The three approaches to data that could transform policing in 2021

One thing we’ve all learned during the last year is how to deal with uncertainty and change.

Whether it’s a new tier of Covid-19 restrictions we’re living under, how we carry out our work, or how we best adapt and adjust to the new rules; we are quickly getting more used to this unpredictable world we occupy.

The same is true for our police forces and law enforcement agencies. The pandemic has accelerated the need to become future-proofed, with technology playing a vital role in this rapid evolution.

“The pace of innovation and uptake of new technologies has never been faster, and it will never be this slow again.” The National Policing Digital Strategy was quick to point out the pace of transformation. And this was even before the true level of disruption caused by a global pandemic was predicted.

Like every organisation, police forces cannot stand still. They are adapting to this increasingly digital world that we work and live in.

The exploitation of data is key to this transformation, and we must look for rapid and agile ways of accessing and using the data which is available to us. The ‘data maturity’ within UK policing is widely variable between different forces, with some having already invested in modern systems, architectures and capabilities, while others still live in a silo-ed world, where it’s difficult to access core data, let alone leverage it to drive change and innovation.

Traditionally, police forces would see data projects as large, complex, costly and inherently risky. And the value of investment in data was hard to measure in a conventional sense, especially in comparison to investment in other more visibly vital areas – equipment and hardware. An investment in data was (and is) often seen as a long term project with little tangible gain to show in the early days. But this view is changing; and so too are the tools and techniques available, unlocking quick wins and tangible outcomes.

With modern data techniques, we can challenge the conventional approach. Here are three approaches which are potential game-changers for police forces, helping them to unlock their data effectively and efficiently, to enable a faster digital transformation journey:

1. Data virtualisation

The virtualisation of data allows specialists and users to access data in one place via a common, secure gateway, with governance and a data catalogue, without the time and expense of creating a separate data repository, whether that be a data warehouse, lake, swamp, marsh, whatever! The gateway acts as a ‘pointer’ to the real data (along with performance-improving caching etc) and avoids the need for the expensive ‘lift and shift’ of data using ETL (extract, transform, load) processes.

2. Synthetic data

The foundation of many of today’s innovative solutions, especially AI and machine learning, is the data used to train and test models. Often there are significant logistical, contractual and security barriers to obtaining the data needed. ‘Synthesising’ large-scale, fully representative data sets allows these models to be tested and iterated, leading to better overall performance, and reducing risk. Project timescales are reduced, as the solution can be developed, tested and proven in parallel with the governance and assurance activities which would previously have been a hard pre-requisite

3. Design-led approach

Not so much a technology, but a methodology for a flexible and rapid approach to data projects. The distinction is that the focus of problem solving is on what the end-users want, not what IT or data specialists think the business should need. And doing just enough in the project to solve that problem – sourcing the right data from what’s available, letting the user requirements drive the data management required. Yes, an overall data strategy, direction and governance is still necessary, but addressing the users’ requirements rapidly is key. This demonstrates value from the data project at an early stage, and smooths the path to further investment for iteration or new projects.

There are many more technologies which we could have included here (the most obvious being the prevalence of cloud). The message, though, would be the same: with the pandemic being seen as an opportunity to change our attitudes and ways of working, let’s take this chance to challenge the conventional thinking on data projects, look for ways in which we can rapidly and incrementally innovate, overcoming the traditional barriers, and delivering operational value fast.