What’s the Role of the Data Office in Modern Businesses and How to Leverage It

Nick Deveney • March 26, 2025

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. 

By Christa Swain December 3, 2025
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 .
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