Managing Data as an Asset in Policing and Law Enforcement
Policing resources are finite and ever more stretched. Crime must be tackled with ever fewer officers and the traditional methods of policing are morphing. New technologies ranging from artificial intelligence and analytics to drones and biometric identity recognition are entering into service as crime prevention and detection tools. At the same time criminal behaviour is becoming ever more sophisticated, with cybercrime exploiting technology and data at speed. As a result, the volumes and types of data being collected by law enforcement agencies are increasing exponentially.
A change of mindset is needed to drive improved policing outcomes.
Treating data as an asset is very easy to say, but it’s a pretty radical change in approach for law enforcement agencies. Data management is generally not part of the skill-set, and certainly not part of the organisational culture which considers data as being primarily for the operational recording of incidents that have already happened. Management preoccupation is with the perceived burden of collecting and recording this data – the dreaded paperwork and admin – with less focus on exploiting the data to help with future incidents. In many instances law enforcement organisations and related agencies are struggling simply to know what data they have, let alone exploit it to achieve policing objectives. These skills and cultural challenges often lead to a discussion around what to do with the data either being avoided or at best put to the bottom of the ever growing to-do pile.
Realise the importance of data and act on it.
Once you change your mindset and start to treat data as a valuable asset, it becomes one. The holy trinity of data is the same for policing as other industries quality, discovery and exploitation. Understand the value data delivers, and could deliver, against your objectives, and you’ll naturally create a focus on data quality. You’ll also work harder and smarter to make sure you know what data you have, how to get to it and how to use it. Once you have quality and discovery sorted,
you’ll have the opportunity to use the data to take action, exploiting the insights it can provide to improve policing outcomes.
Data as an asset – is everyone’s responsibility
Data is not just the responsibility of the technologists in policing. Treating data as an asset is not a task to be delegated out to the CTO. The cultural shift to becoming a data-driven organisation, needs to involve senior command ranks, leadership and teams responsible for managing change and transformation, as well as the Police and Crime Commissioner (PCC). There must be engagement with questions asked of both the command team and the technology team from Chief Constable down. Only this approach will ensure every aspect is covered, from traditional data management and security and of course cost implications, to areas of governance and regulation like GDPR – regulations are complex but they shouldn’t hold you back from transforming and getting benefit.
Joining it up
Data has significant importance in its own right, but a vital part of the exploitation phase and unlocking value is found by joining up data pools – delivering major, much-needed and wide reaching benefits.
Policing is part of a complex and disparate ecosystem of agencies and organisations that all have data assets. Improving the data collaboration and relationships between police partners provides a much fuller and sophisticated picture of a situation. Patterns can be identified and efficiencies gained by automating the collaboration process enabling all stakeholders within the ecosystems to benefit. Joint interventions can be developed across policing: from improved serious crime detection (using joined up data between vehicle tracking information, mobile phone data and border movements) and better support of vulnerable people (identifying victims of modern slavery by analyzing crime and intelligence records) to more effective crowd control strategies (using CCTV analysis). Criminal patterns and escalations of criminal behavior will become more visible. Fundamentally the ability to join and analyse data will prevent and detect crime.
This is evidenced day-to-day across multiple law enforcement organisations where multiple, detailed records, of various types exist on the same person – yet pulling them together is difficult and time consuming, especially if there are inconsistencies in the data. This means it’s not been practical or economical to extract the real value of collecting the information. Today if any incident data collected pre-conviction doesn’t join up – it can’t be shared easily. Hand-written statements of similar crimes across many boroughs mean that patterns can’t be seen and perpetrators can continue their activities.
The move to predictive policing
But things are changing. New technology is providing the tools to help join up data to extract meaning and then drive timely insights to allow predictive policing. Recognising that no data is perfect, new tools such as eDiscovery can suggest potential links and patterns in data despite minor inconsistencies. Stored statements become searchable, keywords tag key elements of the crime, and images can be added to unstructured data searches. The Metropolitan Police force is doing this now – other forces need to follow.
In the coming years good, successful policing will go hand in hand with predictive policing. We’re heading to an environment where we can identify traits and circumstances that lead to certain situations. Such intelligence leads to the ability to make more effective and efficient interventions, taking away burden from the police force, and ultimately helping reduce crime.
Real time responses to data are possible if you treat data as an asset being created in real time. In crowd control situations, for example, there’s the opportunity to combine facial recognition data from CCTV footage with data on known offenders in real time – this can help direct and inform front line response units.
Police forces are already embracing drone technology for information gathering to improve decision making in a cost effective way. At large events a drone can be deployed ahead of people for information gathering. Triaging the response that is required means greater efficiency and more effective use of finite resources.
For many industries treating data as an asset is a given, but law enforcement currently lags behind. Complex data analysis is now an imperative for law enforcement agencies and maximum value must be extracted from data to benefit policing outcomes. The first step must be to treat data as an asset.
Managing data as an asset, the to do list.
- Talk.We need to start talking about data in policing. What is the data we have and where is it? How is it handled and how is it stored? How can we exploit it to help victims and vulnerable people and reduce crime? There needs to be an ongoing dialogue within and between forces and between other law enforcement and social agencies involved. Start to find out more. Ask who is going to own this? Make it part of the force’s objectives and measures. Only then will it be embraced and discussed.
- Transformation.Forces across the country are experiencing a period of huge transformation, towards 2025. Be that sharing resources, a new regional approach, mobile working, formal collaborations with fire departments and council to make service provision efficiencies, the list goes on. In all transformation areas you need to consider your data assets and how they will be impacted by the change. Within any transformation activity make sure you are thinking about, and developing robust processes around capturing and making data assets available and exploitable.
- Tools.Think about the tools you can use to understand the quality of your data better. Identify data quality issues and then create appropriate and robust processes and governance around your data assets.
- Governance.Who owns the asset, who controls and maintains it, who can access it and how? What will happen to the data in the months and years ahead. How will you comply with ever growing regulations? Treat your data as a business asset and make decisions in the same way you would in deploying physical resources to drive targeted outcomes.
- Store.Where and for how long will you store your data? Analyse the data asset’s purposes. Work hard to find cost effective answers to storage. Cloud is key here. The nature of policing requires archiving certain types of data, in a way that allows access and analysis for long periods of time – for cold case enquiries for example – and that can be expensive. Consider your technology environment with that in mind. The need to make sure storage is appropriate and aligned to the requirements of official information categorisation policy adds another layer of challenge for law enforcement. The good news is that these can now be stored in services like AWS, but only if certain specific procedures are followed. This means making sure you have fully addresses data security needs such as encryption, secure buckets and ekeys. You need to be up-to-date around what software guidance is being issued. You’ll need to work closely with a trusted security technology and data management partner, that can demonstrate real understanding of your data assets and can protect them appropriately, so choose wisely.
Chris Bridgland: Mitigate risks and stay compliant – Practical strategies for meeting strict new data privacy regulations like GDPR. – 12th December 2017
As the regulatory landscape evolves and more data moves to the cloud, compliance has become an increasingly difficult, high-priority challenge. Meeting this challenge—and keeping legal, financial, and other risks in check—requires new levels of visibility into and control over all of your data regardless of where it’s located.
Attend this digital boardroom session to hear how Veritas can reduce data risks by providing you with a clear line of sight into all of your data, helping you quickly classify that data within the context of your established policies, and respond quickly and intelligently to data risks and regulatory requests. This includes practical strategies to help you add machine learning and an advanced classification engine to your compliance efforts, automatically manage and pre-determine the regulatory relevance of every document in your organization, gain the deep insights you need to quickly assess your data risks and make smart, informed decisions, and more.