Policing’s Big Data problem and how to fix it
The benefits of data analytics in policing are huge. Today, we’re already seeing the benefits of analysing data from body cameras, sensor networks and smart devices. The insights derived from this data can help police forces become more efficient and effective at predicting and deterring crime. But for big data to reach its full potential within policing, a rethink of how it is gathered, handled and analysed is required.
The current reality of 21st century policing highlights the need for general improvement. Across many forces, a mismatch of IT systems and low levels of data-trained staff means that big data is not always utilised as comprehensively as it could be. Instead of data being collected, stored and analysed in a consolidated way, processes are often dominated by silos across police forces nationwide. This in turn limits the predictive capability achieved. At best, the results from data analytics are treated cautiously and users often revert to professional judgement. At worst, the results can unwittingly lead to sub-optimal resource deployment and misinformation between police and the public.
Correcting this problem will require more than addressing the ‘data’ challenges. It’ll need law enforcement agencies to rethink how they approach data, as well as how they train people to work with data. The RUSI report, ‘Big Data and Policing’ outlines a number of recommendations to tackle this, including the need to prioritise predictive mapping software, for analytical tools to use national datasets, and for an ethical framework to be developed to ensure big data is being used in an effective and legal way, amongst others. But this is merely the tip of the iceberg. To realise the potential of big data in policing, the following changes are also necessary:
A standardised approach to data
The world is an increasingly connected place which continues to produce more and more data. But quantity isn’t necessary quality. The data police forces collect is usually unstructured. Datasets tend to be stored in separate data repositories and collated in ways that make connecting the dots difficult. In fact, manual data collection remains rife across many local forces. To deepen this problem further, lack of standardisation is endemic across many government departments. While there’s an obvious need for police to collaborate with local authorities – the reality is that data sharing between them is not easily achieved.
The promotion of cross-departmental and cross-border data sharing will require police authorities to collect and manage their data in a joinedup way. A shared Multi-Agency Safeguarding Hub (MASH), which can provide technical architecture, define processes, guidelines, and standards to aggregate and share these different data sources will help. As will the introduction of automation, saving hundreds of hours on administrative tasks. But for change to be effective, a deeper cultural change is needed. Police officers from the beat to the high command will need to approach data collecting and sharing in a transparent and controlled manner.
Skilled data scientists at the helm
Police organisations need access to the expertise to make sense of the volumes of information compiled. This includes structured and unstructured data, such as images and videos. But getting the right data scientist to link these data sets and unveil the ‘unknown unknowns,’ remains to be difficult. Budget restrictions and competing priorities result in limited support for training skilled inhouse data analytics experts. The problem is exacerbated by the trend of public sector data analysts moving to the commercial side in pursuit of better pay and career progression. Investment in inhouse skills is a must. This applies to all public sector bodies, who instead of relying on outsourced IT, must now look to build up their own core team in order to succeed.
Apprenticeships can encourage young people into STEM careers, and embracing more common private sector benefits (such as flexible working) can improve public sector job retention.
A robust technology solution
Many public sector organisations will be all too familiar with the technology trial run. With countless vendors all offering solutions with more or less the same capabilities, it can be difficult to determine which product really is most suitable. But there are certain features that can be prioritised. Any technology solution that provides police authorities access to their data in an easy to read format, available in near real time and on a range of devices, will help with end user adoption. As will any tool that allows for data to be safely shared with others, without putting the data holder in jeopardy.
But such solutions come with barriers. The cost of implementation can often be prohibitive for many police authorities, and some officers will have a natural aversion to change. This is why finding a vendor that provides both technical know-how and support is crucial. A non-proprietary solution will give law enforcement agencies flexibility in their infrastructure, meaning that they are not locked-in to one solution or vendor. The best practice for implementation would be to start small, prove the value of a solution, then incrementally add new features and functionality, This iterative methodology means forces can adapt capabilities to meet changing threats, budgets and needs. And as the RUSI report recommends, any decisions concerning big data technology should be made by consulting and keeping end users in mind.
Prevention has long been a mainstay of modern policing. Yet due to human limitations, much policing had been reactive and incident-focused. But data and technology is helping. When harvested, stored and used correctly, data can identify and address complex patterns in crime trends. This means that forces can more efficiently utilise manpower, focusing limited resources into areas the data tells them they are needed the most. Advanced analytics can also be used to ensure the enquiries outside of the police’s remit are routed to the appropriate local service. Data can also help planning authorities improve the way they allocate resources. Yet most importantly, data can help police and crime commissioners achieve results in a future-facing manner, with insights and crime prevention at its core.
Got to www.unisys.com/SafeCities to find out how next-generation technology solutions are being used by law enforcement to protect citizens and keep them safe.