In the second part of our series discussing data-driven policing, experts from Agilisys discuss the measures police and law enforcement organisations need to put in place to unlock the full potential of data and insights.

As discussed in the first part of our series, the benefits of data-driven policing are clear. But what foundations are required to ensure organisations are utilising the full potential?

“From my perspective, policing needs to have the right infrastructure in place,” explains Evie Dineva, Senior Analytics and AI Consultant at Agilisys. “It’s all well and good to say, you know, we can predict crime, we can predict demand, we can approach certain individuals that we believe are more vulnerable, but if we forget the basics, then success will be limited. Data quality is vital, and that requires the right infrastructure because data coming from disparate systems must be interlinked.”

Kate Hemstock, Senior Data and Insight Consultant at Agilisys and former Strategic Analysis Manager at Derbyshire Constabulary adds: “I think data quality and completeness is the biggest challenge holding back data-driven policing. There are real challenges in terms of the input of data because officers and staff are required to put in a heck of a lot of data. There will be many people who think they didn’t sign up to be a police officer to sit and fill in forms on a computer.”

Data literacy

Data literacy is the cornerstone of success, according to Kate. “Understanding the purpose of data and why it needs to be collected is vitally important. This is particularly true for frontline staff, who need to believe that they’re contributing and preventing crime or boosting safeguarding when asked to collect information. How does the data help? How does it drive efficiency? How does that ultimately result in better outcomes for police and citizens?”

“Everybody needs to understand the value of data and be able to connect it to their role.

A senior leader responsible for planning the resourcing of a division of officers will have a very different need to a frontline officer, for example. There’s a need to understand locally, from that wealth of data, the information needed to make those appropriate resource planning and activity decisions and shape the inward long term strategic view.”

James West, a former Police Officer who now heads up the Agilisys Policing and Criminal Justice team, agrees, adding that there’s an issue with the people who are inputting this data not necessarily seeing the potential value of what that data means. “They don’t understand, because they haven’t been involved in the development of data tools, how the information they’re gathering can prevent harm, or how it can enforce an arrest.

“Data is like building a jigsaw puzzle. Over time, each piece comes together to build a bigger, clearer picture of what’s happening. That picture can be shaped and made multi-dimensional through AI and all that great tech, but the foundations must be right.”

Data-driven policing strategies

According to Evie, any organisation wanting to unlock the potential of data needs a stable, well-established data strategy that the senior leadership have bought into, and which is understood across the entire organisation.

“I’m not just talking about those in IT, but about everybody and their processes,” she says. “Data-led transformation requires a lot of stakeholders to be aligned; it needs numerous conversations and extensive communication to ensure the whole organisation understands the steps to be taken along the way. Get this right, and it will provide strong foundational platforms capable of interlinking data from different and disparate systems that unlock the ability to do more prescriptive analytics, using historical data to inform the future.”

James says one of the problems with this is the difficulty of defining what data means to policing. Whereas commercial organisations’ aims and objectives are clear – how can they manipulate their data to make more money and/or improve the customer experience? – in policing it’s more challenging.

“There are numerous different lenses,” he argues. “It could be strategic planning, it could be tactical in terms of how resources are used, or it could be operational and the quickfire ‘who’s behind the door’ question. Am I going to save a life by knocking the door down, or will I wait because I might cause more harm and damage than good?”

The give and take of data-driven policing

“Whatever the strategy, there needs to be a give and take when it comes to data literacy,” continues James. “We’re potentially asking more of people at the front end, but what can we give them in return that makes their life easier? How will filling in more fields in a form benefit them?

“I also think we also need to remove any disconnect between users and the people building data-based tools and dashboards. Instead of developing a dashboard for police officers and training them on it, why not start with asking those officers why they need it? What is the value that they will derive from the said dashboard or analytics tool? Before we even start developing, why not involve end-users in conversations as key stakeholders? Their needs can then be embedded from the outset, and the process won’t feel like a checkbox exercise. We need to start with the why and finish with the how.”

Following on from that point, James adds that he believes we’ve reached the point where police and law enforcement needs to take a step back and decide what they want to do with the terabytes of data they have – before control is lost.

“I would argue technology is probably five or six years ahead of data, and there’s a lesson we can learn from the public sector here.

Nearly everyone got it wrong with technology because we got to the point where the tech was driving the business. Yes, we benefit from technology and what it delivered, but it wasn’t business-driven. We don’t want the same thing to happen with data. Instead, we want businesses to drive data-informed, data-driven decision making. That means at a strategic level, we’ve got to get the strategy around what we want to do with data nailed, so it doesn’t send us off on a tangent.”

Kate agrees, adding: “You must start with the problem statements. How can data help us? What are the problems we’re trying to solve? And how can data assist us in that now? Data and related technologies such as AI and automation are not there to take away from people’s jobs or judgment; it’s there to help get one step ahead and enable more informed decisions.

“Ultimately, it’s about ensuring the right people have the right information at the right time, so they have evidence to support their judgement and the professional expertise for the good of policing and the citizens it serves.”

 

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The imperative for data-driven law enforcement