Article by Jim Schleckser, CEO, Arx

The job of the police chief has gotten more complex as the political and regulatory environment has changed.  Particularly concerning is the ability to manage a distributed workforce in the form of the officers in the field.  Leaders are dependent on these officers to make quality decisions that can impact the citizens, themselves, and the political leaders.  This can involve thousands of interactions with citizens across all conditions possible and their performance is critical to the success and reputation of the police force.  

Given this complexity, how can a police chief manage the thousands or more interactions with citizens across their force and know which officers are their performers and which have red flags that need attention?

The Current Approach Isn’t Working

The historical approach has been officer reputation, feedback from supervisors, a limited reading of call reports, and citizen complaints. This piecemeal data provided a less than complete view of the officers and allowed low performers or problem officers to continue to exist in the force.  Worse, since the data was hearsay and piecemeal, it was nearly impossible to build the case needed to remove an officer from the force until something tragic happened. Those incidents cover the front page of newspapers and lead the nightly news locally and nationally.

We have seen how the behavior of one officer can bring into question the entire force, including lots of good officers that perform at a high level every day.  That has brought down police chiefs and mayors, and put into question the approach for public safety.  This has driven a national dialogue on defunding the police and the threat of legislation that may permanently change the face of public safety.

Excessive Amounts of Data Require Help from Technology

A big part of policing today is collecting large amounts of data on every interaction. So much data that it is impossible for a supervisor to analyze it to find trends.  A digitized information system is needed that puts all the data on one screen for the police chief, combined with artificial intelligence that finds the red flags around at-risk officers and makes these obvious and actionable. This can shift performance management by the chief from reactive to proactive.

Let’s take a look at some of the key data that can be used to help supervisors identify performance issues:

  1. Citizen Complaints – Citizen complaints are one element of this robust information system.  Citizens have become more willing to record and report concerning officer behavior and hopefully, they transmit that information to the police chief before the local news.  As a famous management thinker once said, “When there is one customer complaint, there are nine that didn’t say anything.”  These citizen complaints are the tip of the iceberg for identifying problem officers and need to be elevated, even if they are not substantiated.
  2. Calls for Service – Call reports form the second leg of the information system for the chief.  Unfortunately, many organizations are using paper reporting for these call reports and that makes recovery and analysis almost impossible.  It is a simple matter to digitize the call reporting process, saving time for the officers, and bringing all the data together in one place. 
  3. Use of Force – Use of force reports are another key data source for the chief and even in a moderately sized force, the chief should be aware of use of force in the field.  The key for identifying problem officers is where they go on the force continuum for a roughly equivalent situation.  Some officers can resolve most situations with a commanding voice and a less secure officer might unholster their firearm.  A chief needs to be aware of those officers that move up the continuum rapidly as they are candidates for training or other forms of intervention.

In an ideal system, all this data and other important sources such as personnel records are aggregated and presented to the chief on a continuous basis.  It can also form the base for a community transparency dashboard with the personally identifying information removed.  Alerts and reminders can be set to drive performance, and these can be sent to supervisors for follow-up and action.

Proactive Police Performance Management is Possible

The complexity of a system providing all that data, alerts and follow-up can be overwhelming if there isn’t suppression of unimportant data and elevation of critical data. Artificial intelligence can do both.  Normal patterns of behavior that don’t require action can be suppressed to not show alerts.  Critical incidents can be pushed to the top of the screen for the chief and demand action.  Perhaps more interesting is the ability of the artificial intelligence models to sift through the millions of pieces of data and identify patterns that might not be obvious at first glance.  When a chief can be identified of these patterns, they can anticipate problems, predict issues, and tackle them before they become serious.

There is a way to take control over the complexity, fast pace and lack of information facing a police chief as they respond to their environment.  A predictive, artificial intelligence driven data system can provide a chief with the data to understand their force and proactively manage to mitigate risk and improve accountability.

Check out these success stories from agencies using transparency and accountability management to build trust with their communities