When law enforcement first began using predictive analytics years ago, no one foresaw how data analysis would someday be used to predict officer capabilities. It is now also used to predict job performance.
Today, predictive analytics powered by integrated systems can identify patterns in large datasets. As a result, it enables the flagging of individuals who may develop inappropriate behaviors over time. This approach mirrors how, in airline systems component failures are anticipated before they occur. That allows for timely intervention. Similarly, some experts believe that applying predictive analytics to the hiring process can reduce poor hiring decisions. It can also strengthen overall accountability.
Key Takeaways
- Predictive analytics can help law enforcement identify patterns and predict officer capabilities, improving job performance.
- Richmond police reduced random gunfire incidents by 47% through predictive analytics, demonstrating the approach’s effectiveness in resource allocation.
- Analytics alone often provides a limited view and can flag officers incorrectly; a holistic approach is needed for better insights.
- Integrating smart training and compliance solutions with analytics enhances accountability and internal communications within police departments.
- To maximize the value of predictive analytics with holistic technology, law enforcement must ensure high-quality data, innovative thinking, and proper oversight.
Predictive analytics for policing
Predictive analytics is statistical data software used to predict probable criminal activity, but it isn’t new. In fact, crime analysts have been using predictive policing techniques for more than four decades. Predictive analytics was originally designed to enable law enforcement to use resources more efficiently. Specifically, it does this by raising awareness of potential hot spots and likely illegal actions before they occur.
For example, the Richmond, Virginia, police department has used predictive analytics to reduce random gunfire incidents. These incidents were occurring every New Year’s Eve.
In analyzing the data, police were able to zero in on the time, location, and likelihood of random gunfire. By employing their personnel accordingly, Richmond police saw a 47 percent decrease in random gunfire. The department also saw a 246 percent increase in weapon seizures. Additionally, they saw a $15,000 savings in personnel costs.
Today, in the post–George Floyd era, growing suspicion and open rancor toward law enforcement is accelerating the use of analytics to root out potentially bad cops. It is also helping improve accountability and enhance transparency. In particular, it does so by focusing on how police administrators monitor themselves.
Why analytics alone won’t work
Unfortunately, as with any technology, such efforts address only part of the story. The conclusions reached by these platforms often rely heavily on information housed in numerous disconnected systems. Without “good” data, they often flag the wrong officer for the wrong reasons.
In addition, the use of predictive analytics for early intervention and to anticipate officer misconduct offers only one dimension of the problem: probable behavior. It often ignores key contributing factors, such as inadequate training.
What’s needed to improve the use of predictive analytics in law enforcement oversight is a holistic approach to data collection and analysis. This approach should provide a more comprehensive picture of officers confronted with split-second decision-making. It should consider their career-altering implications.
In other words, Why not use data collection to prepare officers for the job, rather than predict how they may react on the job when it is too late?
Predictive analytics are just one part of a holistic system
Adopting a smart training and compliance solution and integrating it with appropriate analytics can help police departments accurately track whether officers have completed the mandatory courses. It can also help ensure that those courses are aligned with actual department policies. Moreover, it can maintain legally defensible certification and compliance records. In this way, it can prove that an officer is authorized to be on the street. Using such solutions can improve hiring and training practices in general. They can also enhance internal communications by standardizing case processes. In addition, these solutions help with management workflow.
Using a best-of-breed technology system with flexibility and internal security protocols also helps supervisors perform better oversight—particularly of disciplinary and separation actions for officers. Additionally, predictive analytics with holistic technology provides better insights.
Compare that with police departments working “old school” by manually maintaining their personnel and service records. In response to a civilian complaint, an inappropriate use-of-force accusation, or worse, it’s difficult to collect and share the pertinent information needed to refute an allegation.
In short, a successful case management system should complement the usage of predictive data analytics by making that data more actionable.
Predictive analytics with holistic technology alone can be misleading. Instead, law enforcement should maximize the value of predictive analytics by ensuring it is supported by the right baseline systems, innovative thinking, and conscientious oversight.
