Sustainable Data Practices & What New Data Professionals Should Know

Marie May • June 10, 2025

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. 


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 .
Woman and man touching hands, digital data flowing between them, with digital head projections.
By Eden Smith December 3, 2025
Discover why teams resist AI and how leaders can drive real buy-in using behavioural science, transparency, and human-centred adoption strategies.
People in office meeting with person on screen via video call.
By Eden Smith December 2, 2025
Discover why Data Translators, hybrid talent blending business, data, and communication, are becoming essential as organisations move beyond pure tech roles.
Show More