

Leaders who bridge that gap, with empathy, transparency, and inclusion, will unlock the full power of AI transformation.

�� Get in touch to explore how we can support your AI transformation journey.
Let’s build a future where people and AI work better together.

Why Hybrid Skills Are Outpacing Pure Tech Roles

Tue, 02 Dec 2025 14:21:15 GMT

From Data Overload to Data Understanding

For the past decade, organisations have poured investment into building data teams, hiring data scientists, engineers, analysts, AI specialists, and technical architects at unprecedented scale.

Yet despite the rapid growth of these technical roles, one challenge keeps resurfacing across industries: businesses are still sitting on huge volumes of data they don’t fully understand, can’t easily translate into action, and struggle to embed into decision-making.

This gap between technical output and practical business value has given rise to one of the fastest-emerging roles in the digital economy: the Data Translator .

A Data Translator isn’t a “lite” data scientist nor a glorified analyst. They are professionals who sit at the intersection of data, business, and communication. Their job is to understand what data teams are building, interpret these insights in context, and ensure the business can use them to make consistent, evidence-based decisions. In other words, they convert complexity into clarity.

Organisations increasingly recognise that even the most advanced models, dashboards, and algorithms are only as valuable as the decisions they influence. The Data Translator is the missing link ensuring that technical solutions actually solve real-world problems.

And this shift is transforming hiring priorities.

Why Hybrid Skills Are Outpacing Pure Tech Roles

For years, technical capability dominated recruitment. Businesses believed that stacking teams with more specialists would automatically accelerate transformation. But the reality proved different: many initiatives stalled because technical teams and operational teams didn’t speak the same language.

Enter hybrid skillsets.

Data Translators combine a unique blend of competencies that pure tech roles often lack:

Domain knowledge to understand business priorities and constraints
Analytical fluency to grasp what’s possible with data
Communication skills to convert data into stories and recommendations
Change management awareness to influence adoption
Strategic thinking to align technical work with commercial outcomes

These hybrid capabilities enable Data Translators to bridge a critical divide, one that technology alone can’t fix.

This helps explain why demand for hybrid roles is growing 2–3x faster than for deep technical specialisms in many sectors. Organisations don’t just need more models; they need people who can turn those models into behaviour change, product improvements, better forecasting, and measurable business results.

Simply put: Data Translators multiply the value of technical teams.

And in a world where every business wants to be data-led, that multiplier effect is becoming indispensable.

The Real Impact of Data Translators Across Organisations

The value of Data Translators becomes most visible when organisations try to operationalise their data strategy. These professionals help teams understand not only what the data says but what it means and what to do next.

Here are a few high-impact examples of where Data Translators shine:

1. Turning Insights Into Decisions

They transform highly technical outputs into simple, actionable guidance that non-technical teams can use immediately — whether that’s optimising supply chains, reducing churn, or improving customer journeys.

2. Breaking Down Silos

Because they speak both technical and commercial languages, Data Translators naturally bring teams together. Marketing, operations, finance, and data teams finally align around shared metrics and goals.

3. Increasing ROI on Data Investments

Many organisations invest heavily in data platforms, tooling, and models but achieve only partial adoption. Data Translators ensure that these investments convert into real business impact.

4. Accelerating Digital and AI Transformation

As AI adoption rises, the ability to understand, explain, and trust automated decisions becomes crucial. Data Translators help organisations navigate ethical concerns, interpret model behaviour, and embed AI responsibly.

In short, they close the gap between what’s technically impressive and what’s practically transformative.

The Era of Hybrid Roles

The rise of the Data Translator signals a broader shift: hybrid roles are becoming more valuable than deep specialisation alone.

This doesn’t mean technical roles are losing relevance, far from it. Data engineers, scientists, and analysts remain essential. But they are no longer the only essential roles. As organisations evolve, they need professionals who operate at the intersection of disciplines, not just inside them.

The future workforce will be shaped by:

T-shaped talent (broad business understanding + one deep specialism)
Adaptive communicators who can simplify complexity
Multi-disciplinary problem-solvers
People comfortable navigating data, technology, and strategy simultaneously

And Data Translators represent the blueprint for these emerging roles.

Companies that invest in hybrid talent are already seeing faster adoption of insights, stronger decision-making, higher efficiency, and more impact from their digital and AI transformation programmes.

Because in the end, data doesn’t create value, people who know how to interpret and apply it do . If your organisation is looking to build hybrid capability or strengthen the bridge between data and decision-making, let’s talk. We help teams unlock the true value of their data by developing the translators who bring it to life.

Hiring for the Unknown

Wed, 12 Nov 2025 15:26:31 GMT

How to Create Roles for Jobs That Don't Exist Yet

Preparing organisations for the future of data, AI, and digital transformation.

The Leadership Challenge of the Unknown

In a world where technology evolves faster than job descriptions can be written, many organisations are grappling with a new kind of leadership challenge:
How do you hire for roles that don’t yet exist?

Artificial intelligence, automation, and data transformation are redrawing the boundaries of what leadership looks like. Five years ago, few companies had a Chief AI Officer or a Head of Data Ethics. Today, these roles are fast becoming critical to growth, resilience, and reputation.

The problem is that traditional hiring models are built on the past. They focus on experience, not potential. Titles, not trajectories. Yet the leaders who will define the next decade are those who can adapt, learn, and lead in ambiguity .

At Eden Smith Executive Search, we’ve seen this evolution first-hand across the data and digital landscape. The organisations thriving in this new era are not just recruiting for today’s capabilities, they’re building leadership benches for tomorrow’s unknowns .

From Job Descriptions to Leadership Blueprints

When you’re hiring for the unknown, the job description is no longer the starting point, it’s the outcome.

Forward-thinking organisations are beginning their search with a different question:
“What capabilities will we need to remain competitive in three to five years?”

These capabilities might not align neatly with existing roles, but they often include:

Data fluency and digital confidence at the executive level
A deep understanding of AI governance and ethical innovation
Comfort leading multidisciplinary, hybrid teams
The ability to translate complex data into commercial outcomes

Instead of recruiting against static job specs, leaders must now think in capability ecosystems , blending technical knowledge with strategic foresight, emotional intelligence, and the courage to experiment.

Eden Smith’s executive approach helps clients map these emerging competencies, defining leadership archetypes that can flex with change. Because hiring isn’t just about filling gaps, it’s about futureproofing the business.

Spotting Potential Over Experience

One of the biggest shifts in executive search today is the move from experience-based to potential-based hiring.

When the role itself is evolving, past experience is often a poor predictor of future success. The leaders best equipped for the age of AI and data-driven transformation share distinct traits:

Learning agility - a track record of thriving through change
Systems thinking - seeing beyond silos to understand interconnections
Empathy and communication - the human side of digital leadership
Strategic curios ity - continuously exploring “what’s next”

These attributes can’t always be captured in a CV. They’re uncovered through behavioural assessment, scenario-based interviewing, and in-depth leadership diagnostics, tools that Eden Smith integrates into every search.

By focusing on mindset, adaptability, and values alignment, organisations can confidently hire leaders who will shape their next wave of innovation, not just manage it.

Co-Designing the Future of Leadership

The future of leadership isn’t written by one party alone, it’s co-designed by business, academia, and executive search partners who understand the pace of change.

As AI, data, and sustainability continue to converge, the most successful organisations will build collaborative talent ecosystems , cultivating future leaders internally while scanning the market for external innovators.

At Eden Smith, we see our role as more than identifying top-tier candidates. We act as strategic partners, helping businesses anticipate future leadership needs, design adaptive roles, and build talent pipelines that evolve with technology.

The question isn’t just “Who do you need now?” but “Who will you need next, and how do we find or develop them before the market does?”

The Takeaway: Build for Agility, Not Certainty

In an era defined by disruption, hiring for the unknown is no longer optional, it’s a competitive advantage.

The leaders of tomorrow won’t fit neatly into existing boxes. They’ll be boundary-crossers, data-literate decision-makers, and purpose-driven innovators. The organisations that learn to identify, attract, and empower them today will be the ones shaping the markets of the future.

At Eden Smith Executive Search, we help businesses stay ahead of that curve, connecting them with visionary leaders ready to thrive in what comes next.

To explore how we can help your organisation design its future leadership strategy, contact Jane Smith today.

The Risk to Reinvent

Fri, 17 Oct 2025 10:42:40 GMT

There is a moment in every transformation journey when organisations must decide:
�� Will we protect what we’ve built?
�� Or reinvent what’s possible?

On 17 October , the Data Leaders Executive Lounge gathered senior data leaders to explore this very question. Hosted under Chatham House rules, the evening’s theme - “Risk to Reinvent” - brought together sharp minds, bold ideas, and honest reflections on how data leadership is (and must be) reshaping business strategy.

Kate Sargent, Chief Data Officer at Financial Times, and Eddie Short, a renowned transformation and AI leader led the conversation. Their perspectives framed a candid discussion about shifting from process-led thinking to data-centric, predictive, and commercially intelligent business models.

From Process-Led Legacy to Predictive-by-Design Futures

For more than a century, businesses have been organised around process - a model designed for 19th-century manufacturing. But today, 91% of the UK economy is service-based. Yet many organisations still operate as though process is king.

This disconnect surfaced repeatedly in the discussion: leaders often can’t articulate what capabilities actually matter to deliver strategy. Instead, they talk in terms of technology platforms - “We need Oracle” or “We need Pega” - rather than customer value or strategic outcomes.

The call to action:
✅ Reframe the backbone of the enterprise - where data and AI are the orchestrators, and processes play a supporting role.
✅ Shift from “backward-looking by design” to “predictive by design” architectures - operating models that drive agility, growth, and resilience.

The Capability Flywheel & The Intelligent Enterprise

Eddie Short shared the evolution of a capability flywheel model developed over 20+ years - integrating people, process, technology, data, and AI to create the Intelligent Enterprise.

This approach starts by asking:

What must this business excel at to win?
How can data and AI supercharge those capabilities ?

Many executive teams can’t answer those questions clearly. And if capabilities aren’t defined, a data strategy is destined to be reactive rather than transformational.

The Trap of Technology-Led Change

One of the most striking points of consensus...
Organisations are spending heavily on technology but not transforming.

Why?

Because technology alone doesn’t solve business problems .

A culture of FOMO, vendor pressure, and shiny-object syndrome often leads to tech purchases without clear value articulation. Meanwhile, the real differentiator - execution, adoption, and value creation - gets overlooked.

Data Value Over Data Tech: A Necessary Mindset Shift

Kate Sargent outlined how the Financial Times is deliberately reframing its approach to data value measurement .

Rather than treating data as an abstract asset, the FT is embedding a “value funnel” into its operating model. This funnel tracks the potential , captured , and realised value of data initiatives, surfacing where value is lost - whether through data quality issues, resourcing gaps, or lack of adoption.

The goal?

To create a shared understanding of data value across the organisation, linking it directly to strategic and commercial outcomes.

This is data not as “back office plumbing” - but as a driver of growth.

Case in Point: Reinvention in Retail

A real-world example brought the principles to life. A Romanian retailer - Profi - facing stagnant digital performance, shifted from risk avoidance to experimentation:

Deployed Azure AI and revamped its digital app to promote bundled meal purchases.
Leveraged ChatGPT and Midjourney to rebrand a wine range - from ideation to market in weeks.
Result: 50% increase in basket size and repeat purchases , and a £100m uplift in company valuation in under a year.

This was data as a commercial engine , not an IT project.

Overcoming Cultural and Structural Barriers

The conversation turned candid on risk aversion - especially in regulated industries. Many leaders default to compliance-driven, process-heavy approaches, making bold transformation nearly impossible.

Key reflections:

Too many leaders rely on anecdotes over analytics.
Data teams are often pigeonholed into reporting functions, rather than driving strategy.
Transformation requires assertive data leadership at the top table.

“Stop being the data guy. Be the business transformation leader.”

Speaking the Language of the Board

Data initiatives fail to resonate at the board level when they are framed in tech-speak.
But, when translated into three universal levers the narrative shifts from “support function” to strategic enabler .

1️⃣ Growing revenue
2️⃣ Increasing profitability
3️⃣ Reducing risk

This was the evening’s unifying thread: If you can’t articulate the straight line from data to revenue, profit, or risk reduction, you’re wasting your time.

Embedding a Data Value Mindset

Kate Sargent’s work offers a clear roadmap:

Establish a value mindset - shared language, communication assets, and strategic alignment.
Capture value systematically - using value calculators and prioritisation frameworks.
Close the feedback loop - to learn, iterate, and scale what works.
Build literacy beyond the data team - empowering the wider organisation to speak and act in terms of data value.

This structured approach aims to make value conversations accessible and embedded into daily business operations - not confined to dashboards.

Call to Action for Data Leaders

The event closed with a clear mandate for those shaping the future of their organisations through data and AI:

✅ Reframe your role from data manager to transformation leader .
✅ Speak in the language of commercial outcomes.
✅ Challenge risk avoidance with predictive-by-design models .
✅ Experiment fast, prove value, and scale boldly.
✅ Build data value thinking into the fabric of the organisation.

As one participant noted: “Risk and performance are two sides of the same data.”

What’s Next

A heartfelt thank you to our speakers Kate Sargent and Eddie Short, our event sponsors - Cloudaeon - , and everyone who contributed their insights.

The Winter Party returns on 20 November 2025 - a festive gathering, in London, and an opportunity to continue these conversations.

�� If you’d like to be part of the next Data Leaders Executive Lounge, register your interest at Eden Smith.

HUMAN + AI - Turning Strategy Into Action

Fri, 17 Oct 2025 10:13:34 GMT

In today’s boardrooms, the conversation is no longer if AI will reshape work - but how fast.

On 15th October , cross-functional business leaders gathered for the first event in the HUMAN + AI Series , a collaboration between Eden Smith and Corndel, designed to demystify AI strategy and help organisations move from intention to meaningful action.

This first session was a candid, insight-rich discussion about what it takes to build trust, drive adoption, and enable every part of an organisation to thrive with AI - not just the tech teams.

Why AI Success Starts with People

Erik Schwartz, Chief AI Officer at AI Expert, opened with a clear message:

“AI is only as strong as the leadership behind it.”

In a live poll of 27 leaders, most revealed they are still in the early stages of AI adoption. Many have experimented with tools like Copilot, but few have moved into structured implementation. Erik shared powerful case studies where targeted AI initiatives streamlined workflows and delivered measurable business impact.

His call to action was simple but potent:

Build leadership AI literacy early.
Start small but show results fast.
Use hackathons and prototype projects to turn theory into momentum.

“Put something tangible in front of your executives,” he urged. “AI adoption accelerates when people can see and feel the value.”

Embedding Data and AI into Organisational DNA

Helen Blaikie shared how Aston University overcame silos, data hoarding, and cultural resistance to create a mature data and AI strategy for 2030.

Key pillars of their success:

Leadership sponsorship and clear performance measures
A robust data governance framework
Organisation-wide upskilling (over 600 trained colleagues)
A relentless focus on trust and quality

By aligning data and AI initiatives directly with business objectives, the university didn’t just modernise - it transformed how decisions are made.

The Human Experience of AI

Helen Matthews tackled one of the most pressing realities: people’s fears and expectations around AI.

�� 65% of employees fear job loss.
�� 45% resist change.
�� 91% want responsible AI policies.

Matthews highlighted how starting with “why” is essential. AI strategy isn’t just about algorithms - it’s about trust, transparency, and storytelling . By mapping workforce capabilities, tailoring training, and leveraging early adopters, organisations can turn anxiety into agency.

She also outlined a practical maturity model: start with foundational awareness, tailor training to function, then continuously refine. A particularly resonant insight: use the apprenticeship levy to fund AI learning programs - removing one of the biggest adoption barriers.

The Leadership Panel: Turning Insight into Impact

A dynamic panel session explored how leaders can practically navigate the intersection of people, talent, and technology.

Key insights:

Use AI tools to empower employees to self-assess skills and career paths.
Start with one well-defined pain point to build trust and credibility.
Involve frontline employees early to ensure solutions solve real business problems.
Encourage co-creation spaces and flexible policies to adapt fast.

The message was consistent: AI adoption is not a spectator sport. It’s a collective, cross-functional effort that demands experimentation, communication, and strong leadership.

Top Action Points for Leaders

Build AI literacy at the top and cascade it down.
Align AI strategy with business objectives - not the other way around.
Start small, show value fast , then scale.
Invest in data governance, trust, and culture.
Equip people to experiment with AI tools and co-create solutions.
Communicate, measure, celebrate - repeatedly.

This was just Part 1 of the HUMAN + AI Series . The conversations were raw, practical, and inspiring - setting the stage for the next event, where we’ll dive deeper into human capability building and AI readiness at scale.

Why ESG Reporting is the Next Big Data Challenge for Businesses

Thu, 16 Oct 2025 13:42:03 GMT

Businesses today don’t just face financial data, they now also collect huge volumes of ESG data : emissions, social responsibility metrics, governance structures, supply chain practices, human rights compliance, and more. What was once a compliance checkbox is rapidly evolving into a strategic necessity. But that evolution brings a new set of data challenges that many companies are only just beginning to confront.

Regulatory Pressure and Reporting Scope Expansion

Regulation is increasingly pushing ESG from voluntary disclosure to mandatory reporting. For example, the EU’s Corporate Sustainability Reporting Directive (CSRD) expands reporting requirements to more companies and more detailed ESG areas. Businesses are seeing not just more data to collect, but more kinds of data, higher granularity, and tighter demands for auditability and assurance.

But many organisations are not ready. A Workiva survey found that 83% of companies believe collecting accurate data for CSRD will be a challenge. ESG Today Meanwhile, only around 29% of companies feel prepared for ESG data assurance, per recent KPMG research. KPMG As regulations deepen, the cost of lagging behind increases, not only through financial penalties, but reputational risk.

Data Quality, Standardisation, and Scope 3 Complexity

Collecting raw data is one thing; ensuring that data is accurate, consistent and comparable is another. Many senior executives report that data quality is one of the biggest hurdles in their ESG reporting journey. Sustainability News+2CIO+2

Scope 3 emissions (those from supply chain and indirect impacts) remain particularly difficult. It involves pulling data from many suppliers, often with very different data maturity levels or inconsistent systems. Deloitte’s 2024 Sustainability Action Report found that while many companies are making progress, Scope 3 reporting is still “rare” in practice, and many organisations cite data from suppliers as both incomplete and inconsistent. Sustainability News+2Ace Cloud Hosting+2

Standardisation is also a problem. Multiple frameworks—such as GRI, SASB, TCFD, and ESRS (under CSRD), all have overlapping but distinct requirements. This patchwork makes it difficult to align and compare data across organisations or geographies. Manifest Climate+2PwC+2

Technology, Resource & Governance Gaps

Many ESG data tasks still rely heavily on manual work—gathering, cleaning, and consolidating information from different sources, often via spreadsheets. Even in large organisations this leads to inefficiencies, delays, errors, and limited auditability.

Another big challenge is skills and resources : many companies don’t have enough in-house ESG or data experts. According to KPMG, skills and resources are viewed as the single biggest challenge in ESG assurance readiness. Proper technology platforms, data pipelines, and infrastructure, such as having a data warehouse or single source of truth for ESG metrics, are increasingly essential.

Governance structures are often under-built. Without clearly defined roles, data ownership, responsibility, audit trails, and metadata standards, it becomes difficult to ensure consistency, reliability, and confidence in ESG reporting.

Seizing the Opportunity

The businesses that succeed will be those that go beyond compliance and treat ESG reporting as strategic intelligence. This means using ESG data not just to report, but to inform leadership decisions, risk management, investment strategy, supply chain decisions , and operations.

Several organisations are increasingly using AI and automation to streamline data collection and improve accuracy. Platforms that integrate with utilities, supplier systems and supply chain data are helping reduce manual workload and human error.

Also, early movers are investing in analytics dashboards, scenario modelling, and predictive ESG indicators, looking ahead rather than just reporting what's already happened. This gives them competitive advantage.

What You Can Do Now

Map your data sources: Understand where ESG data lives in your organisation, including internal systems, supplier data, third-party datasets.
Prioritise metrics: Focus first on what matters most, those ESG areas that pose the biggest risk or opportunity for your business.
Invest in technology & automation: Tools to capture, process, validate ESG data can dramatically improve accuracy and reduce manual burden.
Build governance and ownership: Clear roles, responsibilities, data ownership, and quality controls across the organisation are essential.
Upskill your teams: ESG reporting demands both technical/data skills and subject-matter knowledge in sustainability, regulation, and materiality.

Final Thoughts

ESG reporting is fast becoming one of the most complex, but also most strategically valuable, data challenges businesses must master. The companies that fail to plan risk falling behind, not just in compliance, but in stakeholder trust, investor confidence, and competitive positioning.

Data isn’t just for finance or operations anymore, it’s central to how companies demonstrate responsibility, resilience, and long-term value.

If your organisation is looking to navigate ESG data complexity, build stronger reporting, or turn ESG into strategic value, it may be time to consider external analytics or data consulting support.

From Classroom to Career

Thu, 16 Oct 2025 13:17:52 GMT

How the Nurture Programme Helped Amit Become a Data Engineer

At Eden Smith, we believe the future of data and technology depends on one thing, people.

That’s why our Nurture Programme was created: to bridge the gap between academic study and industry practice by connecting MSc students in AI, Data Science, and IoT with real businesses solving real-world challenges.

Through short-term projects that double as dissertation opportunities, students gain hands-on experience, while businesses benefit from new perspectives, cutting-edge knowledge, and emerging talent.

One of our recent success stories is a collaboration with Rebura , a leading AWS consulting partner, and Amit Kumar Biswas , a student from the University of Salford. Together, they turned an academic project into a career-launching opportunity, and demonstrated exactly why the Nurture Programme works.

�� You can read Rebura’s perspective on the project here: How Rebura took Amit from Student Engineer to Data Engineer.

A Real-World Challenge That Made a Real Impact

When Amit joined Rebura’s Data team in January 2025, his brief was both ambitious and practical: to build an internal data portal that consolidated information from multiple sources into one easy-to-use platform.

The project involved integrating data from tools like Jira, Confluence, and AWS monitoring, then visualising it through tables and interactive dashboards. Working primarily with AWS technologies such as Chalice, Lambda, Cognito, Amplify , and API Gateway , Amit built a secure, scalable solution that made a tangible difference for the business.

But beyond the technical delivery, what made the experience transformative was the collaboration between Amit and the Rebura team. They created an environment where learning was encouraged, feedback was continuous, and innovation was expected.

“Working at Rebura has enabled me to experience my academic knowledge in a real-world scenario.”
- Amit Kumar Biswas, Data Engineer at Rebura

Developing the Next Generation of Data Professionals

The Nurture Programme isn’t just about technical projects, it’s about developing the full range of professional skills today’s data talent needs.

During his 12 weeks at Rebura, Amit didn’t just build an AWS solution. He strengthened his communication skills, learned to manage deadlines and stakeholder expectations, and gained the confidence to operate as part of a high-performing team.

His achievements included:

Designing and deploying an AWS-based data integration solution
Applying software engineering principles in a commercial setting
Translating technical detail into clear, actionable insights
Learning to collaborate effectively with both peers and senior engineers

This combination of technical growth and personal development is what sets the Nurture Programme apart, it helps students transition from the classroom to the workplace with confidence and capability.

“From day one with the initial interviews with Amit, it was clear he is a self-starter. His can-do attitude and thoughtful approach made asking him to join the team at the end of the project a no-brainer.”

- Dan Keely , Principal Data Engineer at Rebura

Shaping the Future of Green and Data Talent

For us at Eden Smith, Amit’s journey represents exactly what the Nurture Programme stands for: collaboration, opportunity, and the growth of tomorrow’s workforce.

“A heartfelt thank you to everyone at Rebura for their mentorship, support, and belief in the next generation of data professionals. It’s been a privilege to collaborate with you.”
- Marie May, Head of Education at Eden Smith

We’re proud to see Amit thriving as a full-time Data Engineer at Rebura, and even prouder to see businesses like Rebura investing in the next generation of talent.

If your organisation wants to make a tangible impact, not just on projects, but on people, the Nurture Programme can help you do exactly that.

�� Get in touch with Marie May to learn how your business can participate in the next Nurture Programme cohort and shape the future of data talent.

The Power of Storytelling in Business

Sat, 11 Oct 2025 19:44:03 GMT

Date: August 18, 2025 Event: Data Storytelling – influence with meaning

Live Webinar with Rosanne Werner, CEO of XcelerateIQ Hosted by: Marie May – Head of the Nurture Programme & NextGen Talent

Why Data Alone Isn’t Enough

Most of us have been there: you spend hours perfecting a report, walk into a meeting, present the numbers, see heads nodding… only for your insights to disappear when they move onto the next agenda item. No follow-up. No action. No change.

As Rosanne Werner , CEO of XcelerateIQ and former leader of Coca-Cola’s global Data & AI Mindset and Culture programme, explained in our recent webinar, the problem isn’t the data. The problem is the lack of a story.

Data doesn’t automatically change minds, shift budgets, or inspire action. People do. And human brains have evolved to remember and act on stories far more than on raw facts.

The Science of Storytelling in Data

Rosanne unpacked the neuroscience behind why storytelling is the most powerful way to present insights:

· Memory retention: 63% of people remember a story compared to just 5% who recall statistics.

· Whole-brain engagement: Stories activate every part of the brain - logic, emotion, and even motor functions - making information stick.

· Neural Coupling: Our brains mirror the storyteller’s, creating shared experiences.

· Oxytocin release: The feel-good brain chemical that builds connection and trust, making audiences trust the storyteller and more open to their ideas.

The key message? Data informs. Emotion moves.

Source: The Power of Visual Data Stories | The Science of Storytelling | InformIT

"I found it really helpful to have a reminder that data alone is not enough and that we need emotions and a story too. As a data scientists, I tend to get bogged down in the details sometimes and forget that others don’t love stats as much as I do!"

Busting the Myths: Why Data Doesn’t Speak for Itself

Two common misconceptions were explored:

“Facts are enough.”
If knowledge alone drove behaviour, we’d all sleep eight hours, walk 10,000 steps, and drink eight glasses of water daily. In reality, emotions are the primary driver behind most of our decisions, not logic. The decisions are often already decided in the subconscious, before our rational mind even catches up.
“Data speaks for itself.”
Seventy neuroimaging expert teams analysing the same raw functional MRI data reached different conclusions. No two teams chose the same approach to the data. Our brains are wired to anticipate, to fill in the gaps with our own knowledge, experience and biases. Storytelling is what connects the what to the why.

Source: Different Interpretations of Same Data Is Routine | The Institute for Creation Research

The Art of Data Storytelling

Start with Asking the Right Questions

Knowing how stories work isn't enough, you need to make them yours . Many professionals dive straight into analysis, only to watch their insights get ignored.

Rosanne shared that effective data storytelling requires strategic preparation :

Understanding your Audience – their decision making power, attitudes towards data and technical proficiency.
Key Business Problem to Solve – what keeps them up at night?
Outcomes to Achieve – What’s the desired result or strategic goal they want to achieve?

The Art of Data Storytelling

Start with Asking the Right Questions

Knowing how stories work isn't enough, you need to make them yours . Many professionals dive straight into analysis, only to watch their insights get ignored.

Rosanne shared that effective data storytelling requires strategic preparation :

Understanding your Audience – their decision making power, attitudes towards data and technical proficiency.
Key Business Problem to Solve – what keeps them up at night?
Outcomes to Achieve – What’s the desired result or strategic goal they want to achieve?

By understanding both the audience and the context, we can filter through mountains of data to find the insights that matter , and highlight their importance and consequences.

Avoiding Cognitive Overload

When presenting data, less is more. Rosanne highlighted three frequent pitfalls that overload audiences:

Graphical clutter – too many colours, symbols, or lines.
Excessive scales & labels – drowning viewers in unnecessary detail.
Poor visual design – forcing audiences to “decode” charts rather than see the message instantly.

The solution? Simplify. Emphasise key takeaways. Use visuals that support, not compete with, your narrative.

Narrative Structure and Emotional Engagement

Rosanne introduced the IMPACT framework which is a six-step formula for turning insights into action:

Intrigue – Open with a surprising fact, question, or scene.
Motivate – Show why it matters and what’s at stake.
Present Evidence – Share one clear insight, not 10.
Analyse & Answer – Explain the ‘why’ behind the numbers.
Call to Action – Be clear, urgent, and practical.
Tie Back – Close the loop with your opening hook.

As one attendee noted,

“I’m already thinking about how to use this structure when speaking to clients - it makes your message so much more engaging and memorable.”

This structure follows the traditional narrative arc (think your favourite blockbuster) and makes data impossible to ignore. More importantly, it makes the call to action unforgettable.

In summary

Data needs context, emotion, and narrative to drive decisions.
Stories are remembered 22x more than raw facts.
Tailor your story to the audience’s role, decision power, and level of technical expertise.
Use the IMPACT framework to move from information to action.

What’s Next?

This webinar was a taster of Rosanne’s upcoming Data Storytelling Course , launching in December 2025. The programme will provide:

Lifetime access to lessons
Templates and cheat sheets
Practical techniques to craft data stories that influence decisions

Join the waiting list today and secure your exclusive Eden Smith community discount: [https://www.xcelerateiq.co.uk/datastorytellingwaitinglist ]

“Data builds the evidence, but your story builds the trust, empathy, and action.” Rosanne Werner

Final Thoughts

Storytelling isn’t just for marketers, filmmakers, or leaders - it’s essential for anyone working with data. When you combine evidence + emotion + narrative , you don’t just present information. You create change.

Why Big Data LDN Mattered

Mon, 29 Sep 2025 12:26:28 GMT

Big Data LDN 2025: Key Talks, Top Takeaways & Next Steps for Data & AI Leaders

Big Data LDN celebrated its 10th edition on 24–25 September 2025 at Olympia London , attracting thousands of data and AI professionals. Across 300+ speakers and 16 theatres , the show spotlighted how organisations are moving from analytics to intelligent action .

The headline topics were:

Agentic AI and autonomous data workflows
Unified data platforms and data products
Governance-by-design and ethical AI
Real-time decisioning, observability and AI safety

“AI-powered is now everywhere… this year, ‘agentic’ had obviously joined most offerings.”

Keynotes that Set the Agenda

Google Cloud: The Agentic Era

Google Cloud explained how data science is shifting to autonomous agents that reason and act across enterprises . Their call to action: design for unified, AI-native platforms because silos can’t sustain intelligent agents.

What you can do: Identify five workflows where agents could boost productivity (e.g., pipeline remediation, cost governance) and create an agent safety spec with approvals and audits.

Professor Brian Cox: The Universe as a Quantum Computer

Professor Brian Cox reminded the audience that complexity, measurement, and physical limits shape how we compute at scale - a timely warning as AI ambitions grow.

Women in Data: Ethics at the Core

Sessions from Women in Data highlighted bias, harm modelling, and real-world impact . For many, this signalled a shift: ethics is now a core product requirement, not a compliance afterthought.

“This year’s talk… pulls together purpose, adaptive mindsets, and effective governance… speed without proper foundations is a liability.” Jovita Tam

Key Insights from Jez Clark: Culture & Talent Strategy for Business Impact

Jez Clark’s session on Culture and Talent Strategy underscored that people and mindset remain the foundation for every AI and data initiative. Drawing on his long experience at the heart of the data industry, Jez highlighted three key messages:

Culture is the hidden architecture of innovation
Without a shared purpose and psychological safety, even the best technology cannot deliver sustainable impact.
Talent strategy drives measurable business outcomes
Jez encouraged leaders to align data team structures and growth pathways directly with business KPIs, ensuring investment in skills maps to value creation.
Leaders must enable continuous learning and adaptive teams
Building learning loops - regular review, reflection, and re-skilling - keeps teams resilient in fast-changing AI environments.

“People create the conditions for technology to succeed. Culture and talent are not side projects - they are the strategy.” Jez Clark

For a deeper dive, you can read Jez's full presentation here .

Six Takeaways for Data & AI Professionals

Build for Agentic AI
Unify operational and analytical data, embed memory and tool-use interfaces, and implement observability and rollback from day one.
Governance by Design
Embed policies into data contracts and CI/CD, automate lineage-based controls, and generate continuous compliance evidence.
Trust as a Product Requirement
Model harm to users and non-users, publish transparent impact assessments, and keep ethics at the heart of every AI initiative.
Ruthless Data Product Management
Track business value, deprecate underperforming products, and encourage reuse via a semantic data marketplace.
Next-Level Observability
Monitor prompts, tool calls, cost, and guardrail events for both data pipelines and AI agents.
Real-Time Decision Automation
Combine streaming data, rules, and ML for closed-loop operations, with “pause/confirm” safeguards before full autonomy.

Quick 90‑Day Action Plan

Days 0–30: Create an Agent Safety Spec , audit your top 10 data products, and integrate PII checks into CI/CD.
Days 31–60: Launch two pilot agents (e.g., pipeline remediation and BI Q&A) and implement full observability.
Days 61–90: Promote a successful pilot to production, roll out a data product marketplace, and publish an AI Transparency Note .

Why It Matters

Big Data LDN 2025 made one message crystal clear: Unify before you amplify.

AI magnifies both value and risk. For data and AI leaders, now is the moment to put strong foundations in place, experiment safely, and scale only what proves real value.

See you there next year!

Data Leaders of the Future Will Be Translators, Not Just Technicians

Tue, 23 Sep 2025 15:48:59 GMT

The role of data leaders is shifting rapidly. For years, technical mastery, knowing how to build models, optimise databases, or fine-tune algorithms, was considered the core requirement for anyone at the top of a data-driven organisation. But as companies increasingly depend on data to guide decisions across every department, another capability is emerging as equally critical: translation. Tomorrow’s most effective data leaders will not just be technical experts; they will be translators who bridge the gap between technical teams and business stakeholders.

From Technical Expertise to Cross-Functional Leadership

In the past, organiaations primarily sought data leaders who could design infrastructure and extract insights. These skills remain vital, but they are no longer enough. Business leaders often struggle to understand what data teams produce, while data professionals sometimes lack the business context needed to shape truly strategic solutions.

This gap creates a disconnect, valuable insights sit unused, or worse, are misinterpreted. The future of data leadership lies in closing this gap. Leaders who can fluently “translate” between data science, business goals, and customer needs will ensure insights are not only accurate but actionable. They will be the bridge that allows data to flow seamlessly into decisions, culture, and strategy.

Why Communication Skills Are Becoming Mission-Critical

As organisations accelerate digital transformation, the complexity of data ecosystems grows. Data leaders are tasked with guiding executives, frontline staff, and even customers through this landscape. Doing so requires clear, persuasive communication.

The best leaders of tomorrow will explain technical outputs in plain language that inspires confidence and drives adoption. They will understand that data storytelling is as crucial as model accuracy . Moreover, these leaders will know how to listen, absorbing challenges from marketing, operations, or HR and reframing them into solvable data questions. Communication is not just about talking; it’s about co-creating meaning across disciplines.

The Rise of the “Data Translator” as a Competitive Advantage

McKinsey and other analysts have pointed out that “data translators” are increasingly valuable because they maximise the return on existing analytics investments. While AI models or dashboards may be developed in-house or outsourced, the translation layer, the ability to contextualise insights, prioritise their use, and secure stakeholder buy-in, cannot be automated away.

Organisations that nurture this skill set in their leaders will stand apart. Their data teams will not work in isolation but in synergy with every business function, accelerating innovation and decision-making. In effect, the translator becomes the multiplier of value.

Preparing the Next Generation of Data Leaders

Developing translators requires a shift in both hiring and training. Technical expertise will remain essential, but organisations must also prioritise interpersonal skills, empathy, and business literacy. Aspiring data leaders should cultivate public speaking, active listening, and negotiation skills alongside coding and statistics.

Business schools and data science programs are beginning to integrate these dimensions, but forward-looking organisations can go further. Rotational programs, cross-department collaborations, and mentorship opportunities help data professionals expand beyond technical silos. The leaders who embrace this hybrid path will be best positioned to thrive in tomorrow’s data-centric world.

What’s Hot, What’s Hiring, and Where It’s Headed

Mon, 08 Sep 2025 15:21:46 GMT

Trends, Talent, and Opportunities Shaping the Future of Data in the UK

The UK data market is evolving rapidly, driven by technological advancements, regulatory changes, and shifting business needs. For candidates, this presents a wealth of opportunities; for hiring managers, a dynamic landscape to navigate. Let's delve into the current trends, in-demand roles, and future directions of the UK data market.

Trending Skills and Technologies

In 2025, several key areas are defining the UK data landscape:

Generative AI and Machine Learning : With the rise of tools like ChatGPT, there's a surge in demand for professionals skilled in AI and machine learning. A recent study indicates that AI job postings grew by 21% from 2018 to 2023, with a notable shift towards skill-based hiring over traditional degree requirements arXiv .
Data Governance and Ethics : As businesses collect and utilize more data, the need for robust governance frameworks has intensified. The UK government reports that nearly half of businesses have struggled to recruit for roles requiring data skills, highlighting a significant skills gap in this area GOV.UK .
Cloud Data Engineering : The transition to cloud platforms continues to accelerate, necessitating expertise in cloud data engineering. A report by Lightcast emphasizes the importance of understanding shifting industry skills and measuring gaps to stay competitive Lightcast .

For candidates, upskilling in these areas is crucial. For hiring managers, attracting talent with these skills is essential to remain competitive.

In-Demand Roles in the Data Sector

The demand for data professionals in the UK is robust, with several roles standing out:

Data Engineers and Analysts : These professionals are pivotal in building and interpreting data pipelines. A report by ITPro highlights a significant shortage in digital skills across various industries, with data engineers being among the most sought-after roles IT Pro .
AI and Machine Learning Specialists : As AI technologies become integral to business operations, there's a growing need for experts who can develop and implement AI solutions.
Data Governance and Compliance Experts : With increasing regulatory scrutiny, professionals who can ensure data practices align with legal and ethical standards are in high demand.

Interestingly, fractional leadership roles are gaining traction. Companies are opting for senior expertise on a part-time or project basis to guide their data strategies without the commitment of a full-time hire.

Future Trends in the UK Data Market

Looking ahead, several trends are poised to shape the UK data market:

Integration of AI Across Sectors : As AI becomes more embedded in various industries, the demand for professionals who can integrate AI solutions into business processes will continue to rise.
Emphasis on Ethical Data Practices : With increasing concerns over data privacy and ethics, businesses will prioritize hiring professionals who can navigate the complexities of data governance.
Rise of Hybrid and Remote Roles : The shift towards remote work has expanded the talent pool, leading to more hybrid and remote data roles.

For both candidates and hiring managers, staying informed about these trends and adapting accordingly will be key to success in the evolving data landscape.

By understanding these dynamics, both candidates and hiring managers can better navigate the UK data market, ensuring they are well-positioned for current and future opportunities.

Ready to navigate the UK data market with confidence? Whether you’re hiring top talent or looking for your next data role, Eden Smith can help you find the right people, strategy, and opportunities to stay ahead. Get in touch today to explore how we can support your data ambitions.

The Hidden Factor Behind Successful AI Adoption

Tue, 02 Sep 2025 14:27:47 GMT

The Role of Data-Literate Boards

Artificial intelligence is reshaping industries, driving efficiencies, and unlocking new opportunities for growth. Yet for all the excitement, many AI projects fail to move beyond pilots or struggle to deliver real business value. Why? More often than not, it’s not the technology holding organisations back, it’s the lack of data literacy at the leadership level .

When boards and senior executives don’t fully understand the fundamentals of data, its limitations, or its potential, they can’t make the informed decisions needed to scale AI responsibly and effectively. Data literacy at the top table is the hidden factor that often determines whether AI adoption becomes a success story or a costly misstep.

Why Leadership Buy-In is the Starting Point

For AI initiatives to succeed, they must be backed by strong leadership. Boards set the vision, allocate resources, and define the metrics of success. Without their buy-in, projects risk being siloed in IT departments, underfunded, or disconnected from the broader business strategy.

Data literacy equips leaders with the ability to ask the right questions:

What data do we actually have, and is it reliable?
How do biases in the data or models impact outcomes?
What risks and ethical considerations need to be addressed?
How do we measure ROI beyond short-term cost savings?

When leaders can engage with these questions confidently, AI projects are more likely to be aligned with business goals, better governed, and more impactful. Importantly, data-literate boards can also communicate AI’s purpose and progress more clearly to investors, regulators, and employees, creating trust and transparency.

Bridging the Gap Across Departments

Successful AI adoption isn’t just about boardrooms and data science teams. It requires cross-departmental understanding and collaboration . Finance teams must interpret AI-driven forecasts. HR leaders need to assess AI’s role in workforce planning. Operations teams have to trust predictive models for supply chain or safety decisions.

This is where data literacy becomes an organisational competency, not just an executive one. By fostering a shared language around data and AI, organisations can avoid the common pitfalls of misalignment and mistrust. Employees who understand the basics of how AI works, and how it applies to their roles, are more likely to adopt it confidently, rather than resist it out of fear or uncertainty.

Creating this culture of literacy requires investment in training, but also in accessible tools and processes. Dashboards, visualisations, and natural language interfaces can make data insights easier for non-technical stakeholders to grasp, empowering everyone to participate in AI-driven decision- making.

Data Literacy as a Strategic Advantage

As AI adoption accelerates, the competitive edge won’t come from technology alone. Cloud platforms, algorithms, and machine learning frameworks are increasingly commoditised. The real differentiator will be how effectively organisations can embed AI into their strategy and operations , and that starts with data-literate leadership.

Boards that embrace data literacy don’t just understand the risks and opportunities of AI, they model a culture of curiosity, accountability, and informed decision-making that cascades through the business. They are better positioned to balance innovation with governance, to spot new opportunities early, and to build trust with stakeholders inside and outside the organisation.

In this sense, data literacy at the board table isn’t just a hidden factor behind AI adoption, it’s a fundamental requirement for sustainable, long-term success in the data-driven economy.

The Future of Hiring in Data

Thu, 14 Aug 2025 22:37:22 GMT

Blended Teams, Project Agility, and On-Demand Expertise

The way we build and scale data teams is changing, fast. As organisations race to harness AI, advanced analytics, and automation, the traditional “all-permanent staff” model is giving way to something more dynamic: blended teams .

This approach combines the stability of permanent hires, the flexibility of contract talent, and the strategic value of specialist consultancies. It’s a shift driven by the rapid pace of technology change, budget pressures, and the need for immediate, high-impact results.

For leaders, this isn’t just a staffing trend, it’s a blueprint for how to stay competitive in an era where skills evolve in months, not years.

Why Blended Teams Are Gaining Ground

The data talent market is both hot and highly fragmented. Roles like AI Engineers, MLOps Specialists, Data Product Managers, and Governance Leads are in high demand, but sourcing top talent can take months, and those skill sets may only be needed for part of a project’s lifecycle.

This is where blended teams offer a strategic edge:

Permanent staff provide continuity, culture alignment, and deep business knowledge.
Contractors bring in-demand technical expertise exactly when and where it’s needed.
Consultancies offer specialist insights, frameworks, and delivery capability for complex challenges.

By designing teams this way, organisations can scale up for major projects, pivot quickly when priorities shift, and avoid overcommitting to skills that may become obsolete in 12–18 months.

From Projects to Products: Building for Agility

The modern data environment is increasingly project-based , from implementing a new AI model to building a governance framework. Many of these initiatives need an intense burst of expertise, but not a permanent headcount.

Blended models give leaders project agility :

Start with consultancy-led strategy and road mapping.
Integrate contract talent to accelerate delivery.
Transition knowledge to permanent staff for ongoing operations.

This approach also helps prevent burnout in core teams, who can focus on business-as-usual while specialised project teams handle high-intensity sprints.

Forward-thinking organisations are even treating their data capabilities as products , with dedicated product managers, iterative roadmaps, and measurable business outcomes, making the blend of skills and contract types even more important.

On-Demand Expertise as a Competitive Advantage

The reality is, no organisation can house every data skill in-house all the time. Technology is moving too fast, and niche expertise is too valuable to sit idle between projects.

On-demand expertise allows businesses to:

Access rare skill sets without the long-term cost.
Move faster from concept to delivery.
Reduce risk by tapping into proven external specialists.

This isn’t about replacing internal teams, it’s about supercharging them . When permanent staff work alongside contractors and consultants, knowledge flows both ways, creating a richer, more adaptable capability.

The organisations leading in data today aren’t just hiring for roles; they’re orchestrating ecosystems of talent .

What Leaders Should Do Next

If you want to future-proof your data hiring strategy:

Audit your current capability - identify which skills are core, and which can be accessed on-demand.
Map your project pipeline - understand when peaks in specialist skills will occur.
Build trusted partnerships - with recruiters, contractors, and consultancies who understand your domain.
Invest in knowledge transfer - so skills brought in temporarily have lasting impact.

The future of hiring in data isn’t about choosing between permanent or contract, it’s about getting the mix right to deliver value faster, adapt to change, and stay ahead in a rapidly shifting landscape.

Data, Analytics & AI. Why Data and Why Now?

Wed, 23 Jul 2025 11:55:51 GMT

In today’s economy, data is everywhere, but value isn’t always present. Businesses generate massive volumes of data, yet many still struggle to understand how to get the best from it and prioritise as you would any other business-critical assets.

To thrive and become data-enabled, organisations must shift their mindset: data isn’t just a by-product of business activity, it is the business. When approached correctly, data becomes a powerful lever for decision-making, innovation, and long-term growth.

Here’s why it matters, and how to get it right.

Why Data is an Asset

Think of your most important assets: people, capital, intellectual property. Data supports each one. It enables smarter hiring, sharper investment strategies, more personalised customer experiences, and better operational resilience.

In short, data is not an IT issue. It’s a board-level concern.

Informed decision-making: High-quality data fuels better strategy.
Competitive edge: Organisations that harness their data effectively outperform those that don’t.
Long-term value: Like financial or physical assets, data appreciates when curated and protected properly.

If you’re not treating data as an asset, you’re already falling behind competitors who are actively using it in day-to-day decisions.

What Data Is an Asset

Not all data is created equal. The true asset lies in high-quality, actionable, and trusted data, the kind that helps you make better decisions, identify new opportunities, and mitigate risks.

Key examples include:

Customer insights and behaviour patterns
Operational and supply chain efficiency data
Employee engagement and workforce analytics
Financial forecasts and real-time performance metrics

It’s not about how much data you have. It’s about how useful it is. Poorly governed, outdated, or siloed data can do more harm than good.

How to Make Data an Asset

Treating data like a strategic asset isn’t just about tech infrastructure, it’s about mindset, governance, and culture. Here’s how to start:

Ownership and stewardship: Assign clear data ownership across the business, not just in the IT or data teams.
Governance and quality: Create frameworks to ensure accuracy, accessibility, and relevance of data.
Data literacy: Upskill leaders and teams so they can confidently use data to inform their decisions.
Alignment with business goals: Ensure that your data strategy directly supports core business objectives.

When teams understand the value of data, and are empowered to act on it, they start to generate real business outcomes.

Looking Ahead

The most successful organisations of the next decade will be those who treat their data not as exhaust from operations, but as fuel for growth. As AI and automation become more embedded in our systems, the value of well-managed, well-governed data will only grow.

The question isn’t if your business should treat data as an asset. It’s how soon you can start. Please see the link to the original article by Matt Small - Data as a Strategic Asset: Unlocking Hidden Value Across the Business | Data Value Creation | Data and Analytics Consultancy

Dinner meets Data

Wed, 16 Jul 2025 09:10:01 GMT

Tiny Bites, Big Data: The Canapé and Amuse-Bouche

Even the smallest dishes carry history.

Canapé comes from the French word for “sofa,” describing how a topping sits atop a base - like a person on a couch.
Amuse-bouche , or “mouth amuser,” emerged in the 20th century as a way for chefs to show off a flash of creativity before the main show.

In both cases, you’re looking at micro-expressions of culinary identity . Think of them as the “elevator pitch” of a chef’s worldview - one bite, one idea, one statement.

From Devilled Eggs to Data: What a 7-Course Meal Taught Me About Global Cuisine

Over the weekend, I hosted two Eden Smith Directors and their wives for a dinner party. It was a night of great company, conversation - and seven courses of food inspired by French classics.

Behind every carefully plated dish is a story - not just of flavour, but of history, culture, and, yes, even data.... so here are my reflections, with a data twist .

At first glance, this is the stuff of my culinary dreams. But zoom out just slightly, and you realise: this menu is also a mirror. It reflects a centuries-long evolution of how we eat, why we eat the way we do, and how food is an ever-evolving cultural code.

Naturally, I did a little research (and yes, pulled some data)… Let’s tuck in!

The Psychology of Courses: Why We Love to Sequence

Why do we enjoy meals served in courses? The answer goes deeper than "it's fancy."

The concept of multi-course dining dates back to Ancient Rome , where feasts began with a gustatio (appetiser), moved through mains, and ended with secundae mensae (dessert). But it was the 18th-century French who codified the structure into what we now know as the "tasting menu" or table d’hôte - a fixed, chef-curated journey of flavours.

Each course serves a purpose: to awaken, surprise, satisfy, cleanse, and close. It’s not just a meal - it’s a data-informed experience designed to guide the diner’s palate through peaks and valleys of taste, texture, and temperature.

The Menu:

Canapés:

Paprika-Dusted Devilled Eggs
Mini Smashed Pea & Mint Tortillas
Tomato & Onion Bruschetta with Sweet Balsamic Glaze

Amuse Bouche:

Mushroom Soup with Crispy Pancetta & Balsamic Mushrooms

Starter:

Petit Tartiflette with Cornichon Salad

Salad:

Warm Beetroot, Sweet Potato & Apple Salad with Roquefort Mousse

Main:

Sous Vide Fillet Steak with Mustard-Infused Mash, Honey-Roasted Carrot, Caramelised Shallots & Edamame
Finished with Bone Broth & Red Wine Jus

Dessert:

Raspberry & White Chocolate Semifreddo with Raspberry Coulis

Palate Cleanser:

Lime-Infused Mango with Raspberry Sorbet & Mint

Global Palates: What the Data Says

One of my guests requested a South East Asian-themed menu next time. So, I compared popular dishes across Western and Eastern cuisines to explore flavour preferences and cultural differences.

The results are probably not very surprising … and probably hasn’t changed since the 80’s!

Western Cuisine

- Starters �� Soup of the Day (35%) �� Pâté with Toast (30%) �� Smoked Salmon Salad (25%)

- Mains �� Fish & Chips (45%) �� Roast Chicken (40%) �� Beef Wellington (30%)

- Desserts �� Sticky Toffee Pudding (50%) �� Cheesecake (40%) �� Chocolate Brownie (35%)

Eastern Cuisine

- Starters �� Spring Rolls (40%) �� Dumplings (35%) �� Samosas (30%)

- Mains �� Sushi/Sashimi (50%) �� Dim Sum (45%) �� Butter Chicken (40%)

- Desserts �� Mochi (45%) �� Gulab Jamun (40%) �� Mango Sticky Rice (35%)

This isn’t just about taste - it’s about climate, culture, and consumption habits . Western dishes lean towards cream, meat, and dairy. Eastern menus favour rice, spices, fermentation, and balance between hot and cold, soft and crunchy.

What’s next on the menu?

Menus evolve. But they’re also a canvas for cultural storytelling - and the data underneath tells us who we are, where we’ve been, and what we value.

The next dinner party menu will have a South East Asian twist. Think lemongrass instead of shallots, tamarind instead of balsamic, pandan leaf instead of vanilla.

Final Bite

Whether you’re a data scientist, home cook, or curious diner, food is one of the richest datasets around

So next time you’re plating up dinner, ask yourself:

What story is this dish telling?
What journey is it taking you on?
And what kind of data - cultural, emotional, nutritional - is being served?

Because a great meal doesn’t just feed the stomach. It feeds the mind.

Until then, keep eating curiously and thinking analytically.

Why Recruitment Companies Matter for the Next Generation

workexpereince@edensmith.co.uk (Ellie Dickinson) — Mon, 14 Jul 2025 12:29:06 GMT

Unlocking Opportunities and Shaping Careers for Young Talent

Why go in at it alone when you can have a job-finding partner in your corner?

Imagine you’ve just finished your studies or are thinking about making your first big step into the working world. The options can seem endless yet overwhelming. Where do you start? How do you know which jobs are right for you? How will you make your difference in the world? This is where recruitment companies – like Eden Smith & Green Talent Solutions - can offer a helping hand. For young people, especially those just entering the job market, recruitment companies can be a valuable partner on the journey to a successful and fulfilling career.

How Recruitment Agencies Connect Talent with Opportunity?

Recruitment companies act as a bridge narrowing the gap between jobseekers and employers. Their main mission is to help companies find the right people for their job openings by analysing and implementing effective data strategies through expert consulting whilst, helping individuals find positions that fit their skills, interests, and ambitions. They work with a wide variety of industries and roles, from entry-level to executive, and often have insights into jobs that aren’t even advertised publicly helping to get your foot in the doorway.

No Experience? No Problem, Recruiters Know Where You Fit In.

Starting a career can be challenging and confusing especially for those new to the work life. For younger audiences, whether you’re finishing school, graduating from college, or just exploring options, recruitment companies offer a range of benefits that can give you a real edge and helping hand:

Insider Access to opportunities: many companies use recruitment agencies for their hiring needs, sometimes even before posting jobs online. By working with an agency, you could get access to positions that aren’t visible to everyone else, placing you at an advantage.
Guidance and Support: navigating job applications, preparing for interviews, and figuring out your career path can be daunting and create anxiety around the whole situation. Recruiters help polish your CV, offer interview tips, and provide personalised advice based on your strengths and interests.
Time Saving: job hunting can take a lot of time and energy. Recruitment companies streamline the process by matching you with suitable roles and handling some of the paperwork and communication for you, creating a smoother sailing experience.
Building Confidence: having a professional ally on your side can boost your confidence, especially if you’re nervous about entering the job market for the first time.
Feedback and Growth: Even if you don’t get the first job you apply for, a good recruiter will offer constructive feedback to help you improve for next time.

What Recruiters Actually Do for You

The process typically starts with a conversation. You might submit your CV and have a chat about your background, interests, and goals. Recruiters use this information to match you with suitable job openings. They’ll often help you improve and perfect your CV, prepare for interviews, and negotiate job offers.

Many recruitment companies specialise in particular industries - tech, healthcare, finance, creative arts, or (like us) Data & AI! By choosing an agency that focuses on your area of interest, you’re more likely to get advice and opportunities tailored to your ambitions.

What you’re missing out on

A significant portion of jobs are never advertised publicly. Instead, they’re filled through internal recommendations or, increasingly, through recruitment agencies. This “hidden job market” is especially important for younger people, who might not have extensive professional networks yet. By working with a recruitment company, you instantly gain access to their well-established contacts and insights, opening doors that could otherwise remain closed creating a valuable experience and a one up from other job seekers

Career services estimate that roughly 75% of job openings are filled through the “hidden job market” and never posted on public boards.

How Recruiters Help You Grow

Let’s look at some concrete ways recruitment companies can benefit younger jobseekers:

Access to Entry-Level Roles: many agencies specialise in roles for recent graduates or those with limited experience. They understand what it’s like to be at the beginning of your career and can connect you with companies looking to invest in young talent.
Career Exploration: Unsure about what you want to do is completely normal at a young age that’s why recruiters can introduce you to a variety of roles and industries, helping you discover careers you might not have considered.
Internships and Temporary Positions: Recruitment companies often have leads on internships, apprenticeships, work experience and temporary jobs, which are great ways to gain experience better your CV to stand out.
Mentorship and Networking: Recruiters have seen countless people start their careers; they can offer advice, encouragement, and sometimes even mentorship creating self-confidence and a sense of accomplishment. Plus, they can introduce you to key players in your chosen field.

Scrolling Past Your Best Career Help?

Social media has changed how young people approach job hunting - but not always for the better. With platforms like TikTok, Instagram, and LinkedIn full of career tips, influencer advice, and direct job postings, many young job seekers rely heavily on these channels and often overlook the value of recruitment companies.

The fast-paced, DIY nature of social media can give the impression that finding a job is as simple as sending a DM or following a viral hack. In fact, you need to compete for every role and engaging with those who are in a position requires proactiveness and persistency.

Passive DM’s that merely state “I’m interested in this role” hardly demonstrates commitment and is unlikely to get you any interview. Yes, you can find roles without a recruiter, but some young candidates miss out on the tailored support, insider connections, and long-term career planning that recruitment agencies offer. While social media can be a great starting point, it’s important to recognise that recruiters bring real-world experience and professional networks that algorithms and trending videos just can’t match.

How to Spot a Recruitment Company That Works for You

The right recruitment company is crucial when looking to join the work force and shapes your chances in succeeding so it is essential to find one that is a specialist in your chosen career pathway and reputable. To get the most out of your experience, look for agencies that:

Take the time to understand your goals, skills, and interests
Offer honest feedback and constructive advice
Have a solid reputation in your area of interest or industry
Communicate clearly and regularly
Respect your preferences and boundaries

Team Up with a Recruiter the Right Way

Be honest about your goals and limitations. The more they know, the better they can help you.
Keep your CV up to date and highlight your achievements, skills, and interests.
Stay open-minded. You might discover opportunities you hadn’t considered.
Ask questions! Don’t be shy about seeking advice on interview techniques, workplace culture, or career progression.
Keep in touch, even after you land a job. Recruiters can help you as your career evolves, offering guidance on promotions, new roles, or further training.

Shaping the Future

For younger audiences, recruitment companies are more than just job-finding services, they are gateways to new experiences, learning opportunities, and long-term growth. They play a vital role in shaping the workforce of tomorrow by connecting energetic, creative individuals with businesses eager for fresh talent.

In an ever-changing job market, having a professional ally can make all the difference. Whether you dream of launching an app, designing buildings, working in healthcare, or anything in between, recruitment companies can help you take the first step.

Next steps

The importance of recruitment companies for young people cannot be overstated. They offer unique access, expert guidance, and genuine support at a time when you need it most. If you’re ready to jump-start your career or just explore your options in the world of data and AI, reach out to a member of our team. Your next big opportunity could be just a phone call away. The world of work awaits, and with the right help, you can step into it with confidence and excitement.

Umbrella Reform Is Coming; Make Sure Your Staffing Partners Are Proactive and Compliance First

Wed, 25 Jun 2025 16:23:21 GMT

With draft legislation on umbrella company reform expected July 2025, much of the conversation has focused on the umbrella providers themselves. But if you are an end client engaging contractors through recruitment agencies, you may be closer to the risk than you realise.

The government’s consultation made one thing clear, where non-compliant or financially unstable umbrella providers are used, liability could be shared across the supply chain, including you as the client.

Working only with compliant, financially stable umbrella companies has never been more important with the full force of legislation expected to be implemented in April 2026. The risk is no longer limited to the umbrella provider. Clients must take a more active role in ensuring their supply chains are secure, transparent, and compliant.

Now is the time to ensure that your staffing partners are taking a proactive, compliance first approach to protect your business.

Eden Smith & Future-Proofing Clients Against Compliance Risk

At Eden Smith, we understand that in a fast-evolving regulatory environment, peace of mind comes from knowing your staffing partner is compliance-first. We’ve been working ahead of the curve, embedding enhanced governance processes and early onboarding with trusted partners – including Giant, a long-standing industry leader in IR35 compliance and umbrella management.

But we don’t just talk about compliance – we deliver it through action. From stringent supplier due diligence to routine auditing and contractor education, our processes are built to shield clients from risk and give them full visibility and assurance across the hiring lifecycle.

Flexible Hiring, Without the Compliance Guesswork

We also know that compliance doesn’t have to come at the cost of flexibility. Eden Smith works with clients to design talent solutions that meet business needs without exposing them to legislative risk. Whether you need:

Contractors via compliant umbrellas
Fixed-term consultants
Permanent hires – on either a retained or contingency basis

…we help structure your workforce for agility and growth – without tripping over red tape!

In many cases, we also help clients unlock hiring budgets by advising on the most cost-effective and compliant route to talent. With deep understanding of headcount constraints, IR35 complexities, and project urgency, we position ourselves not just as a recruiter, but as a strategic workforce partner.

What will Change?

The proposed reforms are expected to introduce:

A statutory definition of an umbrella company, which will end any ambiguity about who qualifies
New responsibilities for recruitment agencies to verify the compliance and financial stability of umbrella providers
Joint & several liability, meaning that if an umbrella company fails to meet its tax or worker obligations, your recruitment partner, or even your own business, could be held responsible for unpaid tax

This is not just a change in definitions, it is a shift in accountability . Clients will need to be confident that every link in the supply chain can stand up to scrutiny.

The Risk to You as a Client

Even if you don't choose the umbrella company directly, your agency might. And if that umbrella operates non-compliantly, the financial penalties, reputational damage, and lengthy HMRC investigations could land on your doorstep.

That’s why it’s critical to ensure your staffing suppliers are acting proactively, not reactively, to protect your business.

What You Should Be Asking Your Staffing Partners

To stay protected and ahead of the changes, now is the time to challenge your staffing partners. Here is how to get started:

1. Ask for a review of their umbrella PSL

Find out who they are working with. Are those umbrellas established, financially sound, and subject to regular audits?

2. Request documented due diligence

Your agency should be able to show exactly how they have assessed and approved their umbrella providers. Relying on verbal assurances is not enough.

3. Insist on proof of compliance, insurance, and financial stability

This should include:

PAYE compliance
Appropriate insurance cover
Financial health checks or audits
Confirmation of good standing with HMRC

4. Expect consistent, auditable record keeping

Any umbrella-based engagement should be traceable, auditable, and clearly documented to protect your business if HMRC or legal teams ever need to investigate.

5. Check internal training levels

Consultants and compliance staff should be fully briefed on the reforms and be ready to answer your questions about how risk is being managed.

A Strategic Opportunity

Working with staffing partners who prioritise compliance over convenience is more than a regulatory necessity – it’s a strategic advantage.

At Eden Smith, we see these reforms not just as a burden, but a chance to future-proof your workforce, reduce hidden costs, and streamline your hiring decisions with full confidence in compliance.

The Bottom Line

Shared liability means shared responsibility, and it starts with your staffing partner.

Thanks to our proactive processes, our trusted partnership with Giant, and our flexible approach to hiring models, Eden Smith is helping clients stay protected, agile, and ready for the legislative shift ahead.

Now is the time to ask the right questions, and partner with the right people. How can we help?

Leading a Transformation

Wed, 25 Jun 2025 16:03:15 GMT

Leading a Transformation Enterprise | LAB Webinar Insights | June 2025

Transformation is no longer a choice - it’s a necessity. At our latest Leaders Advisory Board (LAB) webinar with Jane Smith , we explored what it truly means to lead a transformation enterprise in 2025. Featuring Claire Lock , a global HR transformation specialist, and Niresh Rajah , an award-winning CDO and AI executive, the session brought together powerful insights on people, technology, and organisational change.

Transformation Is Everyone’s Business

Claire opened with a bold statement: HR is no longer a support function. Whether in startups or global organisations, HR is now a core strategic partner - leading on scale, maturity, and talent strategy. The shift to people-first, globalised business models demand a fresh approach to HR: one that’s data-driven, technology-enabled, and obsessed with simplicity.

Claire's five-pillar framework - Align, Design, Enable, Analyse, Measure - offers a pragmatic roadmap for HR leaders to lead enterprise change from within. At its heart: designing intelligent, scalable operating models and empowering teams with the right tech, insight, and support.

The AI Disruption: Risk or Opportunity?

A recurring theme was the impact of AI on talent models. Claire and Niresh both acknowledged the fear and fatigue surrounding automation -especially among those in entry-level or technical roles. But rather than replace people, AI should be used to elevate them.

Niresh shared a compelling case study from Danske Bank: 500 operations staff were cross trained in data and AI skills, enabling a pivot into newly emerging roles. Structured mentoring and business-aligned learning were critical to this success.

The message was clear: Upskilling isn’t just a moral responsibility - it’s a competitive advantage.

The Human Side of Change

The session underscored that transformation is personal. Claire spoke candidly about the emotional cycle of change and role displacement - from shock to acceptance - and the importance of leading with integrity. Whether redeploying talent or reshaping teams, it’s the how that people remember.

“Change fatigue” - a reality many leaders are grappling with - requires organisations to slow down, listen deeply, and re-engage disengaged employees through honest conversation and practical action.

Leading with Purpose

As Niresh reminded us, success in transformation is not about having all the answers. It’s about building trust, crafting a clear vision, and being brave enough to say no when needed. Whether you’re launching a GenAI initiative or reimagining your operating model, the foundations remain the same:

Start with people.
Ground decisions in data.
Communicate consistently.
Lead with empathy.
And always, always keep learning.

Join the conversation: If you're a senior leader driving change in your organisation, connect with like-minded peers in the Leaders Advisory Board LinkedIn Group . Let’s build the future, together.

How the UK's Invest 2035 Strategy Empowers Data Professionals and Business Leaders

Wed, 25 Jun 2025 14:49:48 GMT

With the UK’s “Invest 2035” modern industrial strategy now live, business leaders and data professionals face both a unique challenge and opportunity. This 10‑year blueprint, reinforced by £22 bn+ in R&D funding and deep energy policy reforms, puts digital transformation, AI, and data-enabled innovation at the centre of national productivity.

For business and data professionals alike, this is a wake-up call to align, adapt, and lead.

In this article we wanted to explore how our community, as data professionals and as business leaders, can respond to elevate their professional careers and lead in business.

Guidance for Data Professionals at Every Stage

Entry-Level (Career Starters)

e.g. recent grads, bootcamp completers, apprentices

Key Advice:

Build foundational skills fast: Focus on SQL, Python, Excel, data visualization (Power BI/Tableau), and cloud basics (Azure, AWS).
Work on real projects: Freelance, open-source, or personal projects build credibility.
Apply even if you’re not 'ready': Hiring is now based more on potential and portfolio.
Use new routes: Look at apprenticeships, graduate schemes, NHS Digital, ONS, GDS.
Understand your chosen sector: Data needs vary across retail, charities, health, energy.

Junior Professionals (1–3 Years Experience)

e.g. data analysts, junior data scientists/engineers

Key Advice:

Sharpen your niche: Decide your path: engineering, ML, BI, or AI ethics.
Ask for stretch projects: Innovation sprints, cross-functional teams offer growth.
Improve communication: Learn to present your insights clearly.
Build your public portfolio: GitHub, blogs & LinkedIn profiles matter.
Upskill smartly: Cloud data platforms (e.g. Databricks), domain skills, or AI tools.

Mid-Level Professionals (3–7 Years Experience)

e.g. analytics leads, senior engineers, data scientists

Key Advice:

Solve business problems, not just technical ones. Be proactive.
Mentor others: Develop leadership and communication – our Nurture Programme is great for adding capability to your team and demonstrating your skills in leadership.
Bridge technical and commercial teams. Find ambassadors, solve challenges and win hearts and minds.
Build professional networks: Meet people in person and be active in webinars – try Big Data LDN, Women in Data, AI for the Rest of Us, The OR Society.
Think like an architect: Scalable systems, governance, model ops.

Senior Data Leaders (8+ Years / Heads of Data, CDOs, etc.)

Key Advice:

Use Invest 2035 as a strategic lever: Justify data investments by tying them to productivity and sector growth.
Champion ethics & diversity: Stay ahead of privacy, ESG and governance trends.
Upskill and reskill internally: Create academies, support apprenticeships and build talent pipelines and succession plans that support diverse teams and cross-skilling.
Build influence externally: Speak, publish, sit on boards.

What Business Leaders Should Do Now

1. Align Data Strategy with National Priorities

Put data at the centre of business strategy: Tie data initiatives to revenue, cost, satisfaction, compliance.
Map your data goals to IS‑8 sectors: e.g. predictive maintenance in manufacturing, demand forecasting in energy, creative analytics in media.
Frame case studies using industrial strategy language: cost-savings via efficient grid use or exporting AI solutions within clean energy.

2. Invest in Talent to Fuel Innovation

Invest in the right roles: Build cross-functional data teams that span ops, product, finance.
Create a data-literate culture: Build internal confidence and reduce reliance on specialists. Where using consultancies, make sure you look for a team with a focus on people, knowledge transfer and an exit plan.
Recruit for skills and leverage global talent using brighter visa paths.

3. Leverage Regulatory & Financial Tailwinds

Exploit energy cost savings from subsidy schemes: reallocate those resources into data infrastructure.
Use public procurement reforms to bid for government-backed digital pilots and innovation tenders.

4. Use Regional Clusters & Collaborations

Use public-private opportunities: Tap into Innovate UK grants, AI accelerators, and regional data clusters.
Build partnerships within regional industrial clusters (e.g., West Yorkshire, Midlands, Scotland).
Engage with local councils and devolved bodies to co-design data-driven initiatives aligned with regional growth strategies.

5. Be Data-Aware in Governance & Ethics

Establish trust through governance: Compliant, clean, ethical data is a reputational asset and essential for carbon reporting.
With heavy R&D and AI focus, establish robust data governance, privacy, and responsible AI frameworks .
Engage with the new Industrial Strategy Council and be ready for annual progress monitoring and compliance metrics

6. Create Value Through Metrics & Storytelling

Define and track value: Set clear KPIs: time saved, insights delivered, churn reduced and tie to the strategy’s objectives: energy saved, processes optimised, new revenue streams in priority sectors.
Frame achievements in narrative form: "X% CO₂ reduction in our clean energy operations", or "AI-driven supply chain cost savings".

In summary

Invest 2035 is more than an economic plan. Professionals must prepare, and leaders must guide - because data maturity will define market leadership in the years ahead.

Business leaders must embed data at the core of innovation, finance, and operational decision-making to ride the Invest 2035 wave.

Data professionals should seize new talent pipelines, grand-challenge funding, and cross-sector collaborations to show measurable impact. Together, this alignment will bolster competitiveness - not just for individual firms, but for the UK as a data-powered, industrially resurgent nation.

Opportunities AI Creates for Early-Career Professionals

Wed, 25 Jun 2025 10:57:52 GMT

Artificial Intelligence (AI) is rapidly transforming the workplace and early-career professionals are at the forefront of this change. As we continue to integrate AI into more aspects of business, it’s vital to understand how this technology will shape career paths, opportunities, and expectations for the next generation of talent… and support the transition before the skill gap widens any further.

Leaders within our space have a responsibility to ensure early careers are supported within their organisations through well-structured onboarding programmes that ultimately offer a broader range of more advanced projects than entry level positions would typically offer.

Is AI an Opportunity or a Challenge?

The answer is both. The challenge being that there is a phasing out some traditional entry-level responsibilities. However, this opens the door to opportunities that have never been more exciting!

How to Leverage the Opportunity

1. Start Closer to the Core

Traditionally, junior roles in data or analytics were focused on data entry, cleaning, or report generation. Today, AI tools like Python-based automation, data cleaning scripts, and platforms such as Dataiku or Alteryx streamline these tasks.

This allows early-career professionals to:

Participate in hypothesis design, stakeholder discussions, and model evaluations.
Contribute to solving real-world problems early in their career.
Gain exposure to the business impact of data from day one.

2. Build Human-Centric Skills

The World Economic Forum (2023) identifies analytical thinking, resilience, and emotional intelligence as some of the most in-demand skills. While AI excels at pattern recognition and prediction, it still lacks true creativity, empathy, and ethical judgment — areas where humans shine.

Early-career professionals should:

Hone their communication and storytelling skills.
Learn how to translate data into decisions.
Build cultural and ethical awareness to address bias in AI.

3. Work Across Disciplines

AI enables data fluency across domains. No longer siloed in IT, data roles now intersect with marketing, product development, HR, and more. This convergence empowers early professionals to:

Rotate through departments.
Collaborate on multi-disciplinary teams.
Become T-shaped professionals - broad generalists with a deep skill in one area.

4. Bring Fresh Perspectives

Early-career professionals are uniquely positioned to:

Spot emerging ethical risks that others overlook.
Ask naïve questions that surface assumptions.
Advocate for underrepresented users.

This is particularly important as AI systems increasingly influence access to jobs, credit, education, and justice and this paid article from MIT on what is AI is a interesting guide .

5. Become AI-Native Innovators

According to the Deloitte 2025 Global Gen Z & Millennial Survey , Gen Z professionals - having grown up with digital tools - are often more comfortable experimenting with no-code platforms, AI assistants like ChatGPT, and collaborative tools. They are well-placed to:

Automate routine workflows.
Prototype internal tools.
Innovate new solutions faster than their predecessors.

Advice for Those Starting Out

Start building now : Use open datasets and platforms (Kaggle, Google Colab, GitHub) to build a portfolio.
Master the fundamentals : Focus on Python, SQL, and basic statistics. They're still the foundation.
Stay curious : Read broadly - not just technical AI, but ethics, policy, and user experience.
Find a mentor : Whether through LinkedIn, Slack communities, or local meetups, mentorship accelerates growth.
Contribute to open source : It’s a great way to learn, get noticed, and give back.

Challenges to Watch

Shrinking Entry-Level Tasks
Traditional ‘foot in the door’ roles involving admin, data entry, or scheduling are increasingly handled by AI raising the bar for first-job candidates, meaning young professionals must enter the workforce with a more advanced skill set or risk being left behind.

Advice for Leadership

To address this, organisations must take responsibility for keeping the early-career pipeline open. Leaders should design intentional pathways for skill development, embedding peer learning, mentorship, and real-world experience into workplace models. Without proactive leadership, the skills gap will continue to widen - stacking the odds against the next generation and weakening the long-term resilience of the workforce.

Rising Expectations for AI Literacy

Today’s early career professionals are expected to be not just tech-savvy but AI-literate. Employers are looking for people who understand how to use AI tools effectively and ethically. This includes knowing when and how to use AI, interpreting results, and ensuring bias and misinformation are minimised.

Advice for Leadership

This growing expectation can be daunting, especially for those just entering the workforce. Leaders have a crucial role to play in bridging this gap. That means embedding AI training into onboarding and development programmes, fostering a culture of experimentation and learning, and making space for open dialogue about responsible AI use. By demystifying AI and supporting ongoing learning, organisations can empower the next generation to become confident, capable contributors in an AI-driven world.

Rapid Change, Rising Uncertainty

As AI tools evolve, job descriptions are shifting quickly. This constant change can create uncertainty for early-career workers, who may struggle to keep up with what’s expected or fear that their job might be automated.

Advice for Leadership

Leaders must recognise and respond to this uncertainty. That means clearly communicating how roles are evolving, providing pathways for continuous upskilling, and offering support structures that help individuals adapt. By fostering transparency, psychological safety, and a growth mindset, organisations can turn the anxiety of rapid change into a foundation for resilience and opportunity.

Source Talent Who Are Ready to Grow with You

Finding early-career professionals who are both qualified and ready to deliver from day one is a growing challenge. That’s where our Nurture Programme makes a difference. Designed for MSc students, it combines academic expertise with hands-on industry experience through live industry placements - equipping participants with the practical skills and confidence needed to make an immediate impact in the workplace.

What is the Nurture Programme?

The Nurture Programme is a three-month, live project experience designed to give graduates:

Real-world exposure in a business-critical setting
Opportunities to apply their theoretical knowledge
Practical, hands-on skills in problem-solving, collaboration, and communication

Bridging the Gap Between Education and Impact

We also support organisations with graduate recruitment by connecting you with talented individuals who have just completed our Nurture Programme.

Our graduates aren’t just fresh from their MSc studies, they’ve been immersed in live commercial projects, developed professional behaviours, and are eager to continue growing in a full-time role.

By partnering with us, you gain access to a diverse pool of pre-vetted, motivated, and work-ready talent.

Hire With Confidence

Access a pool of graduates who’ve proven themselves on real projects
Reduce onboarding time and training costs
Support your early-careers strategy with impact-ready individuals

Whether you’re filling entry-level roles or growing your graduate pipeline, we can connect you with talent that aligns with your goals.

Final Thoughts

The future of AI is not something to fear it’s something to prepare for. AI isn't eliminating entry-level roles - it's upgrading them. For those willing to adapt and learn, you will progress with a more interesting pathway than those who have gone before you. For early-career professionals, success will depend on a blend of curiosity, flexibility, and the willingness to evolve. Those who are proactive, digitally fluent, and emotionally intelligent will find that AI is not a threat, but a powerful ally in shaping a dynamic and fulfilling career.

Let’s Connect

Are you currently recruiting or planning to bring on graduate talent?

Please get in touch to learn more about our Nurture Programme Alumni, we’d be delighted to introduce you to the next generation of high-potential professionals.

Every career needs a strong start. Let’s build that beginning together. Reach out to Marie May to learn more about how we can support you to develop robust and diverse talent pipelines.

In an AI World, People Still Make the Difference

Wed, 25 Jun 2025 10:57:49 GMT

The Rise of AI and a Call to Action

In my last article I wrote about how our own human intelligence, coupled with power skills, can allow us to compliment and get the most from the world of emerging technology.

However, the more I read and observe how AI is being used across industries, the more I feel compelled to issue both a warning and a call to action. Now, more than ever, we all - individuals and organisations alike - have a duty to nurture the next generation of talent, consultants, and digital professionals.

Redefining Entry into Industry

We must create meaningful pathways into industry and invest in training that develops not only technical expertise but also the human skills that AI cannot replicate.

As we transition, resilience and adaptability are key skills to look for and nurture in your talent whilst reducing fear and taking the lead in reshaping - and communicating - the opportunities AI creates for early-career professionals is the responsibility of our leaders.

Consulting in a Time of Volatility and Automation

This year has seen continued volatility across sectors, with consulting firms being asked to do more with less - deliver innovation, improve efficiency, and solve complex problems within tighter budgets.

Clients are demanding tailored, high-impact solutions, often rooted in data, AI, and advanced analytics.

Using AI as a Tool, Not a Crutch

Many companies like ours have embraced AI as both a tool and a catalyst. We've invested in capabilities that help us drive smarter decision-making, automate routine processes, and unlock new business models for our clients.

But while AI has undoubtedly made us more efficient, it has also forced us to rethink the role of human insight and emotional intelligence.

The Risk of Losing Core Human Skills

If we’re not careful, we risk losing some of those core soft skills such as critical thinking, creativity, and interpersonal communication.

AI is not capable, in my opinion, of understanding people, culture, and the impact of change - factors that ultimately pave the way through complex transformation.

Meeting New Customer Expectations in the AI Era

As a small consultancy, we are seeing a very different set of expectations from our customers -driven by the rise of generative AI and automation, and with it, a rise in the “art of the possible.”

Human-Centric AI: Making It Make Sense

So, what’s the answer?
In my view, we must focus on higher-impact, human-centric AI solutions. That means helping our customers and communities of people to Make AI Make Sense .
We are focusing on ethics, efficiency, and empathy in the team’s we engage with.

Challenging Tech for Tech’s Sake

We’re trying to have conversations where we question tech for tech’s sake.
Let’s make it meaningful - and frame all conversations through the lens of transformation and the impact it will have on people.

Leading with Understanding, Not Just Technology

By leading with understanding - not just technology - we can highlight case studies where success hinges on human connection rather than algorithms. This is best delivered through workshops that emphasise soft skills as essential to achieving outcomes, with technology supporting the process rather than driving it.

Putting People First: The True Competitive Advantage

As a nimble business with PEOPLE at our core, we’re committed to supporting the next generation of talent and contributing to the wider conversation. We believe this approach will help cut through the noise and ground AI’s role in real-world impact.

The Real Lesson: People Still Make the Difference

What I’ve personally learned over the last six months is this: no matter how much AI automation we implement, it’s not what sets us apart in the data & AI space.
Our success still lies in our ability to put people first, build relationships, and get to the heart of challenges - online or, even better, face to face.

A Collective Focus on Community and Connection

That’s where we will continue to focus our time and efforts for now. Hopefully many of you will agree and we can all support each other in the continuation of building great communities that share challenges and successes in a safe environment.

The UK Data Jobs Market in 2025

Tue, 17 Jun 2025 12:59:42 GMT

Data Jobs Market Requires Flexibility, Specialisation and offers Regional Opportunity

By Matt Smith – COO Eden Smith Group

Every quarter we update the market and as we leap into H2, I have collated key research and findings that highlight how the UK jobs market is adapting to a highly volatile stress economy.

The UK jobs market continues to adapt to ongoing economic uncertainty, digital acceleration, and a persistent skills gap - particularly where we operate in the data and analytics space. Our ethos at Eden Smith is to support the data community with relevant stats and information to help client decision making. We’ve been closely monitoring the market dynamics to help employers and professionals navigate the evolving landscape with confidence.

In this article, I explore the latest hiring trends, regional differences, and the practical steps employers can take to stay ahead in the competitive data talent market.

The Big Picture: A Market in Transition

While the broader professional recruitment market is showing tentative signs of recovery, the data industry stands out for its resilience and continued growth. Employers are increasingly opting for flexible hiring models , using contractors and freelancers to close urgent skill gaps - especially in data engineering, governance, AI, and analytics.

"After a challenging period, we are finally seeing clear signs of recovery in the data and AI job market. Over the past quarter, demand has steadily increased across both contract and permanent positions, giving us cause for cautious optimism." Jonathan Blaber | Head of Staffing at Eden Smith

According to recent research from the Association of Professional Staffing Companies (APSCo), contract job postings in the UK increased by 11% year-on-year in May , with placements rising steadily. This is driven largely by acute shortages in specialist areas: 64% of UK employers report being impacted by skill deficits , particularly in data and tech. Further reasoning lies with businesses hesitant to hire full time employees (FTE’s) in an uncertain economic environment, projects still require delivery so a move to utilising Capex spend to keep the lights on.

Contracting: The New Normal for Data Hiring

The rise of the data contractor is reshaping how businesses access technical talent. From day-rate Data Analysts to specialist Machine Learning Engineers, the freelance model is thriving.

Average contract day rates for data professionals in the UK currently stand at:

£500–£750/day for Data Scientists and Engineers in London
£425–£500/day in Manchester and the North West
£400–£450/day across the Midlands and other regions

High demand and short supply are pushing up these rates - especially for AI and ML talent, where the most experienced professionals can command upwards of £850/day .

Eden Smith’s Advice:

“For time-sensitive digital or AI projects, hiring a skilled contractor can offer speed, flexibility, and impact. We recommend clients benchmark rates by region and skill set - and consider contract-to-perm pathways to secure long-term talent.”

Regional Insights: Where Demand Is Growing

London & South East
Unsurprisingly, the capital remains the beating heart of the UK’s data economy. With over 70% of the UK’s high-paying data jobs based here, demand remains consistently high. Day rates are elevated, but so too is the competition for talent.

Manchester & the North West
This region is fast becoming a northern powerhouse for data roles, particularly in fintech and digital transformation. Strong university links and lower contractor rates compared to London make it an attractive location for scaling data teams.

Birmingham & the Midlands
The Midlands is seeing steady growth in public sector data roles and finance-based analytics. Median contractor day rates hover around £400/day, offering excellent value for employers and attractive opportunities for mid-level professionals.

Northern Ireland
An emerging outlier, with surprisingly high day rates (median £581/day), reflecting both a small but competitive market and limited local supply.

Eden Smith’s Advice:

“Regional hiring offers real advantages. Employers in London can look to remote contractors from the North West or Midlands to balance cost and capability, while professionals outside the capital are enjoying access to better remote roles than ever before.”

Permanent Hiring: A Gradual Rebound

Although contracting dominates the current narrative, permanent hiring is beginning to recover. LinkedIn and REC ( follow me on LinkedIn if you want to see the latest stats every month ) data show slight month-on-month increases in job postings and placements, suggesting growing employer confidence .

Yet, caution remains - many businesses are still reluctant to commit to long-term hires without clear ROI or internal upskilling in place.

Strategic Recommendations for Employers

Whether you're scaling a data team, launching an AI project, or stabilising your workforce, here’s our guidance:

1. Embrace a Blended Workforce

Combine permanent staff with flexible contractors to bridge gaps, manage risk, and increase delivery capacity.

2. Plan Hiring Around Regional Value

Leverage location data to source competitively priced, high-quality professionals - particularly from Manchester, Birmingham, and remote-ready regions.

3. Benchmark Day Rates Proactively

Keep a close eye on market shifts. Rates for specialist data talent are moving fast - paying fairly secures loyalty and quality.

4. Prioritise Skills Over Job Titles

The most in-demand professionals offer practical, hands-on experience - regardless of formal qualifications. Skills-first hiring is more predictive of success in data roles.

5. Partner With a Specialist

Generalist recruiters often miss the nuance of data talent. A sector-focused partner like Eden Smith provides insight, speed, and the credibility needed to engage top-tier professionals.

Final Thought

The data economy isn’t slowing down - it’s adapting. Employers that embrace flexibility, think regionally, and invest in talent strategically will not only weather current challenges but emerge more capable, competitive, and digitally mature.

"As the market continues to evolve, so too does the hiring landscape. We will be keeping a close eye on where the demand shifts next and are always here to support clients and candidates navigating this exciting space." Jonathan Blaber | Head of Staffing at Eden Smith

At Eden Smith, we’re proud to support this evolution - connecting businesses with the right data professionals to thrive in 2025 and beyond.

Need help sourcing top data talent or shaping your workforce strategy?
Contact me directly or reach out to our data recruitment specialists directly.

Eden Smith’s Advice:

“Now is a smart time to secure permanent talent before competition heats up. If budgets allow, locking in strategic data hires - particularly in data governance, analytics leadership, or cloud platforms - can build long-term resilience.”

Sustainable Data Practices & What New Data Professionals Should Know

Tue, 10 Jun 2025 13:10:59 GMT

As data becomes one of the world’s most valuable assets, it's also becoming one of the most energy-hungry. From powering massive server farms to training AI models, the digital footprint of data is growing rapidly and with it, the need for sustainable data practices. For new professionals entering the field, understanding the environmental and ethical implications of data is no longer optional. It’s part of being a responsible and future-facing data practitioner.

What Does “Sustainable Data” Really Mean?

Sustainability in data is about more than recycling old devices or turning off unused servers. It encompasses a broad range of practices that reduce environmental impact, improve data ethics, and ensure long-term societal value. Key areas include:

Ethical data collection : Ensuring transparency, informed consent, and fair usage of data.

Energy-efficient infrastructure : Leveraging green data centres, optimising query loads, and using energy-conscious algorithms.

Data minimisation : Only collecting and storing data that adds real value, avoiding the "hoard everything" mentality.

Lifecycle management : Building strategies for archiving, deleting, or anonymising data to reduce storage needs over time.

These principles are not only good for the planet, they’re also good for business. Sustainable data management reduces costs, builds trust with users, and supports compliance with growing global regulations like GDPR and the UK’s Environment Act.

Why Businesses and Professionals Need to Care Now

Sustainable data practices are no longer a "nice to have", they're becoming a competitive advantage. Businesses are under pressure from regulators, investors, and consumers to align with Environmental, Social, and Governance (ESG) goals. That includes how they handle their data.

According to a Capgemini report, over 60% of organisations now consider their data infrastructure when assessing environmental performance. Cloud providers are also joining the movement, Microsoft, Google, and AWS have all announced carbon-neutral or carbon-negative goals, influencing how data is stored and processed.

For early-career professionals, this means more than understanding Python or SQL. It’s about developing digital responsibility: learning how your technical decisions affect sustainability outcomes and becoming a voice for ethical and efficient practices in your team.

Whether you're a data analyst, engineer, or scientist, you'll be expected to work in ways that support a company’s climate and social impact targets. Understanding how to embed sustainability into your data workflow can set you apart as a valuable contributor to future-fit teams.

Tools and Approaches for Green Data Innovation

Luckily, there are growing numbers of tools and methods available to help professionals embed sustainability into their data work. These include:

Green algorithms : Tools like CodeCarbon track the carbon footprint of machine learning models, helping teams optimise for efficiency.

DataOps for sustainability : Building pipelines that auto-archive stale data and monitor usage can cut waste.

Cloud sustainability dashboards : Platforms like Azure and AWS provide real-time visibility into the energy consumption and carbon impact of your workloads.

Responsible AI toolkits : From IBM’s AI Fairness 360 to Google’s What-If Tool, data scientists can ensure their models are not only efficient but also equitable and explainable.

Training in sustainable data practices is also expanding. Many bootcamps and universities are introducing modules on green IT, ethical data science, and climate tech. Getting certified or skilled in these areas early can give professionals a strong edge in a job market that’s increasingly values-led.

Building a Data Career with Purpose

As industries become more digitised and more conscious of their environmental footprint, sustainable data practices are no longer niche, they’re the new standard. For aspiring data professionals, this is a rare opportunity to be at the intersection of two major transformations: digital and environmental.

The next generation of data leaders will not just be brilliant with numbers and models, they’ll also be trusted stewards of resources, advocates for ethical use, and innovators of low-impact, high-value solutions.

If you’re starting out in the data world, ask yourself: How can I build with purpose? How can I help turn today’s data growth into tomorrow’s green advantage?

The answers will shape not just your career, but the future of the digital economy.

Ready to grow your career in data while making a real impact on the future?

The Nurture Programme works with leading universities in the field of data, AI & IoT. If you want to benefit from unencumbered minds and the latest in academic research the Nurture Programme could be your salvation! Connect with Marie May to learn more.

Data Consulting that takes Accountability for Deliverables & Builds High-Impact Teams

nick.deveney@edensmith.co.uk (Nick Deveney) — Wed, 04 Jun 2025 14:55:35 GMT

The journey from a successful data pilot to a fully scaled solution is often more complex than it seems. Many organisations find early success with proof-of-concept projects, only to stumble when trying to implement those learnings at scale. This is where experienced data consultants become invaluable, not just for their technical knowledge, but for their ability to build and structure high-impact teams that can deliver sustained, enterprise-level transformation.

Why Data Pilots Succeed but Scale Fails

Data pilots are designed for speed, agility, and experimentation. They’re usually run by a small, cross-functional group of internal stakeholders and external experts, often in a silo. Success is typically measured by early performance indicators, faster insights, better customer segmentation, more accurate forecasts.

But when it’s time to expand the project across departments or regions, the cracks often show. Scaling introduces new variables: data governance, infrastructure readiness, change management, security compliance, and, most critically, talent gaps. Without a team model that supports collaboration, knowledge transfer, repeatability, and resilience, even the most promising pilot can stall.

Our consultants play a key role in navigating this phase. When we embark on a project our teams embed into your teams seamlessly, creating a collaborative workspace that upskills your teams and increases their impact. They don’t just help validate a model; they help design the organisational structures and team capabilities that will carry that model through operationalisation. This can include identifying internal champions, outlining skills matrices, and advising on org design to future-proof the solution.

Building the Right Team for Scale

Success in data scaling isn’t about hiring more people; it’s about hiring the right people and empowering them within the right frameworks. High-impact teams are built on complementary skill sets, clear roles, and a shared understanding of business goals.

A good consultancy works with leadership to identify which roles are critical for scale. Our unique business model means that not only do our partners have access to niche data consulting experts but leading recruiters with specialist knowledge in the data space. Taking a skill-based approach, our staffing team can help identify hidden talent with a future-focussed approach to hiring today.

Equally important is assessing whether to augment existing teams, hire new talent, or to bring in contractors or partner firms. Boutique consultancies and their people provide objective insight here, often leveraging industry benchmarks and workforce analytics to guide decisions.

Beyond technical staffing, consultants also advise on cultural alignment. Are teams empowered to experiment? Are KPIs connected to data outcomes? Are stakeholders trained in data literacy? These soft elements can make or break a scale-up effort.

From Advisory to Action: Creating Repeatable Impact

The final step in scaling data initiatives is embedding a feedback loop between implementation and outcomes. Consultants help teams move from reactive decision-making to proactive insight generation by designing systems of continuous learning and optimisation.

This often includes:

Establishing data product roadmaps
Setting up cross-functional steering committees
Building internal training academies or Centres of Excellence
Implementing agile delivery practices tailored for data work

Importantly, consultants don't just "hand over" a solution, they build internal capability to own it. That means structuring teams to be self-sufficient, automating governance where possible, and instilling a mindset of long-term value creation.

When scaling is done well, it’s not just about rolling out more dashboards or pipelines. It’s about enabling your people to ask better questions, make faster decisions, and drive measurable outcomes.

Looking to Scale Your Data Strategy?

At Eden Smith, our boutique data consulting division specialises in building agile, high-impact data teams tailored to the unique challenges of scaling. Whether you're transitioning from pilot to enterprise rollout or rethinking your team structure, we can help you future-proof your data journey.

Whether you're expanding a promising pilot or rethinking your team structure for enterprise impact, our expert consultants Nicholas Deveney and Lauren Johns are here to help.

The Future of Work in Renewable Energy

Thu, 29 May 2025 15:36:32 GMT

The Future of Work in Renewable Energy

The global shift toward renewable energy isn’t just transforming how we power our homes and cities, it’s reshaping the very nature of work. As governments, businesses, and communities rally around net-zero targets and climate resilience, renewable energy is fast becoming one of the most dynamic job creators in the world.

From solar and wind to hydrogen and energy storage, demand for skilled talent is rising sharply. But beyond the growth, the sector is also redefining what it means to build a career: purpose-driven, high-tech, and future-focused.

Assessment of the clean energy skills challenge - GOV.UK

According to the LinkedIn ‘Jobs on the Rise 2025’ report, Energy Manager is the 8th fastest growing job title. Opportunities are abundant for someone transitioning from the Oil & Gas sector or building a career in clean energy and energy reduction. ( Job trends 2025: The 25 fastest-growing jobs in the UK_)

A Surge in Demand

According to the International Renewable Energy Agency (IRENA), renewable energy jobs reached 13.7 million globally in 2022 and are expected to surpass 38 million by 2030 . Solar and wind lead the way, but growth is accelerating in energy efficiency, green hydrogen, smart grids, and carbon capture.

Key in-demand roles include:

Wind turbine technicians and solar PV installers
Energy storage and battery engineers
Grid modernization specialists
Sustainability data analysts
Policy and regulatory experts

This isn’t just manual or engineering work either. There’s a rising need for digital expertise, data science, AI, cloud computing, to support optimisation and innovation across projects. The intersection of technology and sustainability is where future jobs are being created.

Skills for the Green Economy

As the sector evolves, so too do the skills required to thrive. It’s no longer enough to have traditional energy experience. Workers must embrace a blend of technical fluency, environmental literacy, and agile thinking.

Some of the most sought-after competencies include:

Digital skills : Data analytics, simulation tools, IoT platforms
Project management : Especially for large-scale infrastructure projects
Cross-functional communication : Collaborating across tech, policy, and business units
Adaptability : Staying current with fast-changing regulation and technologies
Purpose-driven mindset : Passion for sustainability is a differentiator

To meet demand, education providers are pivoting too. We're seeing an explosion of upskilling programs, from micro-credentials in green hydrogen to full Master’s degrees in climate tech.

Building Inclusive, Purpose-Driven Teams

Attracting top talent to the sector isn’t just about competitive salaries, it’s about culture, purpose, and progression.

The next generation of renewable energy professionals wants to see real impact. They value:

Clear career pathways
Flexible and hybrid work models
Diversity and inclusion in leadership
Evidence of environmental and social governance (ESG) in action

Companies investing in employer branding, professional development, and authentic sustainability messaging are pulling ahead in the talent race. Equally important is building partnerships with education and workforce organizations to create robust talent pipelines.

Future-Proofing Your Career or Organisation

Whether you're a job seeker looking to pivot into a meaningful career or a company racing to scale clean energy solutions, the message is clear: renewable energy is no longer a niche sector, it’s a cornerstone of the global economy.

For individuals now is the time to explore green job opportunities, invest in upskilling, and align your career with the planet’s most urgent priorities.

For employers: success in renewable energy will be defined not just by the technologies you build, but the teams you empower.

Ready to Power the Future of Work?

At Eden Smith, we help connect exceptional talent with purpose-driven opportunities in renewable energy and sustainable innovation. Whether you’re hiring, reskilling, or transitioning into the sector, we’re here to help.

�� Get in touch with Jake Carrington to start building your future-forward workforce.

How to Turn Business Challenges into Data Opportunities

Wed, 21 May 2025 13:42:42 GMT

How to turn Business Challenges into Data Opportunities

As we all know, every business has its own set of challenges. This is absolutely nothing new to any of us. We know that today’s business environment is defined by so many different factors, such as change, complexity, uncertainty in the markets, the list goes on. From shifting consumer expectations to disrupted supply chains, companies are navigating constant turbulence. But within every challenge lies an opportunity, and increasingly, those opportunities are unlocked through data.

Enter the modern data leader. More than just a steward of infrastructure, today’s Senior Leader in Data has to be a business strategist capable of turning pain points into company performance gains. Their mission? To unify fragmented data, uncover insights, and translate complexity into clarity, creating measurable value in the process.

In an era where decision-making needs to be fast, accurate, and forward-looking, data leadership isn’t a technical function, it’s a transformative force . And the most effective data leaders are the ones who can look at a business problem and say, “Where’s the signal in the noise?”

Tackling Customer Retention with Data

Consider a familiar challenge: customer churn. A B2C enterprise is seeing declining retention rates, but the cause isn’t immediately clear. Marketing blames pricing. Sales points to competition. Product teams cite feature gaps. The truth? It’s a mix of all three.

This is where data leadership shines. A strong data leader brings cross-functional teams together and designs a unified data model that combines CRM, customer feedback, usage patterns, and churn rates. By applying predictive analytics and customer segmentation, they can identify high-risk segments and create personalised interventions, turning a retention crisis into a loyalty-building strategy .

The proof is in the pudding. Telcos, retailers, and SaaS companies alike have leveraged data to reduce churn by up to 30%, simply by letting insight guide customer engagement. Surely this should be seen as a huge success to any business.

Optimising Supply Chains with Predictive Insights

Supply chain inefficiencies are another business-wide issue. Delays, rising logistics costs, and inventory mismatches all lead to missed revenue. Traditional solutions often treat symptoms, adding staff, increasing safety stock, but fail to address the root cause: lack of data visibility .

Any strong data leader worth their salt will step in with a broader lens. They may integrate internal ERP systems with external data like weather patterns, supplier performance, and transport disruptions. By applying machine learning models, they would have the ability to forecast demand, flag potential bottlenecks, and automate reordering decisions.

The result? One global manufacturer cut lead times by 18% and improved inventory accuracy by 25% by introducing a data-driven logistics dashboard led by the CDO’s office. This is how business pain becomes an operational advantage.

Cross-Functional Collaboration: The Heart of Strategic Data Leadership

The most impactful data initiatives don’t happen in silos. Solving challenges like employee productivity, fraud detection, or ESG compliance requires coordination across departments, from finance to operations to HR. That’s why modern data leaders must also be collaborators, translators, top communicators and just as importantly change agents! The saying ‘one must wear many hats’ seems to fit this idea.

Take operational efficiency. A large insurance firm struggled with slow claims processing due to fragmented systems across teams. Their CDO orchestrated a task force combining IT, legal, customer service, and compliance. Together, they mapped the claims process, identified redundancies, and introduced AI-driven triage tools. The result was a 40% reduction in processing time.

It’s not about owning the solution, it's about enabling the right solution , at the right time, with the right data and ensuring you have the right team in place!

Turning Insight Into Impact

Every organisation has untapped potential buried in its data. The difference between surviving and thriving often comes down to whether that data is used reactively or strategically .

When data leaders align with business priorities, build bridges between teams, and foster a culture of insight-driven action, real transformation happens. They become not just problem solvers, but opportunity creators , helping the business move faster, smarter, and more confidently toward the future.

The next business breakthrough may not come from a new product or market. It might come from seeing what’s already in your data and having the leadership in place to act on it.

Ready to Turn Data into a Strategic Advantage?

At Eden Smith, we work with forward-thinking organisations to identify and place the data leaders who can drive meaningful change. Whether you're looking to build out your leadership team or explore how data strategy can solve your biggest challenges, let's start the conversation.

�� Connect with Jane Smith today to learn more about our executive search and data consulting services.

The Power of Mentorship in Data Careers

Mon, 19 May 2025 09:29:42 GMT

The Power of Mentorship in Data Careers

Mentorship is one of the most powerful tools in shaping successful careers, especially in the fast-evolving world of data. For early-career professionals navigating complex tools, technical skills, and industry expectations, a mentor can offer invaluable guidance, encouragement, and direction.

A great mentor can help bridge the gap between academic knowledge and practical application. They provide clarity on career goals, help refine your data storytelling abilities and offer insight into the dynamics of data teams and industry trends. Whether you're learning how to craft a compelling portfolio or make sense of your first data project at work, a mentor’s perspective can be the difference between confusion and confidence.

Finding the Right Mentor in the Data Industry

So how do you find a mentor that aligns with your career goals? Start by identifying individuals who are doing what you aspire to do—whether they’re analysts, data scientists, data engineers, or AI strategists. LinkedIn, professional forums, data communities like DataTalks. Club, and meetups (virtual or in-person) are all excellent places to begin your search.

When reaching out, be respectful of their time. A concise message explaining who you are, why you admire their work, and what kind of guidance you’re seeking can go a long way. You might say: "I’ve been following your work on responsible AI and found your recent talk really inspiring. I'm an early-career data analyst eager to improve my model evaluation skills. Would you be open to a brief chat or mentorship?"

Once the relationship is established, make it a two-way street. Be prepared with thoughtful questions, share your progress, and offer your own insights where relevant. Mentorship works best when it feels collaborative rather than one-sided.

https://www.forbes.com/councils/forbestechcouncil/2023/12/06/why-mentorship-is-crucial-for-data-science-and-ai-professionals/?utm

Becoming a Mentor and Building a Culture of Knowledge-Sharing

As you progress in your own career, there comes a time when you can and should give back. Becoming a mentor isn’t just about sharing knowledge; it’s about uplifting others and strengthening the data community.

You don’t need to be a senior executive to offer mentorship. If you’ve been in the industry for a couple of years, there’s already someone a few steps behind you who could benefit from your experience. Sharing your journey through blog posts, webinars, or one-on-one sessions creates ripples of learning that extend far beyond one individual.

Mentorship also benefits the mentor. Teaching reinforces your own understanding and encourages empathy, communication, and leadership skills qualities that are invaluable for long-term career success.

Ultimately, mentorship is a cycle. You seek guidance, grow from it, and then pass it on. That’s how we build not just careers, but a thriving, supportive culture in data and AI.

Contact Marie May At Eden Smith to chat more about our Nurture Programme and how it could help you.

Using Data to Make Smarter Environmental Decisions

Wed, 14 May 2025 21:57:25 GMT

Using Data to Make Smarter Environmental Decisions

In the era of climate urgency and environmental accountability, the convergence of big data and sustainability is creating new opportunities for impact. With the vast volume of information available today, from energy usage metrics and logistics data to real-time climate patterns, data is no longer just a business intelligence tool. It's a critical asset for making smarter, greener decisions that drive both environmental and economic value.

Organisations that effectively harness data can uncover insights that inform more sustainable operations across the board. Whether it's cutting waste in production processes, forecasting renewable energy needs, or reducing emissions in global supply chains, data can reveal what’s working, what’s not, and where the biggest opportunities for change lie.

From Carbon Footprints to Greener Logistics: Real-World Data in Action

Data is already transforming how companies address their sustainability goals. In supply chains, for instance, businesses are using advanced analytics to map out routes that minimise fuel consumption and optimise loads, dramatically cutting transport-related emissions. Retail giants like Walmart and Amazon have invested heavily in data platforms to streamline logistics and reduce environmental impact.

In manufacturing, predictive maintenance powered by sensor data helps reduce energy waste and prevent equipment failures, both of which have significant environmental benefits. Even in agriculture, data analytics is enabling precision farming, where data on weather, soil, and crop health leads to more efficient use of water, fertilisers, and pesticides.

Cities, too, are becoming smarter and greener. From managing traffic patterns to optimising energy grids and improving air quality, urban planners are increasingly relying on data to inform sustainable development policies.

For many organisations, they are motivated by compliance to ensure their climate data is accurate and up to date. Whether a business is a large corporate or a part of their supply chain, energy audits are happening, and it is vitally important to stay compliant. Companies are using data tools to ensure they are not left vulnerable.

The Tools Behind Smarter Sustainability Strategies

To drive these insights, organisations are turning to a suite of data tools and platforms designed for sustainability. Technologies like IoT (Internet of Things) devices collect real-time environmental data, while cloud-based analytics platforms process and visualise the findings. Tools such as Microsoft Cloud for Sustainability, Google Earth Engine, and IBM’s Environmental Intelligence Suite are empowering businesses to track, report, and act on sustainability metrics.

Data visualisation tools like Tableau and Power BI make it easier to communicate sustainability insights with stakeholders, driving better decision-making and accountability. Additionally, ESG data platforms such as EcoVadis and 51toCarbonZero help organisations measure and benchmark their sustainability performance against global standards.

The intersection of data and sustainability is more than a trend; it’s a fundamental shift in how organisations approach environmental responsibility. By embedding data into the heart of sustainability strategies, companies can achieve measurable impact, drive innovation, and build a more resilient, responsible future. Top 10 Big Data Platforms and Tools

Ready to leverage big data for a greener future? ��

Discover how your organisation can use data-driven insights to make smarter environmental decisions and build a more sustainable future. Explore real-world examples and powerful tools that are already driving change in sustainability practices.

Take action today – Get in touch with Jake Carrington at Eden Smith to start your journey toward integrating big data into your sustainability strategy!

Exec Summary: What's Next for the Role of the CDO?

christa.swain@edensmith.co.uk (Christa Swain) — Mon, 12 May 2025 09:01:54 GMT

The Evolving Role of Data Leaders | Insights from the Data Leaders Executive Lounge | 8th May

At a recent Data Leaders Executive Lounge , hosted by Eden Smith, senior data professionals and thought leaders came together to explore one of the most pressing questions in the industry: What is the role of a CDO and how can data leaders evolve to deliver tangible business value?

As the role of the Chief Data Officer (CDO) and senior data leaders continues to transform, the conversation centred around moving beyond governance and compliance, and instead focusing on business transformation, operational efficiency, and revenue impact.

Key Takeaways from the Discussion

1. From Governance to Growth: The New Mandate for Data Leaders

Traditional CDO roles have often been limited to compliance and data governance. However, there’s a growing demand for data leaders to take on more commercially focused, transformative positions. Attendees agreed that the modern data leader must align data strategy with overarching business goals - particularly where growth, customer experience, and innovation are concerned.

2. Creating Measurable Business Value with Data

Participants emphasized that it’s no longer enough to “manage data.” Today’s data leaders must demonstrate clear ROI - whether through improved decision-making, product innovation, or cost savings. This shift demands stronger communication with the board and senior stakeholders, as well as a deep understanding of the organisation's commercial drivers.

3. Defining the Role: The Case for a Standard Framework

With high turnover in data leadership roles and ongoing ambiguity around responsibilities, there was consensus on the need for clearer, standardised job descriptions. A well-defined data leadership framework could help organisations hire more effectively and enable data professionals to deliver greater impact.

4. Building Influence: Networking, Head Hunters, and Career Growth

Speakers highlighted the importance of engaging with head hunters, developing a compelling personal narrative, and actively participating in the wider data community. Relationships and visibility are essential for data leaders seeking to move into strategic, board-facing roles.

5. Future-Proofing the Profession

The group also discussed the pressures of the current economic climate, and how this affects board expectations. There was a strong call for data leaders to reposition themselves not as a cost centre, but as strategic enablers of value and innovation. This includes staying current on AI developments and being able to speak the language of business transformation.

Final Thoughts

The Data Leaders Executive Lounge continues to serve as a vital space for open, strategic dialogue among senior data professionals. This event underscored the urgency of redefining the CDO role - not just to stay relevant, but to drive real, sustainable business outcomes.

As the data leadership function matures, one thing is clear: those who can bridge the gap between data and business will shape the future of enterprise success.

The Evolution of the Data Leader

Thu, 08 May 2025 08:15:17 GMT

The Rise of the Modern Data Leaders

Over the past decade, the role of the data leader has undergone a profound transformation. Once viewed primarily as technical experts or back-office analysts, today’s data leaders are now strategic partners at the core of business decision-making. As data has become a foundational asset for innovation, competitiveness, and operational efficiency, data leaders have found themselves in increasingly influential roles.

Where data leaders were once tasked primarily with reporting and managing data infrastructure, the modern expectation is to lead enterprise-wide data strategies. They are responsible not only for data quality and governance but also for driving business value through analytics, machine learning, and now, generative AI. The title has shifted from “Head of BI” or “Data Analyst Lead” to Chief Data Officer (CDO), Chief Analytics Officer, or even Chief Data and AI Officer, reflecting a broadened scope and elevated status.

According to Gartner, 81% of respondents agree that the role of their senior data and analytics leader has evolved over the past year, with 82% noting increased influence on business decisions, indicating a significant shift in the strategic importance of these roles.

Navigating New Challenges: AI, Ethics, and Data Regulation

As the importance of data has grown, so too have the challenges facing data leaders. Today’s leaders must grapple with the rise of big data, the ubiquity of cloud computing, and the rapid evolution of artificial intelligence. These shifts demand a deeper understanding of technology and an ability to translate complexity into actionable business insights.

Data privacy laws like GDPR, CCPA, and evolving global regulations now require that data leaders embed compliance into every initiative. Simultaneously, the ethical implications of AI, ranging from bias in algorithms to the responsible use of generative models, have added a new dimension to leadership. Data leaders are increasingly expected to guide their organisations not just through technical implementations, but also through complex ethical and societal considerations.

The Future Skill Set: Strategy, Storytelling, and Systems Thinking

Looking ahead, the data leaders of tomorrow will need a versatile and evolving skill set. It’s no longer enough to be a technical expert. The future requires hybrid thinkers, professionals who understand cloud architecture and AI models, but who can also influence business strategy, tell compelling data stories, and build collaborative, cross-functional teams.

Strategic vision will be key. Leaders must align data initiatives with corporate goals, prioritise high-impact projects, and champion a data culture across the organisation. Soft skills, particularly communication and storytelling, will be equally important for securing buy-in and driving change.

As data continues to permeate every part of the business, the next generation of data leaders will be systems thinkers, capable of connecting dots across departments, anticipating downstream effects, and steering organisations toward long-term success. In this evolving landscape, leadership in data is not just about managing information, it’s about shaping the future of the business itself.

The Evolution of Chief Data Officers in the Digital Age

Ready to Lead the Future of Data?

Whether you're stepping into a leadership role or evolving your current one, now is the time to invest in the skills and mindset that will define the next generation of data leadership.

Connect with Jane Smith at Eden Smith to learn how we support data leaders in navigating this transformation through community, training, and strategic insights. Let’s shape the future of data together.

Unlock Innovation with Our Nurture Programme

Wed, 07 May 2025 14:45:00 GMT

Our award-winning educational programme is a unique opportunity for businesses to leverage fresh talent in Data Science, AI, and IoT

Are you looking for innovative solutions to your business challenges while also contributing to the development of the next generation of data scientists, AI specialists, and IoT innovators? Our Nurture Programme offers businesses a unique opportunity to collaborate with bright MSc students from universities, giving you access to fresh talent, cutting-edge research, and hands-on expertise – all at a low financial cost.

The Nurture Programme is a 12-week initiative designed to provide MSc students in Data Science, Artificial Intelligence (AI), and the Internet of Things (IoT) with real-world, hands-on experience . These students work on live projects provided by participating businesses, applying their technical expertise to real business problems.

By participating in the Nurture Programme , your business gains direct access to talented individuals eager to apply their academic knowledge to real-world challenges . Here’s how your organisation can benefit:

Innovative Solutions at Low Cost

Get fresh perspectives and cutting-edge solutions to your data, AI, and IoT challenges. Our students work on real business problems, offering creative and research-backed approaches, without the financial commitment of hiring a consultant.

Tackle Business Challenges with Data-Driven Insights

Struggling with data analysis, automation, or predictive modelling? Our MSc students bring their expertise in AI, machine learning, big data, and IoT to help your business make smarter, data-driven decisions .

Test New Ideas with Minimal Risk

Have an idea for an AI-driven tool, a data analytics model, or an IoT application but don’t have the time or resources to test it? Let our students develop and prototype innovative solutions without impacting your operational resources .

Access to Emerging Talent

This programme allows you to identify and engage with top-tier future professionals . If you’re looking for skilled graduates, this is an ideal way to assess potential candidates before they enter the job market.

Strengthen Your Industry-Academic Links

Build strong partnerships with universities and shape the next generation of tech professionals. Your involvement in the Nurture Programme positions your business as a leader in fostering talent and innovation.

Third Space Learning

Now is the time to take advantage of this low-cost, high-value opportunity to solve pressing business problems while supporting the next generation of data and AI professionals. There is a £500 Participation Fee per Student. Should the company hire the student after the project has been completed, they will be charged a flat fee of £4000

Needs some inspiration, check out just a few of our case studies:

Predicting Customer Satisfaction with Onward Homes

Chatbot - Customer Focussed with National Highways

Evaluating Complaints with Notting Hill Genesis

Forecasting Service Demand with Cheshire West & Chester

Determining the Value with Stagecoach

Multiple Projects with Haringey Council

Don’t miss out on the chance to gain cutting-edge insights, solve complex problems, and connect with emerging talent.

Define Your Project – We work with you to scope a suitable project that aligns with your business needs.
Review and Shortlist Candidates – You will receive access to a dedicated client portal where you can view student profiles, recorded interviews, and completed application forms based on your project scope. This allows you to review and create a shortlist of students you’d like to interview for your project.
Live Project – After conducting the interviews, you select your preferred student/s, and they are assigned to your project, working under the guidance of both academic supervisors and your internal team.
12 Weeks of Innovation – Over the course of 12 weeks, students conduct research, develop solutions, and deliver actionable insights.
Final Presentation & Business Impact – The students present their findings, prototypes, or models, ensuring that you walk away with real, applicable results.

Frequently Asked Questions

QUESTION: What Happens with Our Data?

ANSWER:

Students work under NDA
Data is anonymized and tokenized
Students work on secure systems
Results can be delivered anonymously, and you keep the IP

QUESTION: How much does it cost to participate in the Nurture Programme?

ANSWER: There is a £500 Participation Fee per Student. Should the company hire the student after the project has been completed, they will be charged a flat fee of £400 0

QUESTION: How much time do we need to invest into the students?

ANSWER: Once onboarded, organisations see the best results by setting up regular weekly check-ins to mentor the student and review their progress. Some clients also use the Nurture Programme as an opportunity to upskill internal teams by involving them in project management and student support.

QUESTION: Do we need to pay the students?

ANSWER: While payment is not mandatory, it is entirely at your discretion. Many of our clients choose to offer a contribution towards students' expenses or time as a gesture of goodwill and to encourage engagement.

Putting the Human Ahead of AI

christa.swain@edensmith.co.uk (Christa Swain) — Wed, 16 Apr 2025 17:01:06 GMT

This article was originally published in the HRDirector

The Urgent Need for Workforce Upskilling

AI enhances efficiency, but humans drive the strategy. While artificial intelligence can process vast amounts of data in seconds, it is human insight and strategic thinking that transform that data into meaningful decisions. The future isn’t just about machines... it’s about how we, as humans, leverage those machines to create smarter, more effective outcomes.

HR leaders are at the forefront of this transformation. As AI, data, and digital technologies reshape industries, the real challenge isn’t just integrating these tools - it’s equipping people with the skills to collaborate effectively with them. Future-ready organisations understand that investing in human potential is just as critical as investing in technology. Businesses that fail to embrace the efficiencies of transformation risk falling behind, while transformation itself will falter without a mindset that embraces change and fosters adaptability, collaboration, and innovation. By cultivating a culture of continuous learning, organisations can drive successful transformation while enhancing engagement and retention.

The Human Advantage in an AI-Driven World

AI is revolutionising the way we work, automating tasks, streamlining processes, and providing insights at an unprecedented speed. Yet even the most advanced AI lacks the creativity, emotional intelligence, and strategic foresight that only humans possess.

Human skills such as critical thinking, empathy, and communication differentiate successful teams from those that simply follow the data. AI can analyse information, but it takes human judgement to contextualise that data and make decisions that align with business goals and ethical considerations.

"AI accelerates data processing, but it's human creativity and strategic thinking that transform information into actionable decisions."

Phil Yeoman, CDO

The Rise of Power Skills

Technical expertise alone is no longer sufficient for achieving organisational success. The emphasis has shifted towards "power skills", formerly known as soft skills, which are pivotal in fostering effective collaboration and driving business growth.

The Evolution from Soft Skills to Power Skills

Historically, attributes such as communication, empathy, and adaptability were labelled as "soft skills", often perceived as secondary to technical abilities. However, recent insights underscore their critical importance, leading to their rebranding as "power skills". This shift highlights their role in empowering individuals and teams to excel in collaborative environments.

A study by Pearson in 2022 revealed that the top five most in-demand skills across major job markets are human-centric, including collaboration and customer focus. This trend is projected to continue, especially with the growth in AI, emphasising the sustained value of power skills in the workforce.

Cultivating Human Skills in the Workplace

Recognising the importance of power skills, businesses are investing in their development to enhance collaboration and overall performance.

Gamified learning and team simulations offer experiential learning that increases engagement, retention, and application. Who does not like learning through play?
Mentorship programmes have emerged as effective tools in skill-building. Research highlighted by Reuters indicates that mentored individuals earn 15% more than their non-mentored peers, attributing this to improved confidence and teamwork abilities.
The rise of "fractional twinning", where part-time executives share roles, underscores the demand for adaptability and emotional intelligence in leadership positions. This approach allows businesses to access specialised skills flexibly, fostering a culture of continuous learning and collaboration.

In an era where technological advancements are reshaping industries, power skills remain the cornerstone of effective collaboration and business success. By prioritising the development of these skills, organisations can cultivate resilient, innovative, and cohesive teams poised to navigate the complexities of the modern workplace.

Why We Learn Better Together

Learning is often seen as a solitary pursuit, but research consistently shows that learning in social contexts significantly enhances understanding, retention, and application of knowledge. Whether through collaboration, simulation, discussion, or observation, social learning taps into our innate human tendencies to connect and share, making it a powerful tool for professional development.

The Science Behind Social Learning

Social learning is grounded in neuroscience and psychology. Studies indicate that when we learn in groups, our brains are more engaged, and we process information more deeply. Consider these key insights:

Collaborative Learning Increases Retention: A study published in Psychological Science found that people who learn through discussion, group activities, or simulations retain up to 50% more information than those who study alone.
The Role of Mirror Neurons: When we observe others performing tasks, our brain’s mirror neurons activate, helping us understand and replicate their actions more effectively.
Boosting Cognitive Load Management: Group learning allows individuals to divide complex tasks, reducing cognitive overload and enhancing problem-solving efficiency.

Social learning does not just help us remember facts, it helps us apply what we have learned in practical, real-world contexts, making it an essential approach for workforce upskilling.

How Social Learning Shapes Professional Success

In the workplace, social learning is a cornerstone of effective training and collaboration. Consider these statistics:

70:20:10 Learning Model: Research shows that 70% of workplace learning happens through on-the-job experiences, 20% through social interactions, and only 10% through formal training (Centre for Creative Leadership).
Faster Skill Acquisition: Teams that engage in collaborative learning complete training programmes 30% faster than those relying on individual learning (Harvard Business Review).
Knowledge Sharing Boosts Innovation: Companies that encourage peer-to-peer learning see a 25% increase in innovation due to the diverse perspectives shared in group settings (McKinsey & Company).

By fostering a collaborative environment, organisations can upskill employees more efficiently while creating a culture of continuous improvement.

Harnessing the Benefits of Social Learning

Whether in classrooms or corporate offices, organisations can implement social learning strategies to maximise impact. Here is how:

Encourage Collaboration: Use group projects, team brainstorming sessions, and peer feedback to foster interactive learning.
Leverage Technology: Digital tools enable seamless collaboration, and simulation activities offer immersive, experiential learning that enhances retention.
Create Safe Learning Spaces: Cultivate an environment where learners feel comfortable sharing ideas, asking questions, and learning from mistakes.

By integrating social learning principles into your strategy, you can unlock higher engagement, deeper understanding, and better outcomes.

The Takeaway

Humans are social creatures, and our learning thrives when it is collaborative. Whether we are working on a group project, sharing insights with colleagues, or discussing ideas in a boardroom, social learning makes the process more effective, enjoyable, and impactful.

“If you want to go fast, go alone. If you want to go far, go together.”

The future belongs to those who lead it. By embracing the power of social learning and prioritising human skills alongside AI advancements, organisations can ensure they stay ahead and are future fit.

Embracing multigenerational teams

Wed, 16 Apr 2025 16:42:00 GMT

Many organisations and HR leaders are finding themselves in the position of having access to fabulous talent made up of not only diverse pools of candidates, but also a broad mix of the generations.

Think Baby Boomers, Gen X, Millennials and Gen Z. There’s even a small percentage of the Silent Generation – those born between 1928-1945 - a generation who make up a portion of the chair, board member and advisory roles, offering a wealth of experience and knowledge.

The demographic of our workforce is experiencing notable shifts. This is influenced by a combination of demographic, social, and economic changes. Understanding these shifts is crucial for organisations aiming to adapt effectively when wanting to stay ahead of the curve and out-perform their competitors.

The factors behind the increasingly multigenerational workforce

People are working longer
Better health and changing financial needs mean older generations are staying in work for longer.
Later retirement ages
Retirement ages are rising, so older and younger employees are sharing more time in the workplace.
Younger generations entering earlier
Gen Z and Millennials are joining the workforce earlier with strong digital skills.
Evolving career paths
People are changing careers or returning to work at different life stages, increasing generational mix.
Remote and flexible working
Flexible work options allow more people, regardless of age, to stay in or return to employment.

As of the second quarter of 2024, Gen Z workers were almost a fifth (18%) of the workforce, with Baby Boomers comprising 15%. Millennials made up the largest share of the workforce (36%), while the Silent Generation has the smallest share (1%).

The ‘why’ behind embracing Multigenerational Teams

Managing a multigenerational workforce presents both challenges and opportunities. Differences in communication styles can cause misunderstandings and conflict whilst aligning traditional work habits against the modern tech-savvy approach are just two challenges to consider.

However, when people of different age groups work together, you get a powerful mix of experience, energy, perspective, and skills that no single generation can offer alone.

Younger team members bring fresh ideas, digital fluency, and a natural instinct for emerging trends. More experienced colleagues contribute deep industry knowledge, strategic thinking, and emotional intelligence. The result? Faster innovation, better decision-making, and a more dynamic, future-ready team.

These teams also mirror the diversity of your customer base. Whether you're selling a product, designing a service, or shaping communications, having people in the room who understand different life stages makes your work more relevant - and your brand more human.

What’s more, multigenerational teams foster a learning culture. Mentoring flows both ways. Seasoned professionals pass on their wisdom, while younger colleagues share new tools and ways of working. Everyone grows, and the business benefits.

Yes, generational differences can bring friction - but they also bring rich opportunities for empathy, adaptability, and stronger collaboration. When managed well, a multigenerational team isn’t a challenge to overcome… it’s your secret weapon!

Attracting Multigenerational Candidates

Hopefully you need no more convincing… just some down to earth advice on how to attract multi-generational candidates and tools to help you do it right.

Attracting a multigenerational workforce involves developing strategies that appeal to individuals across various age groups, recognising their unique values, communication styles, and work preferences.

Flexible work patterns such as hybrid working models. Since the pandemic, we have seen a huge shift in terms of working patterns. Remote or hybrid working is now very common across the workforce. Equally we are seeing employees in all age groups wanting to move back to office-based work settings. Both HR and business leaders are quite rightly expected to implement an overall company policy but by offering flexible options - that accommodate all life stages and responsibilities - you appeal to the cross-section of society.

Provide comprehensive benefits packages to cater to a wide range of needs is a great way of attracting talent across the ages. Different generations will usually prioritise some benefits over others. To ensure inclusivity, it is worth considering a flexible benefits package – or ‘cafeteria plans’ - where employees cherry-pick perks that suit their lifestyles, stage of life or personal preferences.

Utilise diverse recruitment channels, employ various sourcing methods to reach candidates across different generations. Engage in community outreach and attend or sponsor events that attract a diverse age group. This direct engagement can help build relationships with potential candidates. If you can, use specialist recruitment firms who have established networks that will take the hard work out of your search.

Think about your company branding, position yourself for success by showcasing your flexible work patterns, benefits and also share testimonials from employees of different age groups to show how committed you are to an inclusive workplace. Use neutral language and feature images that represent a diverse age range to signal an age-inclusive workplace.

One of the most important factors to think about is in implementing bias-free hiring practices. Everything from how you design your job descriptions to who is on your hiring panel and how you screen is crucial to mitigate bias and ensure inclusivity. A good talent team or recruitment consultant will be able to offer you advise to ensure diverse candidate attraction and a successful hire.

Break down the siloes to maximise team impact

If you haven’t already started to challenge the siloes and encourage departmental and cross-functional collaboration, then what is stopping you?

Here's why it works:

Taps into Diverse Strengths
When you bring different functions and age groups together, you combine institutional knowledge with fresh ideas, technical expertise with emotional intelligence. This leads to better decisions and more creative problem-solving.
Builds Empathy and Reduces Generational Misunderstandings
Cross-functional collaboration gives people a chance to see how others think and work. It breaks down generational assumptions and creates space for appreciation, not frustration.
Speeds Up Learning and Adaptability
Younger employees can learn the "why" behind decisions from more experienced peers, while older team members get up to speed on new tools, platforms, and ways of working. That mutual exchange builds a more agile and future-ready workforce.
Improves Communication Across the Business
Siloed teams often talk past each other. Cross-functional work helps everyone speak the same language and focus on shared outcomes, not isolated goals.
Drives Innovation and Alignment
When departments and generations collaborate, ideas don’t just stay in one lane - they get stress-tested, refined, and accelerated into action. This fuels innovation and aligns everyone behind the bigger picture.

Key Strategies for Successful Management of Multigenerational team s

A multigeneration team won’t magically come together in perfect harmony. You will need to create a culture for success. The good news is that the strategies for successful multigenerational team's mirror what is required to underpin organisational transformation in the age where digital, data and AI are driving the agenda.

Embrace Diverse Communication Styles

Adapt your methods - some prefer emails, others Slack, and some value face-to-face chats. Create an environment where people feel comfortable sharing their perspectives and actively listening.

Focus on Shared Goals, Not Stereotypes

Highlight team objectives and individual strengths instead of generational labels. Create space for SMART collaboration to activate actions and drive project success.

Encourage a Culture of Learning

Pair team members to learn from each other - tech skills, experience, industry knowledge, etc. Mutual mentoring promotes collaboration and help in understanding varying viewpoints, further strengthening the team bond.

Flex Work Styles and Expectations

Offer flexibility in how, when, and where work gets done to suit different life stages and preferences.

Cultivate Psychological Safety

Foster a culture where all voices are respected, and everyone feels safe to speak up. By encouraging dialogue that values individual contribution, teams can bridge generational gaps and leverage diverse experiences effectively.

Leaving You with This…

The modern workplace isn’t just evolving - it’s transforming. Embracing multigenerational teams isn’t about ticking a diversity box; it’s about unlocking a richer, smarter, more resilient way of working.

Each generation brings something vital to the table. From curiosity to wisdom and boldness to balance, when we break down siloes, challenge assumptions, and foster real collaboration, we create something far greater than the sum of its parts.

By cultivating an inclusive environment that values contributions from all generations, businesses can improve employee engagement, satisfaction, and retention. Ultimately this will drive long-term success, sustainability AND attract top talent from across all demographics.

So, if you're looking to future-proof your organisation, start here: value the differences, encourage the dialogue, and build a culture where every generation thrives. The impact? A stronger, more human business that is built for today and ready for tomorrow.

Transforming Culture, Behaviours and Mindsets: Why People-Led Change Matters

Wed, 16 Apr 2025 16:23:43 GMT

The hardest part of transformation is not designing the strategy. It is getting people to live it.

New tools and business models are easy to implement in theory. But real transformation happens when people shift how they think, behave and work together. At Eden Smith, we have seen time and again that cultural change is the difference between short-term improvement and long-term success.

The organisations that thrive are not just structurally agile. They are culturally adaptive.

Culture and Mindset: The Missing Link in Most Transformations

Culture acts as the silent operating system of a business. It shapes how decisions are made, how people respond to challenge and what behaviours are rewarded or suppressed. Mindset sits underneath it all. It is made up of the beliefs and assumptions that shape our everyday actions.

Misaligned mindsets can quietly derail even the best strategic plans. A business might aim for innovation, yet if the culture punishes failure and discourages risk, that ambition will never take root.

Research supports this. McKinsey has found that only 30 percent of transformation success is down to tools and tech. The remaining 70 percent depends on culture and whether people are emotionally and behaviourally bought into change.

Five Core Shifts That Drive Transformation

At Eden Smith, we support organisations to make deep, sustainable shifts in the way their people think and work. These are the five we see as essential:

1. From Compliance to Curiosity

A curious culture fosters continuous learning and better problem solving. Leaders must create psychological safety so that questions, not just answers, are valued.

2. From Control to Empowerment

Transformation flourishes when people are trusted to act. Move away from micromanagement and towards coaching-style leadership.

3. From Fixed to Growth Mindset

Teams that believe in their ability to learn and adapt outperform those that stick to what they know. Growth mindset is foundational to agility and innovation.

4. From Silos to Shared Purpose

Break down functional barriers. When everyone rallies behind a shared mission, collaboration and accountability follow.

5. From Performative to Authentic Leadership

People follow people. Leaders who are open, consistent and values-led set the tone for culture change across the organisation.

Making Change Stick: Eden Smith’s Approach

Culture and mindset do not shift with a single training session. At Eden Smith, we bring together transformative solutions, change programmes and use our people to transfer knowledge and upskill your teams.

Here’s how:

Start With the Top

Transformation must be modelled. We work with senior teams to help them live the behaviours and values they want to see across their organisation.

Build Safe Spaces to Learn

We promote experimentation and intelligent failure. Sustainable cultures grow when people are encouraged to test, reflect and improve.

Embed Change in Everyday Practice

Culture lives in meetings, rituals and language. We help businesses design the daily habits that hardwire new ways of working.

Why Eden Smith?

As a people-first consultancy, Eden Smith is committed to supporting sustainable transformation. We do not just deliver projects. We build capability.

Whether you need to strengthen team cohesion, develop adaptive leadership or embed a culture of learning, we are here to help you make change real.

�� Contact our team to discuss how we can support your transformation

How to Avoid the Pitfalls of a Data Literacy Programme

Tue, 01 Apr 2025 10:34:44 GMT

Would You..?

Would you step into a taxi if the driver had only learned to drive by reading the manual? Or trust a surgeon who picked up their skills from YouTube videos alone?

Probably not.

Yet, when it comes to data literacy, many organisations take a similar approach - believing that a series of online courses and a few training sessions will be enough to transform how employees work with data.

Despite significant investments in data literacy programmes, many organisations struggle to turn the lessons into action. In many cases, employees rush ‘click-through’ online courses, eager to obtain badges that look good on their profiles. Yet these digital credentials rarely translate into meaningful application. Similarly, team members attend workshops and obtain certifications for attendance, but their daily practices remain unchanged.

Why?

Because learning isn’t a one-time event - it’s a continuous process. And without reinforcement, most of what is taught is quickly forgotten.

So, what does it take to truly embed data literacy into an organisation’s culture?

Let’s explore further.

The Common Challenge with Most Data Literacy Approaches

Most data literacy programmes treat learning as an event rather than a continuous process. Often viewed as a one-time initiative, these programmes are typically structured around intensive training events. Initial enthusiasm is common, but the momentum quickly dissipates once employees return to their everyday tasks, leaving little to show in terms of sustained behavioural change.

This pattern aligns with research on knowledge retention. Research shows that individuals forget a significant portion of what they learn within days or weeks. A one-off online course, workshop or webinar, no matter how engaging, rarely translates into long-term skills or improved workplace habits.

The challenge lies not just in transferring knowledge but in ensuring that it becomes embedded in practice, applicable in real-world situations, and sustainable over time. Most data literacy programmes often fall short of addressing these goals, leaving learners disconnected from the material and struggling to retain what they’ve learned.

The Science Behind Effective Learning

Understanding how our brains naturally change and adapt to new information can help us find better ways to learn and remember things. By using techniques that match how our brains work, we can develop habits that stick. These habits not only make it easier to remember what we learn but also help turn that knowledge into regular, meaningful actions over time.

Three key mechanisms are particularly important for developing skills:

Learnings that Stick

One of the biggest challenges in learning is making sure we remember what we've learned after the training is over. Strategies that help combat the challenges of memory retention are micro-learning, spaced repetition and retrieval practice.

Micro-learning, which involves breaking down information into bite-sized, focused sessions, provides an efficient structure for learning, delivering content in easily digestible formats. These shorter, more concentrated learning bursts allow for better engagement and are particularly effective for complex or technical subjects.

To reinforce this, Spaced Repetition schedules revisit learned materials at carefully planned intervals. This method tackles the forgetting curve by strengthening neural pathways through repeated exposure over time, rather than overwhelming.

Complementing this is Retrieval Practice, which actively engages the learning in recalling what has been learnt, for example through quizzes or summarising key points. Adding fun elements like leaderboards, badges, and polls can also make learning more engaging. These strategies not only help us remember information but also make the learning process enjoyable, memorable and easier to apply when needed.

Repeated practice significantly outperforms single-session learning. When we repeat activities, neural pathways strengthen, making skills more automatic and accessible.

Learning by Doing

This approach, also known as experiential or active learning , focuses on direct participation and hands-on activities. The "generation effect" supports this by showing that the act of producing answers improves memory and understanding. Furthermore, physical tasks, like using tools and technology, activate sensory and motor areas of the brain, leading to deeper learning. It builds on foundational knowledge and avoids cognitive overload as complex tasks are introduced only after solid basic knowledge is established.

By actively working with materials in real-world or simulated settings, learners connect the theory to practice. Contextual relevance plays a key role in improving knowledge transfer. Skills learned in isolation from their application context require significant cognitive effort to transfer to real-world situations. Direct involvement in problem-solving with their newly acquired knowledge empowers learners to construct their own understanding rather than passively receiving information.

'Learn by Doing' shifts learning from passive to active.

Learning with Others

Learning with others taps into the natural way humans develop and refine skills - through social interaction . By observing peers, exchanging ideas, and working together, individuals can reinforce their understanding and pick up new techniques in a supportive environment. Activities such as discussions, collaborative problem-solving, and peer feedback create a sense of community that promotes shared learning and long-term improvement.

Key to this approach is the integration of three principles of the AGES Model™*: Attention, Generation, and Emotion. By learning with others, individuals remain more attentive, as the group dynamic reduces distractions. It also supports generation, where new knowledge is actively linked to past experiences, sparking insights. Emotionally, social interactions heighten the learning experience, embedding it deeper into memory by connecting the material with shared feelings and relationships.

Social learning is also about making connections that help deepen memory. Group settings are effective because they encourage participants to form emotional and practical links to what they are learning. Real-time feedback and the opportunity to imitate successful behaviours increase confidence and engagement.

By prioritising collaborative approaches, organisations create a culture where learning becomes a shared responsibility, helping to embed new skills and behaviours across teams. When people learn with and from each other, progress becomes both individual and collective. This works particularly well in with diverse teams across functions or regions to share best practices and generate new insights together.

The Multi-Touch Approach: Building Data Habits™ and Behaviours That Last

The widening gap between technological advancement and organisational adaptation, what's known as Martec's Law, requires a more wholistic approach to creating lasting change.

The usual data literacy programmes tend to fall short as it is not just about one-off learning modules or workshops. It is about building confidence in handling and interpreting data, developing fluency through regular practice and application, changing habits and behaviours to embed data into daily routines, and creating environments where decisions based on data are expected and valued. It's about fundamentally changing how people work, think, and act.

We need to move beyond traditional training methods toward integrated ecosystems that nurture data habits and behaviours through multiple reinforcing channels. This multi-touch approach recognises that lasting change requires consistent reinforcement across various touchpoints in an employee's work experience.

Leadership Alignment and Advocacy

Data literacy initiatives without visible leadership support often become isolated "nice-to-have" programmes rather than strategic priorities. Leaders play a crucial role in articulating a clear vision for data usage that resonates across all levels of the organisation.

Many companies struggle because top managers want quick wins now, not data skills that pay off. This short-term thinking makes it hard to build lasting data habits across the company.

Effective leadership advocacy includes:

Strategic Alignment: Explain exactly how people should use data in their work and why it helps the whole company succeed.
Active Engagement: Leaders should attend data events, talk about data in meetings, and actually use data tools themselves - not just tell others to do it.
Role Modelling : When leaders ask "What does the data show?" before making choices, everyone notices. When they question both the decision and the numbers behind it, it shows they truly value data.

When leaders connect getting better with data to getting ahead in your career, people pay attention. Their visible support turns vague ideas about "data is important" into clear reasons why everyone should care about using data well.

Learning and Talent Strategy

Don’t jump straight into building a training calendar! Start by mapping out what skills your people already have and where they struggle with data. Pay attention to how they feel about using data too - not just their technical know-how.

Instead of one-off workshops, build learning journeys and experiences that grow skills step by step. Mix it up with short videos, hands-on practice, team talks, and real work projects. This helps different people learn in ways that work best for them.

Add a Gaming Element

Add some fun with games and contests. Create data challenges where teams compete to solve real business problems using data. Award points, badges, or small prizes for completing learning modules or applying new skills. A simple leader board can encourage friendly competition and keep people engaged. These game elements tap into our natural love of achievement and recognition, releasing dopamine in the brain to encourage repeat behaviour.

Bring the Data into Conversations

Look for ways to bring data learning into what people already do. Could your team chats include a quick look at key numbers? Could project check-ins have a moment to talk about what the data shows? When learning becomes part of daily work, it doesn't feel like an extra task.

Data Champions

While learning on your own helps, learning with others makes it last. Build groups where people can share problems, celebrate wins, and learn from each other. This support keeps going long after formal training ends.

These groups need champions - people across different teams who help others get better with data. Find these helpers, give them extra training, and let them influence and spread good data habits within their areas of influence. Make sure you reward people who use data well in their decisions, include data skills in job reviews, and show clear paths for growth.

Track the Data

Finally, track what truly matters - are people working differently, not just attending training. Are teams making better choices with data? Are they asking better questions of the data? These signs show your programme is making a real difference in how people work, not just what they know.

Engagement and Communication

Even the best data programmes will fall flat if people aren't excited about it. You need more than just sending out emails to encourage people to get on board.

Multi-channel

Talk to your teams in many ways - not just one. Use team meetings, quick videos, company chat groups, and eye-catching posters. Mix it up to reach everyone, wherever they are.

Purpose

Be clear about why data matters for your company and for each person's job. Show them how better data skills will help make their daily work easier or helping them progress in their role.

Connect Emotionally

Tell stories with data instead of just showing numbers. Show a marketing team how data helps create better campaigns. Show the customer service team how data helps solve problems faster. Make it personal. When Sarah from sales used data to win a big client, share that story! Real examples from co-workers make data feel relatable and useful. When people see "what's in it for me," they care more.

Two-Way

Ask for feedback often and make sure you use it. Quick surveys and open forum discussions can reveal what's working and what's not. When people see you making changes based on their input, they feel ownership of the programme.

When your communication makes data feel helpful, not overwhelming, people will lean in rather than tune out. The goal is to make everyone think, "This could make my workday better," not "This is just another thing to learn."

The Path Forward: From Literacy to Culture

When done right, an impactful data literacy programme becomes part of your company’s DNA. It isn’t just about teaching people data concepts, it’s about changing how people work, think, and engage with data.

As data grows more important for staying competitive, businesses need to help their teams build confidence and courage with data. It goes beyond offering training sessions and instead creating different ways to learn and engage with data.

It's not about filling heads with knowledge but changing how people solve problems. When using data becomes as natural as checking email, it transforms decision-making at every level, from the front desk to the boardroom. By weaving data into daily routines, companies build a culture where data instincts drive more informed, faster choices - making data an effortless part of everyday work life.

With thanks to Rosanne Werner Founder & CEO of XcelerateIQ for sharing her expert view and insight into data literacy.

The Iceberg of AI Adoption and What you Need to Address

Thu, 27 Mar 2025 11:14:43 GMT

The surge and demand for AI adoption is currently in a state of hype. The shiny new exciting product. But before you (or the board) go head-on into buying off-the shelf products that promise the world, ask yourself this…

“Are my data foundations in place to enable this AI advancement… or will the great polar bear (in this case your shiny AI project) sink?!”

The demand for AI adoption is real, the market size in the Artificial Intelligence market is projected to reach US$020 3854 0260bn by the end of the year with an eye watering 92% of companies planning to increase their AI investments over the next 3 years.

The EU AI Act

The European Union’s Artificial Intelligence Act (EU AI Act), which was signed 1 ^st August 2024 and has a 2-year implementation period across the member states, represents a pioneering effort to regulate AI technologies, ensuring they are safe and uphold fundamental rights.

The EU AI Act sets a comprehensive framework for AI regulation, emphasizing a risk-based approach, transparency, and robust governance structures. As AI technologies evolve, the Act is expected to adapt, reflecting ongoing dialogues among policymakers, industry leaders, and civil society.

There have been recent concerns raised regarding potential amendments to the Act that will water down the rules by making certain provisions voluntary, making major tech companies exempt from specific regulations. These debates underscore the dynamic nature of AI governance and the balance between fostering innovation and ensuring safety.

Surge in specialised hiring as AI Adoption takes root

In 2025, the AI and data hiring landscape will see a continued surge in demand for skilled professionals, with a focus on AI leadership, ethical AI development, and specialised roles like Generative AI Engineers and Computer Vision Engineers, alongside a rise in AI-powered automation and data-driven decision-making.

Foundational ecosystem to leverage successful AI adoption

AI adoption across enterprise looks nailed on but are your data foundations in place to enable true accurate models and leverage the true power of AI?

These foundations work synergistically to create a strong ecosystem for successful AI adoption. Here’s how:

When combined, these foundations ensure that the AI ecosystem is reliable, scalable, and adaptable, enabling businesses to extract actionable insights and drive innovation. It’s like building a house – you need a strong foundation before you can construct something stable and impactful.

We at Eden Smith Group get it, we can support via our consulting and staffing services through-out the adoption cycle. Please contact me, Matt Smith , or one of the team for a discussion on how we can support your business goals.

BEFORE you go…

We’ve launched the AI Readiness Survey to uncover where businesses really stand on their AI journey – and how they stack up against industry peers.

This quick, powerful survey evaluates readiness across five critical areas:

Whether or not you’re directly responsible for AI, your input matters. We’re calling on business leaders across all industries to help paint a clear picture of AI maturity in the real world.

How to Build a Winning Data Portfolio

Wed, 26 Mar 2025 14:35:00 GMT

Building a strong data portfolio is essential for showcasing your skills and differentiating yourself from others in the field. A well-curated portfolio demonstrates not only your technical abilities but also your capacity to think critically and solve real-world problems. Whether you’re just starting out in data science or looking to level up your career, creating a portfolio that stands out is key. Here’s a guide on how to build a compelling data portfolio from coursework to real-world projects.

A Foundation for Your Data Portfolio

When building a data portfolio, academic projects often serve as the starting point. These projects provide a solid foundation, as they typically focus on the application of core data concepts and problem-solving methodologies. Academic assignments, such as class projects, homework, or research papers, showcase your understanding of fundamental data science concepts, including statistical analysis, machine learning algorithms, data cleaning, and data visualisation.

Make sure to include projects that reflect a variety of techniques and skills. For example, you could showcase a project where you applied linear regression to predict outcomes, another one where you used clustering to find patterns, and a visualisation project to present insights clearly. It’s important to present these projects in a way that highlights the practical skills you’ve gained, especially if you can draw from your coursework to address real-world problems.

Showcasing Initiative with a Passion Project

While academic projects are important, personal projects demonstrate a deeper level of initiative and passion for data work. Personal projects give you the freedom to choose problems that excite you, whether they relate to your hobbies, social issues, or areas of professional interest. These projects showcase your ability to apply data skills in unstructured settings, reflecting creativity and the ability to think independently.

For instance, you could work on a data analysis project related to sports statistics, social media sentiment analysis, or even financial forecasting using open datasets. When choosing personal projects, try to focus on projects that align with your career goals. If you want to work in healthcare data, for example, work on a project analysing public health data or disease outbreak trends. The more your personal projects align with the industry or role you’re targeting, the more compelling they will be to potential employers.

Highlight Real-World Application of Skills

Real-world projects are the final piece of the puzzle for a strong data portfolio. Internships, freelance work, or any work experience where you’ve applied data analysis to solve real problems should be prominently displayed. These projects show data leaders that you can take theory into practice and deliver results in a professional setting. G one better and add the experience to your CV and professional profiles, such as LinkedIn.

If you’re early in your career and don’t have formal work experience, try to include any freelance or volunteer data work you’ve done for small businesses, non-profits, or local projects. The key here is demonstrating the impact of your work. Showcase how you helped an organisation save costs, increase efficiency, or improve decision-making through data-driven insights. Including measurable results in your portfolio will make a strong case for your ability to add value.

What Data Leaders Look for in a Portfolio

When data leaders review portfolios, they look for a few key elements: technical proficiency, problem-solving ability, communication skills, and a results-oriented approach. They want to see that you not only understand the technical tools and methodologies but also can apply them to real business challenges.

A great portfolio should:

Demonstrate Technical Skills: Show proficiency with programming languages (like Python or R), data wrangling, machine learning, and data visualisation tools.
Solve Real Problems: Your portfolio should highlight how you’ve used data to solve real-world issues. Include projects that reflect how your work helped drive insights, optimise processes, or improve decision-making.
Be Well-Presented: Data leaders also appreciate clarity and well-organised portfolios. They want to easily understand the problem, your approach, and the results.
Show Communication Skills: Clear communication is key. Showcase your ability to explain complex data insights in a digestible and actionable way.

Visualisation and Storytelling

Once you’ve gathered the projects for your portfolio, it’s essential to present them clearly and engagingly. This is where storytelling and data visualisation come into play. A good data portfolio doesn’t just present data, it tells a story.

Make sure your portfolio is structured to guide the viewer through your thought process, methodologies, and conclusions. Here are some tips to ensure your projects stand out:

Create a Clear Narrative: Present each project as a story. Start by defining the problem, explaining how you approached it, and then showcasing the insights you gained. Conclude with the impact of your findings. This will help data leaders see the value you can bring to an organisation.
Use Visualisations to Clarify Insights: Visualisations are key to making your findings accessible and engaging. Use charts, graphs, and other visuals to clearly communicate your results. Make sure your visuals are easy to read and effectively highlight the insights you want to convey.
Keep it Simple: Avoid overcomplicating your presentations. While it's tempting to show off every analysis you did, choose the most meaningful insights and present them in a way that anyone can understand. Remember, data leaders are looking for clarity and insight, not complexity for complexity's sake.

Tailoring Your Portfolio to Your Audience

Lastly, tailoring your portfolio to the specific industry or role you’re targeting is crucial. If you’re applying for a data analyst position, highlight projects where you’ve used data manipulation and statistical analysis to drive insights. If you’re pursuing a data science role, include machine learning projects where you’ve built models and validated results. By focusing on projects that align with the job description, you’ll make your portfolio more relevant and compelling.

Building a Strong Data Portfolio

Building a winning data portfolio is an ongoing process that evolves with your skills and experience. By including academic, personal, and real-world projects (like those undertaken in the Nurture Programme ), you can demonstrate a well-rounded skill set to data leaders. Make sure to focus on the impact of your work, communicate clearly, and present your projects in an engaging and digestible way. A great data portfolio is not just a collection of work, it’s a powerful tool that showcases your ability to think critically, solve problems, and drive results.

Don’t be scared of show casing old projects, it shows your progress and you can also return to them at a future date. This way they can grow and evolve with you and your skills!

What’s the Role of the Data Office in Modern Businesses and How to Leverage It

Wed, 26 Mar 2025 14:14:00 GMT

The data sector has matured significantly in recent years. Organisations now commonly rely on defined blueprints, playbooks, and roadmaps to support Chief Data Officers (CDOs) in developing successful and relevant data strategies. However, as the understanding and application of data continue to evolve, the role of the data office must also adapt to remain effective.

Rethinking the Role of the CDO

As organisations progress in their data maturity, the traditional centralised model of the data office is being challenged. Discussions within the industry, such as What’s Next for the CDO , have questioned the positioning and even the necessity of the CDO role. The shift in organisational behaviour suggests that the CDO should transition from being a centre of excellence to acting as a facilitator. Thus supporting business functions with governance, enablement, and strategic oversight.

A Learning Theory Perspective

The Burch Model of ‘Four Stages of Competence’ provides a useful analogy for understanding this transformation. Businesses are progressing through these stages, moving from unconscious incompetence (not knowing what they don’t know) to unconscious competence (where data proficiency becomes second nature). Our experience across multiple sectors highlights a growing trend where non-data teams are increasingly displaying conscious incompetence or conscious competence . This shift places greater demands on the data function, requiring it to provide structured guidance, governance, and training to prevent a regression into data mismanagement.

The Rise of Data Skills in Non-Data Roles

The demand for data proficiency is no longer limited to dedicated data professionals. Teams in finance, marketing, and other areas are integrating data capabilities into their daily work:

Finance : The emergence of roles such as financial data scientists underscores the sector’s reliance on data-driven decision-making. These professionals analyse vast datasets to identify trends, forecast performance, and assess risks, ultimately driving profitability ( AccountingWEB ).
Marketing : Data analysis skills are already essential for marketing professionals, yet a skills gap persists. A study found that 8.7% of marketing leaders feel underqualified to perform data-driven tasks effectively ( Marketing Week ).
General Workforce : A UK government report estimates that businesses require around 215,000 roles with hard data skills beyond basic IT capabilities, illustrating the broad integration of data competencies ( GOV.UK ).

How the CDO’s Role is Changing

As data responsibilities spread across an organisation, the CDO’s focus is shifting from centralised ownership to enablement, governance, and literacy-building:

Support and Governance : Modern CDOs are responsible for creating data strategies aligned with organisational goals, identifying underlying challenges, and ensuring data is treated as a valuable asset. A key part of this role is fostering a culture where data-driven decision-making is embedded in everyday business practices ( DDaT Capability Framework ).
Capability Building : The CDO now plays a critical role in raising data literacy across all functions. By providing training and governance frameworks, they help non-data teams develop the necessary skills to work confidently with data ( DDaT Capability Framework ).

Data is officially a business asset!

The formal recognition of data as a business asset signifies a transformative shift in how organisations perceive and utilise their information resources. This evolution underscores the necessity for businesses to strategically manage data, akin to traditional assets like capital and property.

Implications for Data Management:

Strategic Asset Management: Viewing data as a strategic asset compels organisations to implement robust data governance frameworks. This approach ensures data quality, security, and accessibility, facilitating informed decision-making and operational efficiency.
Valuation and Investment: Assigning tangible value to data enables companies to assess its contribution to overall worth, guiding investments in data infrastructure and analytics. This valuation process supports informed decisions on resource allocation and potential monetisation strategies.

Implications for Business Operations :

Enhanced Decision-Making: Recognising data as a valuable asset promotes its integration into strategic planning, leading to more accurate market analyses, customer insights, and operational improvements.
Competitive Advantage: Effective data utilisation can differentiate a company within its industry, fostering innovation and personalised customer experiences.
Risk Management: Proper data management mitigates risks associated with data breaches and regulatory non-compliance, safeguarding the organisation's reputation and financial stability.

In short, acknowledging data as a business asset necessitates a strategic approach to its management and utilisation. This will unlock significant value and fostering sustainable growth.

The Organisational Impact of Data Decentralisation

As data capabilities spread throughout the business, organisations must embrace a cultural shift to maximise the value of data:

Empowerment : Employees at all levels need access to data skills to make informed decisions and drive innovation.
Collaboration : A well-structured data function fosters collaboration between data specialists and other departments, ensuring seamless integration of data-driven initiatives.
Compliance : With wider adoption of data usage, strong governance frameworks are essential to ensure regulatory compliance and mitigate risks.

Conclusion

The modern data office is no longer just a centre of excellence. It’s a crucial enabler of business-wide data competency. As data literacy becomes a standard requirement across industries, the CDO must pivot. Leaders should move towards a role that supports, governs, and nurtures a data-driven culture. Organisations that successfully make this transition will be best positioned to harness the full potential of their data assets and maintain a competitive edge in our data-driven economy.

Reflections on Driving Innovation with Jez Clark, CEO

Wed, 26 Mar 2025 13:26:00 GMT

Driving Innovation in Data, AI, and the Future Workforce

In my last quarterly update, I shared some reflections on the opportunities that 2024 has brought us and the challenges and growth that lie ahead in 2025. As we move forward, I want to share how we’re building momentum and strengthening our capabilities to meet these challenges head-on.

2024 Was A Year of Growth, Innovation, and Investment

This past year, our focus was clear:

developing new products
investing in cutting-edge technology
building internal capabilities
prioritising our people

These efforts were all part of laying a strong foundation. And now, in 2025, I believe we are better equipped than ever to support our network of people and businesses with proactive, personalised, and innovative solutions.

The Evolving Data and AI Landscape

The data analytics market continues to grow rapidly, driven by the increasing demand for data-driven decision-making. As AI becomes more embedded in business operations , I believe we’re seeing its most common use case in automation.

But if we want to truly unlock the potential of AI across organisations, the conversation must shift to AI literacy and training .

There’s a growing need for:

corporate training programmes
AI-powered assistants
personalised learning experiences

While some of these solutions are developed in-house and others sourced from external providers, many fall short. In my view, they often lack:

innovation
personalisation
engagement
flexibility and real-world application

A People-First Business

For those who know Eden Smith well, our commitment to people is at the heart of everything we do. We lead with people, both externally and internally, which was recently recognised at the British Data Awards .

Our mission is to:

upskill professionals
provide thought leadership
deliver meaningful consultancy
foster communities of like-minded data & AI experts

Looking Ahead to 2025

As we step into 2025, I have one simple ask:

Continue to support our initiatives , share your experiences, join our communities, and help us grow the next generation of talent.

Let’s Work Together

Want to explore how we can partner to support your data and AI journey ?

Let’s shape the future of data, AI, and the workforce.

Why Green Data Management is the Next Competitive Advantage

Tue, 25 Mar 2025 10:59:00 GMT

In an age of constant digital transformation, data powers everything from customer insights to business innovation. However, the environmental impact of managing vast data systems is becoming harder to ignore. With the rise of sustainability mandates and consumer demand for responsible business practices, green data management is emerging as a critical differentiator. Companies that prioritise environmentally conscious data strategies are not only reducing their carbon footprint but also gaining a competitive edge in a world increasingly focused on sustainability.

The Business Case for Green Data Management

Green data management refers to the practice of reducing the environmental impact of data storage, processing, and transfer. This involves adopting energy-efficient technologies, optimising data usage, and reducing digital waste. As organisations become more data-driven, the energy consumption of data centres and cloud services continues to surge. According to the International Energy Agency (IEA), data centres account for approximately 1% of global electricity demand, a figure that is projected to grow without sustainable intervention. Data centres & networks – IEA

By adopting green data strategies, companies can lower operational costs through energy efficiency while positioning themselves as sustainability leaders. Organisations that prioritise green data management will also be better prepared to comply with upcoming regulations on carbon reporting and environmental transparency. In competitive markets, these sustainability efforts can become a unique selling point, attracting eco-conscious customers and investors. Sustainable data management and the future of green business | Sustainability Magazine

Building Trust Through Transparency in Data Sustainability

In a marketplace where trust is a critical commodity, transparency in green data practices can significantly enhance customer loyalty. Consumers and business partners are increasingly scrutinising how companies manage their environmental responsibilities. Clear, public reporting on data sustainability efforts not only meets regulatory requirements but also reassures stakeholders that the organisation is committed to ethical and sustainable practices.

Companies leading in green data management often publish annual sustainability reports outlining their energy usage, carbon reduction goals, and the steps they are taking to minimise their digital footprint. This level of transparency can improve brand reputation, attract socially responsible investors, and deepen customer relationships. In an age where data privacy and ethical responsibility are paramount, combining these principles with sustainability creates a powerful brand narrative.

Aligning Green Data Strategies with ESG Goals

Environmental, Social, and Governance (ESG) criteria are becoming an essential framework for corporate accountability and long-term growth. Green data management aligns directly with the environmental pillar of ESG, demonstrating a company’s commitment to minimising its ecological impact.

By implementing green data strategies, organisations can meet and exceed evolving ESG regulations, such as the European Union’s Corporate Sustainability Reporting Directive (CSRD) or the U.S. Securities and Exchange Commission’s proposed climate disclosure rules. Beyond compliance, these initiatives showcase forward-thinking leadership, which is particularly appealing to institutional investors and partners focused on sustainable portfolios.

Moreover, embedding green data practices into ESG strategies promotes internal innovation. Businesses are encouraged to rethink their data infrastructure, invest in renewable energy for data operations, and develop more efficient workflows. This innovation not only drives sustainability but also enhances operational agility and future-proofs the organization against environmental and regulatory risks.

The Path Forward: Embracing Green Data for Competitive Success

As sustainability becomes a defining factor for business success, companies that adopt green data management are positioning themselves for long-term competitive advantage. The path forward involves a comprehensive approach that integrates these principles into every aspect of business operations.

Key steps include:

Optimising Data Usage: Reducing redundant data storage and implementing data lifecycle management to minimise energy consumption.

Energy Efficiency: Transitioning to energy-efficient servers, adopting sustainable cloud providers, and investing in renewable energy solutions.

Transparent Reporting: Regularly publishing sustainability metrics and openly sharing progress on environmental goals.

By embedding green data practices into their core operations, businesses can reduce costs, increase market trust, and stay ahead of regulatory trends. In an era where sustainability is no longer optional, green data management is not just an environmental necessity, it’s the next competitive advantage.

The shift toward eco-conscious data strategies starts now. How is your organisation integrating sustainability into its data practices? Get in touch with Jake Carrington on LinkedIn or by email.

Leveraging Data as an Asset

Mon, 24 Mar 2025 13:22:00 GMT

In today’s digital economy, data has emerged as a pivotal asset, driving innovation, efficiency, and competitive advantage. Recognising this, recent regulatory developments have formally acknowledged data as a critical business asset. Prompting organisations to reassess their strategies and governance frameworks.

Data as a Formalised Asset: A Paradigm Shift

Traditionally, data was viewed as a byproduct of business operations, often underutilised and undervalued. However, the exponential growth of digital technologies and the information economy has transformed data into a core asset, essential for decision-making and strategic planning. Recognising this shift, regulatory bodies have begun to formalise data’s status as an asset. Leading to significant changes in how organisations manage and leverage information.

For instance, the U.S. Consumer Financial Protection Bureau (CFPB) proposed new regulations in December 2024 to limit the sale of personal information by data brokers. These rules aim to protect consumers by ensuring that companies handling personal financial data adhere to credit reporting laws, thereby acknowledging the intrinsic value and sensitivity of data. Reuters

Implications for Business Strategy

The formal recognition of data as an asset necessitates a strategic overhaul for many organisations. Key considerations include:

Enhanced Data Governance: Companies must implement robust data governance frameworks to ensure data quality, accuracy, and compliance. This involves establishing clear policies and procedures for data management, aligning with regulatory requirements, and mitigating risks associated with data mishandling.
Strategic Partnerships: Leveraging data can unlock new business opportunities through strategic collaborations. By analysing customer behaviour and preferences, businesses can identify potential partnerships that enhance customer experiences and drive growth. For example, insights into consumer purchasing patterns can lead to innovative product offerings and cross-industry alliances. vox.com

Role of Data Leaders in Navigating the New Landscape

As data becomes central to business strategy, the role of senior data leaders has evolved to encompass broader responsibilities:

Advocacy and Education: Data leaders must champion the importance of data as a strategic asset within the organisation. Fostering a culture that values data-driven decision-making and continuous learning.
Compliance and Ethics: Ensuring adherence to evolving regulations requires data leaders to stay abreast of legal developments and implement ethical data practices. This includes safeguarding consumer privacy and maintaining transparency in data usage.
Innovation Facilitation: By harnessing data analytics and emerging technologies, data leaders can drive innovation, optimise operations, and enhance customer engagement, thereby contributing to the organisation's competitive edge.

Summary

The formalisation of data as an asset marks a significant milestone in the digital era, compelling businesses to rethink their strategies and operational models. For data leaders, this evolution presents both challenges and opportunities to steer their organisations toward a data-centric future. Where informed decision-making and strategic agility are paramount.

Unlock the full potential of your data! Implement robust governance, foster strategic partnerships, and stay ahead in the digital economy. Start leveraging data as your most valuable asset today, simply send me an email or reach me on LinkedIn at Jane Smith .

British Data Awards Finalists for Best Place to Work in Data

Wed, 19 Mar 2025 16:46:00 GMT

We’re delighted to announce that Eden Smith has been shortlisted as a Finalist in the British Data Awards 2025 in the Best Place to Work in Data category!

The British Data Awards celebrate organisations leading the way in the UK’s vibrant data ecosystem. This year saw a record 448 entries from FTSE 100 giants, public sector pioneers, tech unicorns, and innovative not-for-profits. Standing out in such a competitive field is a true honour.

Why Eden Smith?

At Eden Smith , we believe that people are at the heart of data success . Our mission is to create an environment where data professionals and organisations can thrive – not just in their careers but in their personal and professional development.

We are proud to be creating an environment where data professionals thrive, organisations succeed, and the industry as a whole moves forward.

Creating an outstanding workplace & community isn’t just about perks – it’s about empowering careers, championing diversity, and driving impact in the data sector.

What makes us the Best Place to Work in Data?

What Our Team Says

“The culture is built on trust, collaboration, and encouragement. The leadership team is always there to support and inspire.” – Jake, Sustainability Staffing Consultant

“Eden Smith’s flexibility gives me a super healthy work-life balance, making me enjoy my work even more!” – James, Principal Staffing Consultant

“As a working mum, having a company that truly understands work-life balance has been invaluable.” – Marie May, Head of Nurture

“I feel encouraged to share ideas and contribute to the company’s success, which fosters a strong sense of belonging.” – Lauren Johns, Project Coordinator

Looking Ahead

Winning this award would recognise our commitment to shaping the data careers of tomorrow , fostering an inclusive workplace, and making a lasting impact in the data consulting and staffing space.

But for us, the true reward is seeing our team and community thrive .

How Businesses Can Leverage AI for Growth, Innovation, and Sustainability

Mon, 17 Mar 2025 14:02:00 GMT

At the latest Eden Smith Data Leaders Executive Lounge , Hannah Foxwell and Lee Fulmer led a dynamic and thought-provoking discussion unfolded on AI adoption, strategic business impact and sustainability. Operating under Chatham House Rules , our community of data leaders explored real-world AI applications, challenges and opportunities.

AI as a Business Enabler, Not a Replacement

A recurring theme was AI’s role as an assistant rather than a replacement for human expertise. Speakers shared examples of AI’s transformative impact, from reducing construction planning from six weeks to just 30 minutes to driving significant ROI through bespoke AI solutions tailored to specific business needs. The conversation underscored that AI’s true value lies in iterative experimentation , allowing businesses to adapt and optimise over time.

Trust, Verification and Data Readiness

A staggering 72% of high-performing AI pilots face data quality issues , highlighting the need for trust and verification in AI implementation. Clean, well-structured data remains a fundamental prerequisite for success, and organisations must first ensure their AI initiatives are built on a solid foundation of governance and data integrity .

Strategic AI Adoption: Focusing on Business Challenges

Speakers emphasised the importance of solving genuine business problems rather than adopting AI for AI’s sake . Whether in pharmaceuticals, FMCG or finance , AI’s success hinges on aligning solutions with core business objectives. The group also discussed the need for small, measurable wins to build momentum and executive buy-in.

AI Regulation: Balancing Innovation and Compliance

As AI legislation continues to evolve, participants debated how regulation could impact the UK’s competitive stance in AI development . While governance is essential , overly restrictive policies could stifle innovation . The consensus? Businesses should focus on ethical AI adoption while staying ahead of potential legislative shifts.

Sustainability Spotlight: Sapling Spirits

Our evening also featured an inspiring presentation from Sapling Spirits , a climate-positive brand planting a tree for every bottle sold. Their innovative approach to sustainable packaging and reforestation serves as a model for businesses looking to balance profit with purpose .

Key Actions:

As AI continues to reshape industries, our Data Leaders Lounge remains a hub for candid conversations and collaborative innovation . Stay connected for upcoming events and deeper dives into AI strategy, governance and real-world applications .

Take Part in the AI Readiness Survey

Would you like to take part in our AI Readiness Survey ?

Will take just 10 minutes to take part and will help inform understanding of AI adoption, maturity and bench mark this data across various industries. Everyone who takes part will receive:

Click through to be part of this valuable research and join us in shaping the future .

9 years – from Disruptors to Data Leaders

Mon, 17 Feb 2025 15:06:00 GMT

Celebrating 9 Years of Eden Smith

Nine years ago, Eden Smith was born from a bold vision. Jez Clark and Matt Smith saw an opportunity to disrupt a broken recruitment industry, creating a business that would do more than just place talent- it would empower organisations to lead with data. What began as a niche staffing business has since evolved into a full-scale data consultancy, supporting organisations across industries with expert consulting, staffing, and AI-driven solutions.

Today, as we celebrate this milestone, we reflect on the journey – one filled with passion, perseverance, and purpose. From its humble beginnings to becoming a successful enterprise with a global reach, Eden Smith has stayed true to its mission: to connect, support, and elevate the data community.

The Journey: From Vision to Reality

Co-founder and COO, Matt Smith , recalls the early days:

This growth has been driven by an unwavering commitment to both people and progress. From matching top talent with leading businesses to expanding into consulting and AI-driven services, Eden Smith has never shied away from taking bold steps.

A Community-Centric Approach

At the heart of Eden Smith’s success is the community it has built. More than a business, it has become a network- a space where data professionals are empowered, where ideas are exchanged, and where careers are shaped.

Jez Clark , Co-founder and CEO, reflects on this evolution:

Looking to the Future

Nine years is both a milestone and a launching pad. With a strong foundation built on expertise, trust, and collaboration, Eden Smith is set to continue making an impact in the world of data and AI. The next phase of growth promises even more innovation, deeper partnerships, and an unwavering commitment to developing organisations and people that are data fluent & future-fit.

As we celebrate this incredible journey, we extend our gratitude to every team member, client, and partner who has been part of this story. The past nine years have been transformative, and the future is brighter than ever.

Here’s to the next chapter of Eden Smith – bigger, bolder, and ready to shine!

Eden Smith Group Ltd

Talent & Culture in the AI Era - Key Takeaways from the Data Decoded Roundtable

Innovation needs a “portfolio mindset”

Power skills are now “data skills”: storytelling, collaboration, prioritisation

The workforce impact is real - and hiring signals must change

Closing reflection: AI may make organisations more human - if leaders choose to lead

Next Steps

The Eden Smith Exec Lounge Summary

From Data Delivery to Durable Value: What Leaders Are Really Grappling With

Why does value from data and AI still feel so hard to realise - despite record investment, tooling and talent?

From delivery focus to outcome partnership

When goals aren’t real goals

The pull back towards technology ownership

AI, judgement and the risk of hollow progress

The human challenge beneath the technology

A community conversation, not a finished answer

Transformational Leadership in the Age of AI

Executive Summary

1. Leading AI Transformation: Building Intelligence with Human Capability at Core

Speaker - Sonia Aslam

2. Your Best Problem-Solvers Are Holding Your Business Back

Speaker - Alex Patient

3. 3D Transformation – Discipline, Decisions & Delivery

Speaker - Darren Minshall

The Unexpected Perils of Rural Litter Picking (and the Adidas-Wearing Sheep Responsible)

Three Key Litter-Picking Learnings

Why This Matters

How Data, AI and Governance Are Reshaping the Lloyd’s Market

A Market Commentary for the Lloyd’s and Specialty Insurance Community

The Next Era of Innovation in the Lloyd’s Market

From Digital Market to Intelligent Market

The Structural Challenge for Specialty Insurers

Regulatory Expectations and the Rise of Accountable AI

Lloyd’s as a Shared Data Ecosystem

New Risks Emerging from Artificial Intelligence

The Emergence of the Real-Time Insurer

Preserving Trust While Scaling Intelligence

Delivering Business Value Through Data Migration and Transformation Projects

Delivering Business Value Through Data Migration and Transformation Projects

Understanding the Business Need

Collaboration with Technical Delivery Team

Turning Data into Meaningful Insights

Supporting Agile Delivery

Ensuring Quality Through Testing

Unlocking the True Value of Data

Ethics As People, Not Compliance

Ethics As People Change,

Not Compliance

Why Ethical AI Keeps Failing in Organisations

The Compliance Trap

Ethical AI as a change problem

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Ethical AI as a change problem

Where Ethical AI breaks down during transformation

Ethics as a transformation capability:

Practical checklist

What changes when ethics is treated as change:

Nurture Programme Student Spotlight: Charles Adeiye

Celebrating 10 Years of Eden Smith & Building the Next Generation of Data Talent

Please tell us a bit about yourself

Why he applied to the Nurture Programme

Live Project Experience at Cleo

Life after the project

Impact on his career

Reflecting on Eden Smith’s Impact

What a Decade of Nurture Taught Us About Closing the Skills Gap

The Nurture Programme was born to bridge the gap between academia and industry.

1. The skills gap is real - but it is not purely technical

2. Industry must play an active role in shaping academic learning

3. Live projects are the most powerful catalyst for employability

4. Structured employability preparation matters just as much as technical training

5. Closing the gap requires inclusive opportunity, not elitism

6. The skills gap is both a tech challenge and a people challenge

7. A scalable model for national skills development

What the next decade demands

Why AI Transformation Fails

Why AI Transformation Fails

AI is everywhere – but is it creating value?

Why is it important that we’re getting AI right?

So why is AI failing for so many? And how can we fix that?