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The importance of data science, standards, and citizen trust as drivers for sustainable policing success in a rapidly evolving digital world.
For the past ten years the extreme proliferation of new technology and digital innovation has resulted in a societal shift to digital as a natural channel for work, life, entertainment, health and much more besides.
This societal shift to digital, which has accelerated through two years of living through a global pandemic, has forced real change to the nature of policing. More than ever, digital technology is being routinely used and exploited by criminals, with advances in everyday technology affording new opportunities for crime that continually add to the challenges faced by law enforcement and justice agencies. In addition, communities being served serve continue to present increasingly diverse and competing needs, which constantly drive changes in the pace and nature of policing services.
Policing is operating in this context of change, with forces operating under considerable stress, faced with reduced budgets, changing patterns of demand and new skills to match the changing nature of crime. Today, society is made up of an increasing percentage of digital natives, with younger generations driving the pace of change. Citizens are increasingly tech driven and online-savvy and expect the police to be so too.
This pressure, combined with a pronounced emphasis on proactive, preventative policing that is matched to demand requires enhanced levels of insight and initiative. There is an ever-increasing need for a data-led understanding of, and response to, day-to-day policing operations.
Recognising these demands and challenges, the National Policing Digital Strategy 2020-2030, the National Police Chief Council’s Police Vision 2025, and the important policing-industry collaboration, managed by organisations like techUK, now look to address the need to deliver a seamless citizen experience, while enabling officers and staff to take advantage of digital to protect and serve citizens, reduce crime, and maintain order. Technology, data, and digital services are no longer back-office functions, but important keystones of a Target Operating Model for digital policing that drives sustainable operational excellence.
In 2020, the amount of data created globally was the equivalent of every person generating 1.7 megabytes every second. Even as far back as 2017, which seems a lifetime ago, the National Crime Agency (NCA), the Metropolitan Police Service (MPS) and the National Police Chiefs’ Council (NPCC) reported to parliament that in one case led by the MPS, a simple case involving two mobile phones resulted in 20,000 items of data (messages, photos, internet history) needing to be examined, which took around 150 officer hours to review.
We live in a world where organisation of all types in all sectors (and especially policing and emergency services) simply must become data-driven. By data-driven, we mean the capture, analysis, and use of a wide variety of digitised data sources to develop actionable intelligence that informs decision making and improves processes for policing organisations, both on the front-line and in centralised operational management settings.
Volumes of data collected by policing organisations are already large and growing, and the proliferation of new sources of data from body-worn devices, connected cars, and social media platforms is forcing policing organisations to improve their capabilities to understand, process, and make use of exponentially increasing data, from exponentially increasing sources.
This volume of data presents real opportunities for advanced data science techniques to reduce the burden on forces, whilst allowing improving efficiency, effectiveness, and advance at scale and reliably. For example, Natural Language Processing and Machine Learning services can analyse large volumes of documents (e.g., witness statements), forensic data (e.g., genetic records) and imagery data (e.g., CCTV and Drone), identifying significant topic clusters and associations that indicate meaningful correlations between records for further investigation.
The importance of data interoperability and standards cannot be over emphasised. Policing efficiency and effectiveness will involve increasingly complex and interconnected digital systems. For this reason, the standards and capabilities needed to enable systems and data interoperability remain fundamental building blocks for effective digital policing.
The definition of Interoperability is the ability of computer systems or software developed by different ‘manufacturers’ to exchange and make use of information. Our view is that data sharing platforms, and the ethical sharing of data, should not merely be defined by available technology, but by the needs of users (police, citizens, and other stakeholders).
A truly effective interoperability ecosystem should provide an information infrastructure that uses technical standards, policies, and protocols to enable the secure capture, discovery, exchange, and utilisation of information across organisational boundaries and borders.
We can see through our work with NHS England and NHS Improvement on a major national Learning From Patient Safety Events (LFPSE) system, that widely accepted technical standards including DICOM, IHE, and HL7 are having a significant and positive impact on data sharing and insight that will help to consistently and constantly improve patient safety in care settings. With more and more care organisations able to share data, data science techniques are making huge strides in reducing the administrative burden on an already stretched health service to manually investigate millions of incidents, whilst identifying and flagging significant events and trends to experts who can more swiftly and effectively make decisions on service improvements and remedial activity. The adoption and acceptance of standards and interoperability will undoubtedly enable similar gains in policing and citizen safety.
A drive towards nationally accepted (Government Digital Service / Digital Scotland Service / Police Digital Service) digital service standards will encourage and enable data interoperability within and between policing organisations, thereby reducing the cost, risk, and complexity currently associated with data sharing and systems integration. We also note the potential for AI to enable interoperability with new approaches including data lakes and named entity recognition (NER) allowing the extraction of insights from unstructured data.
Wholescale acceptance, and adherence to common storage, sharing, and security standards and practices will ensure a common understanding of ‘what great looks like’, whilst improving the quality and utility of data. To this point, we have observed a trend regarding data and digital evidence towards operational data being modelled and captured using standards based on the Party, Object, Location or Event (POLE) model. Given this trend, we would expect and encourage next generation data standards to adopt the same or similar model where appropriate.
The importance of sustainably enhancing and building on citizen trust in policing increasingly relies on effective engagement between police, citizens, and justice organisations. Building confidence and competence in digital technology and data sciences through learning, development, and strengthened digital and data science capabilities will enable policing organisations to become more effective in both designing and shaping new digital policing operating models and designing digital policing engagement and law enforcement services.
Effective and positive citizen engagement to win and retain public trust is one of the key priorities outlined in the National Police Chief’s Council’s Policing Vision 2025. This will be addressed in part through multi-channel user centred services. Increased police social media presence and digital policing platforms that people choose to both use and trust will be key ingredients for digital policing success.
These well-designed digital services and platforms will be underpinned by secure, standardized, and shareable data across forces and agencies. Both citizen facing services and operational Policing systems will be increasingly powered by artificial intelligence and machine learning that handle huge and growing volumes of information, helping making sense of data to produce insights that allow police to take accurate and appropriate action on criminal investigations, citizen safety action, victim protection services, and the resource and asset management needed to police locally, regionally, nationally, and internationally.