Q: You’ve spoken about the need to “begin with the end in mind” when it comes to data sourcing. What does that mean for government?
For me, the end goal is always the same: delivering valuable services for citizens and value for taxpayers. If you stand back and look at data sourcing from that perspective, it becomes clear that it’s not just a technical exercise. It’s about outcomes.
Across the recent panel on modern data sourcing at Think Data for Government, we talked about maturity, culture, trust and capability, and those really are foundational. Government already has many of the components of a strong data strategy, but the challenge is connecting them to what you’re actually trying to achieve. What services are you delivering? How will data help improve them? And what is realistically possible with the tools you have today?
If you don’t anchor data sourcing in those questions, you risk building pipelines, platforms or products that don’t meaningfully improve services. Beginning with the end in mind helps ensure data is being sourced, shared and reused in a way that directly supports better outcomes for citizens.
Q: One theme you raised was technology convergence. Why is this so important for digital government?
What we’re seeing across both public and private sectors is a shift away from fragmented technology stacks towards more converged, integrated platforms. Historically, government struggled with data sprawl; now we’re seeing a similar challenge emerging with technology sprawl.
Different teams adopt different tools to achieve very similar goals, which introduces friction and complexity. Hyperscale platforms are now converging data storage, integration, analytics, visualisation and AI capabilities into coherent ecosystems. That kind of convergence matters because it simplifies architecture and lowers the barrier to sharing trusted data.
For government, standardised and simplified architectures can remove many of the practical obstacles to collaboration. When systems are easier to connect and data is managed consistently, organisations can spend more time delivering services and less time wrestling with infrastructure.
Q: Is there a risk that simplification limits innovation?
I don’t think simplification and innovation are in conflict – in fact, they enable each other. When you reduce unnecessary complexity, teams can focus on what really matters: designing, building and improving services.
The risk comes when organisations adopt technology without a clear view of how it fits into a wider ecosystem. That’s how technical debt builds up. With technology moving so fast, especially with AI, you have to architect for tomorrow as well as today. Otherwise, you’re constantly firefighting instead of innovating.
Simplified, converged platforms create a stable foundation on which innovation can happen safely and sustainably.
Q: Collaboration came up repeatedly in the discussion. What conditions are needed for effective collaboration across government?
Collaboration isn’t just about sharing data – it’s about shared understanding and shared intent. One of the points that resonated with me during the panel was the importance of bringing together different disciplines.
Data is a technical topic, but the people who understand the policy context, frontline services, legal frameworks and citizen experience are just as important. Effective collaboration requires all of those perspectives to be involved in decision-making.
If you liked this content…
Trust also plays a huge role. Organisations need confidence that data is being used appropriately and that governance is in place. When those foundations exist, collaboration becomes much easier – not just across departments, but between local and central government as well.
Q: You’ve highlighted curiosity and capability as critical enablers. Why do they matter so much?
Digital transformation depends on people as much as technology. You need technical skills, of course, but you also need curiosity – people who want to understand the problem they’re solving and who care about how a solution will be used.
If teams don’t have that curiosity, they lose the passion that drives good design and delivery. In government, where organisations are complex and challenges are often systemic, that mindset is essential.
Building capability isn’t about everyone becoming a data scientist. It’s about raising awareness and understanding across the organisation so that people can engage meaningfully with data-driven initiatives and contribute to better decisions.
Q: AI is accelerating expectations across the public sector. What should organisations be thinking about before deploying it?
The first question is readiness. Are organisations ready to deploy AI safely? That comes down to whether their data is trusted, well governed and fit for purpose.
AI doesn’t remove the need for strong fundamentals – it amplifies it. You need to understand your data lineage, how it’s been collected, and whether it’s appropriate for training or improving models. Otherwise, you risk creating technical debt or introducing new risks.
What excites me about AI is its potential, but things are moving incredibly fast. Government has to deal with today’s challenges while also designing for the future. That balance is difficult, but without it, you end up constantly playing catch-up.
Q: What can the public sector learn from the private sector when it comes to data sourcing – and what should it avoid?
One useful lesson from the private sector is the outward-looking mindset. Private organisations often start by asking what information is available externally and how it can be combined with internal data to deliver better products and services.
In the public sector, the focus often starts internally – within teams, then departments – before looking across organisational boundaries. There’s an opportunity to think more broadly from the outset.
That said, government also has advantages. Unlike the private sector, collaboration isn’t competitive in the same way. There’s a real opportunity for knowledge sharing and collective progress, because everyone ultimately has the same goal: better outcomes for citizens.
