Tracey Allen, Associate Director at digital transformation consultancy Transform, on why the biggest barrier to inclusive public services is not a lack of information – it’s the inability to join it up.
Ask most public sector leaders whether they have data on their most vulnerable service users and they will likely say yes. Ask them whether they can see all of that together, in a single coherent picture, in time to act before a situation escalates – and the answer almost universally changes.
Photo by Lukas Blazek / Unsplash
This is the paradox at the heart of modern public service design. We are not short of data. We are short of joined-up data. And in that gap, between what we hold and what we can see, people fall.
The issue is not technology. It is visibility.
There is a reasonable explanation for how we got here, and it deserves to be named honestly before it can be addressed.
The data that would most help us identify people at risk – the signals of financial instability, of mental health struggle, of domestic difficulty – is also the most sensitive. There are genuine ethical questions about how it is collected, stored, shared and acted upon. Those questions matter. Getting them wrong can cause real harm.
But in too many organisations, those questions have become a reason to do nothing at all. The result is not responsible data use; it is paralysis. And paralysis has its own victims. They are simply less visible than the harms of misuse, so they do not generate the same headlines.
Early warning signs go unnoticed. Intervention happens late, when harm has already escalated. Services default to crisis response when they should be offering early, compassionate outreach. The cost – financial, human, institutional – compounds quietly until it cannot be ignored.
What responsible data use actually looks like
The organisations getting this right are not the ones with the most sophisticated technology. They are the ones who have done the foundational work of bringing their data together with appropriate governance and then trained their people to use it.
Take NAPAC – the National Association for People Abused in Childhood. Its ability to expand support for survivors, influence policing and work alongside the Ministry of Justice did not depend on cutting-edge machine learning. That depended on a centralised dashboard: a single, coherent view of its data that gave NAPAC visibility it had not previously had. More efficient data processing meant more time for the work that actually mattered.
Or consider Health Education England, which used data analysis, including qualitative research and large language model training, to predict which junior doctors were likely to leave their training programmes, and to offer support before they walked out the door. The platform achieved over 60% predictive accuracy. But the more important number is the one that represents every junior doctor who stayed because someone saw the warning signs in time.
There is another dimension to the data problem that sits largely undiscussed in most transformation conversations: sustainability.
Hoarding data is not just an ethical failure. It is an environmental one. Every gigabyte of irrelevant or ungoverned data stored and processed has a carbon cost. It also dilutes AI performance, because models trained on junk data produce junk outputs.
The three conditions for making this real
The path from fragmented data to meaningful action rests on three foundational conditions:
- Translation Lack of data is very rarely a challenge for organisations. It usually already exists alongside signals of need but is often difficult to interpret because it’s isolated or dispersed across departments; only a piece of the puzzle and not the full picture. Translation turns siloed and sensitive data into segmented, meaningful and actionable customer intelligence. Done well, it allows organisations to see people more clearly – not as abstract risk scores, but as individuals whose needs and trajectories can be understood and responded to early and with care.
- Trust Insight without trust is fragile. Trust can only be built through inclusive, participatory design that creates confidence, representation and long-term relationships with the people services are intended to support. This means designing with, not just for, marginalised groups; creating psychologically safe spaces for participation, particularly where trauma or power imbalance exists; and treating lived experience as expertise, not anecdotes. Trust enables better data, better design and better, empathetic decisions. It reduces resistance, improves engagement and ensures that transformation reflects real human experience rather than organisational assumptions.
- Technology Technology plays a critical role, but it shouldn’t be viewed as a standalone solution. The goal isn’t always to replace existing platforms, but to integrate them and layer in new capabilities, including AI, to deliver a coherent and personalised experience. This includes connecting CRMs, case management systems and operational platforms; embedding analytics and AI where they support judgment and action; and designing human-in-the-loop processes by default. When technology is integrated with translation and trust, it becomes an enabler of orchestration — aligning insight, design and delivery around the needs of people at the edges.
The data exists. The technology exists. The will, in most organisations, exists too. What has been missing is the coherence to bring them together and the courage to act on what they reveal.
This article draws on Transform’s February 2026 white paper, Designing for the Edges to Benefit the Whole.
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