Data has always been part of the early years landscape, but its role is changing. When it comes to data, insight, and workforce, The Best Start in Life strategy places new emphasis on data and evaluation.
But data doesn’t excite everyone. It should not be used to just monitor the workforce; it should be used to equip, enable and celebrate it.This means sharing all types of data.Numbers yes, but also information, stories, case studies, and real-time learning – that should foster all people’s relationship with it.Local intelligence is so vitally important so that all practitioners come to understand the patterns and needs within their communities, from multiple perspectives. That requires us to use data collaboratively to co‑design solutions, rather than imposing targets from above.It includes encouraging teams to identify what works, what doesn’t, and what needs to change, drawing upon evidence and experience in equal measure.This is because, workforces feel inspired when they can see problems clearly and are trusted to help shape the response.
The Best Start in Life strategy has accelerated a shift away from viewing data as a compliance exercise and towards understanding it as a core mechanism of local system leadership. It is no longer enough to gather information, submit annual assessments, or produce retrospective reports. If we are to meet rising levels of need, manage the complexities of the childcare market, and deliver genuinely joined‑up support for families, we must treat data as an active, living framework, one that guides decision‑making and enables change as it happens, not months later when it can be too late.
For years, local authorities and partners have generated vast amounts of data. Yet much of it has remained siloed within services, difficult to interpret, or too slow to influence the practice environment. What we need now is a clear and coherent way of organising that information so it tells us something meaningful about the system as a whole. A strong data framework helps us understand not only what is happening, but why it is happening, and what can be done next. It reveals patterns that cut across health, childcare, early education, and family support. It illuminates need earlier, pinpoints the pressure points in local systems, and guides investment to where it will make the most difference.Only if you have time to moderate it, scrutinise and challenge it to ensure it is correct (even if the truth it reveals is uncomfortable) and by asking the ‘so what?’ questions after that.
Critically, using data well allows us to move from assumptions to evidence. We often hear narratives about communities, workforce shortages, childcare sufficiency, or engagement with services that feel intuitively true but are not always substantiated by data. When we bring together population indicators, service performance, workforce information, provider intelligence, and the lived experience of families, a very different picture often emerges, one that is nuanced, sometimes surprising, and always more useful.
This does not mean local authorities need ever more sophisticated technology (although that may very well help and release staff capacity for more delivery). It means using the information we already have more intelligently. In many places, the most transformative progress has come not from new software but from establishing shared definitions, agreeing what ‘good’ looks like, and creating simple, accessible dashboards that everyone can understand. The goal is not to create the perfect dataset but to develop a shared language that supports collective action.
One of the most powerful outcomes of this approach is earlier identification. Families’ needs rarely emerge neatly or predictably. A childcare closure, a delay in speech development, a parent’s declining mental health, or a period of financial instability can all happen quickly, often simultaneously, and with consequences that ripple across multiple services. When data flows are disjointed, those ripples remain invisible for too long. When data is joined up, local leaders can see emerging patterns early enough to intervene, sometimes before a family reaches crisis, sometimes before a provider closes, sometimes before a staff team becomes overwhelmed.
Other benefits are equally significant. Data‑driven decision‑making helps local areas plan their workforce more strategically, deploy resources more efficiently, and test whether interventions are working. It brings transparency to commissioning decisions and strengthens the case for investment. It also helps system partners communicate more effectively. When everyone is looking at the same information, discussions become less about opinion and more about shared understanding.
Importantly, good data does not replace professional judgement, it enhances it. Practitioners can use intelligence to challenge assumptions about their communities, to reflect on their practice, and to better understand the families they support. Leaders can use it to build confidence, to hold honest conversations, and to create cultures where learning is constant rather than occasional. This is not about performance management; it is about empowering the workforce to see the system around them, not just the task in front of them.
To achieve all this, we need a culture that values curiosity as much as accuracy. We need teams who feel able to ask questions of the data and of each other. We need shared spaces where insights can be discussed, rather than emailed. And we need leaders who model the responsible use of data, not as a tool of scrutiny, but as a tool of improvement.
Ultimately, the purpose of any data framework is simple: to help us make better decisions, faster, and with greater confidence. Used well, it becomes one of the most powerful tools we have for shaping services around families’ needs, responding to change in real time, and building the coherent, intelligent systems the early years sector has long required. It ensures we do not simply describe the challenges we face; we act on them, and we do so with clarity, intention, and shared purpose.That way, we can all learn to love data.
