Drawing on award-winning examples from healthcare, justice, media and policing, Jake O’Gorman argues that data-driven transformation depends on people, not platforms. He writes about why leaders must prioritise data quality, skills and ethical governance and shares stories that start with real problems, build confidence to turn evidence into everyday decisions.
Despite billions invested in digital transformation, far too many organisations confuse technology spend with evidence-based leadership. In my roles at Corndel, from leading on Data and AI Strategy to serving as Chair of Judges for the Data-Driven Leadership Awards, I’ve been privileged to witness first-hand what separates ambition from impact.
The most powerful changes aren’t about technology: they are about people
Digital transformation isn’t just about deploying the latest tools. It’s about equipping leaders and teams at every level with the skills, governance, and mindset to use data purposefully, because the most powerful changes aren’t about technology: they are about people.
Leadership in the age of AI means leaders must rethink their roles, combining technical understanding with human judgment, empathy, creativity, and vision, so they can act with clarity and confidence, even when evidence challenges assumptions. Yet only one in four businesses uses data extensively to generate actionable insight, and less than half believe their data strategy is aligned with business objectives.
Data quality is everyone’s problem
As the UK doubles down on digital transformation, poor data quality is stalling progress. Almost 70% of data professionals, including C-suite executives, admit they don’t completely trust the data used for decision-making. But without reliable data, frontline teams can’t deliver reliable outcomes.
At Central and North West London NHS Foundation Trust, inconsistent data was undermining service planning. The turning point came when Christine Herdman, the Trust’s Data Quality Support Manager, reframed data quality as a user-led issue rather than a compliance burden.
As Christine explained: “Early on, there was a gap in understanding, particularly among clinicians, about the relevance of data. So, we didn’t start with the tool. We started with what they cared about: referral-to-treatment times, service performance, patient outcomes. We used Tableau to identify long wait times, but more importantly, to distinguish between genuine waits and delays caused by data quality issues. The wait time report gave them a daily view of their patients. This enabled them to sort by longest waiters and quickly spot errors and anomalies. If a patient showed up on the report but wasn’t actually on a waitlist, it often pointed to a data quality issue. That visibility helped drive behaviour change and better resource allocation.”
Christine continued: “If we found data quality problems, we’d deliver targeted training on everything from reporting context to database usage. No two services were the same. As a result, Tableau became the single source of truth, with clinicians checking wait times daily and using data for real-time decisions.”
As a result, the daily use of data became routine for frontline teams, particularly admin teams, which Christine describes as “the engine room of many services”. She says that giving them the capability to understand their data led to real improvements in efficiency and resource use.
The lessons are clear: the future of data is around its democratisation. Everyone needs the opportunity to investigate the data and make better decisions as a result. But if your people can’t trust the data they work with, no amount of automation will compensate. In other words, you have to help people who don’t care about data to care about data.
From fragmentation to clarity
Complex challenges require joined-up leadership. Few examples illustrate this better than the Ministry of Justice’s Better Outcomes through Linked Data (BOLD) programme.
Led by Toby Hayward-Goulding, the project linked previously fragmented datasets across criminal justice, housing, and healthcare. Connecting these datasets produced a striking new insight: less than half of those issued with a Drug Rehabilitation Order had ever received treatment. Toby explains: “Think of someone rough sleeping, battling addiction, and a history with the criminal justice system. The data and the services that touch their lives are typically fragmented. Project BOLD linked those data points to see how services delivered in one part of government affected outcomes elsewhere.”
Toby’s advice is simple: “Don’t start with the data. Start with a real-world problem you’re trying to solve. Then figure out what data you need, where it lives, and how to access and govern it responsibly. Data is one tool among many, but when used well, it can unlock genuinely transformational change.”
This principle is central to evidence-led leadership. It is not about collecting more data, but about being more purposeful with it. Toby’s story reflects a growing expectation that leaders must be fluent not only in the language of strategy but also have the courage to follow the logic of evidence.
Turning fear into fluency
At the Financial Times, journalists viewed AI adoption with fear, worrying it could compromise editorial independence. As McKinley Hyden, Head of Data Value and Strategy and part of the AI Transformation Programme team, explains, “With our business model centred on trust and credibility, AI presents both a massive opportunity and a real risk. We had to embrace AI because it’s transformative, but we also had to do it in line with our values: human creativity, ethics, and responsibility. Editorial teams, understandably protective of journalistic integrity, baulked at our licensing deal with OpenAI.”
McKinley continues: “Rather than imposing change, the AI Transformation Programme team developed an AI Impact Framework to define where AI added value, and where it did not. An AI Immersion Week, including workshops and playful competitions, reframed AI as a creative tool rather than a threat. Today, journalists, not just technical staff, have developed over 400 custom GPTs.”
Play was an important part of the cultural change. McKinley explained: “What surprised me most was how this playfulness, encouraging people to play with AI, broke down their fear. It shifted the tone from dread to curiosity, and it helped bring people into the conversation.”
Real transformation happens when people are equipped, supported, and motivated to use data confidently in their daily roles. This means giving your team freedom to experiment, question assumptions, and learn from failure without fear.
Governance: The enabler of trust
Governance is too often seen as a brake on progress, yet in reality, it’s the foundation of sustainable innovation. West Yorkshire Police’s Project Spotlight is a digital crime prevention initiative designed to reach young people at risk of offending through platforms like TikTok. It succeeded because of the ethical framework which underpins it, not despite it.
Ethical frameworks were co-designed with legal experts and external advisors to safeguard privacy while enabling effective targeting, and human oversight was embedded across the project. Content was shaped by behavioural scientists to ensure it was appropriate and non-stigmatising.
Chief Inspector James Kitchen, who led the initiative, stresses that ethical frameworks are crucial to maintaining public trust and achieving long-term success. “I’ve learned not to be discouraged by bureaucracy or the sheer volume of legal and policy considerations around data. It can seem daunting, but the frameworks are actually there to support us in using data responsibly. The key is not to retreat in the face of complexity. Push through it. Understand your data – where it comes from, what it says, and what its limitations are. My advice to others considering a similar approach: don’t be afraid to try. Don’t be put off by legislation, forms, or policy hurdles. Yes, they exist, but they are navigable. And if you’re genuinely trying to do the right thing for the right reasons, you’ll find a way through.”
McKinley Hyden at the Financial Times agrees: “AI’s a high-stakes landscape, fast-moving, unpredictable. Taking time to align policy, ethics, and governance isn’t bureaucratic – it’s essential. If you don’t do it, you risk doing more harm than good.”
Rethinking leadership for the data economy
Across these varied examples, the common denominator is not technology. It is leadership. As AI reshapes industries, senior leadership is the cornerstone of a truly data-centric culture.
Data and AI are so intertwined that data literacy has to be part of the organisation’s AI strategy. Yet just 24% of leaders are considered data literate. Successful data-driven organisations don’t just invest in platforms or dashboards. They:
- Start with challenges, not dashboards
- Treat data quality as a strategic issue
- Align governance with ethics and purpose
- Embed fluency across teams, not just specialists
The question every board must now ask is not “how much have we invested in tech?” but “are we turning data into decisions?” because if our leaders cannot answer that, no transformation budget will deliver the results we expect.
Jake O’Gorman is Director of Data and AI Strategy at Corndel
To read more about these award-winning projects, download Delivering with Data: The Data-Driven Leadership Playbook
