I wrote a Police Analytics article for the most recent issue of the OACP’s H.Q. Magazine. I tried to communicate the value of data to police services and how my Service has approached the challenge.
This article was originally published in the WINTER 2014-15 issue of H.Q. Magazine, the official publication of the Ontario Association of Chiefs of Police.
At the beginning of 2014, Halton Regional Police Service (HRPS) created a new unit with a mandate to exploit police data assets to support evidence-based decision making. Combining scientists, programmers and police experts the new unit serves as a clearinghouse for quantitative analysis and report development for customers throughout the Service. From frontline platoons and investigative bureaus to administrative units and the executive branch this unit, named Police Analytics, has changed the way analytic insights are generated and delivered to in-house customers.
Historically, police services have spent considerable resources gathering data; first in the form of paper documents and subsequently in the form of records management systems. The primary function of this data has been to document activity and support investigations but as these data stores have grown in size and complexity the possibility of conducting large-scale analysis to produce efficiencies and insights has been recognized. The challenge is that the data is not in a format conducive to performing analysis but if this challenge could be overcome then tremendous value would be realized.
Business Intelligence and Police Analytics
Recognizing this opportunity, HRPS chose to invest in a Business Intelligence (BI) software system to better exploit these data assets. Business Intelligence is a combination of hardware and software designed to transform existing data resources into a form that is better suited to reporting and analysis. The work is highly technical but the end result is a single database that contains the entirety of a Service’s data. The HRPS IT department, working closely with our BI vendor, spent over 18 months creating a BI database tuned to the Service’s needs and the final result was unparalleled access to our information. But data alone is not enough, you also need skilled analysts who can intelligently and imaginatively utilize that data to produce results, and those skilled analysts work in our new Police Analytics unit.
Police Analytics was envisioned as a different kind of law enforcement analysis; it isn’t crime analysis or intelligence analysis but is instead a data-centric role that provides quantitative analytical products to all levels of the Service. We realized early on that we wanted individuals with a background in math, engineering or the sciences so that they would be capable of performing complex statistical work. Further, the new analysts needed to be highly technical so that they would be comfortable working with databases and writing software to perform their analysis. This combination of skill sets echoes many of the talents of programmers and developers in the world of tech companies and so that was the model we adopted. To get the best people we decided to hire expertise from outside law enforcement and in so doing we created a tech start up inside the Police.
Challenges for a New Unit
Like any start up, there have been growing pains. Our initial conversations focused on where in the organization to position this new unit and while, from a technical perspective, the unit could logically fit in IT, from an analytical perspective it was decided that the unit should fall under the Chief’s Staff. The access provided by working directly for the executive allows the analysts to have a more direct line to senior command—better to communicate analytical findings and field requests—and working alongside other executive units such as planning, audits and policy meant that the analysts could develop a holistic understanding of how data flows throughout the organization. The placement of a highly technical unit outside of the traditional IT infrastructure was a novel undertaking and providing the needed access for police analysts to do their work meant that policies and practices had to be adapted. Consensus was reached through negotiation and collaboration between departments and we were able to ensure data integrity and successfully address security concerns.
The next challenge was one of branding. We had constructed a high-functioning unit that produced useful analysis but we needed the Service to know about it. To address that issue we started an internal campaign advertising ourselves throughout the Service as the source for statistics and analytical products. We positioned ourselves as a resource for Service members of any rank to get data and advice to support their projects and we emphasized the value of having a solid quantitative foundation to produce successful project outcomes.
Evidence-based Decision Making
Our outreach efforts focused on promoting a culture of data-driven, evidence-based decision making and we encouraged Service members to think about data collection and how subtly adjusting business practices could lead to better data which in turn would lead to better analysis. As an example, our computer systems allow officers to push a button every time they change activity but some officers had gotten in the habit of not pushing the button and this lead to data gaps. To address this issue we communicated to officers how consistently updating their status led to much more accurate activity reporting that better captured all of the work they performed throughout the day. When officers discovered the benefits of pushing the buttons, namely credit where credit is due, they modified their behaviour and adopted a data-driven mentality.
We’ve worked hard to change behaviours and clean up our data sets and we’ve started to see rewards for those efforts. One of our successes has been a project that studied call volume and peak period staffing. Calls for service fluctuate throughout the day but our officers work 12-hour shifts and that leads to peaks and valleys in officer busy-ness. By accurately capturing officer status changes we obtain an extremely detailed view of what patrol officers are doing throughout their shifts and with this level of detail it is possible to broadly categorize officer time as either ‘busy’ or ‘available’. For our analysis, we aggregated hundreds of thousands of time-stamped officer activities to construct daily and hourly profiles of when our officers have the heaviest work load and using that data senior commanders are able to construct additional shifts to move officers to busy times. The end result is a reduction in officer busy-ness during peak periods and a levelling of the overall work load across day and night shifts and because the system is data driven we are able to measure the impact of the shift changes and quantitatively demonstrate our success.
Increased Data Awareness
Beyond analytical work the police analytics unit also studies organizational business practices to identify “pain points”. If a business process is cumbersome or time consuming we brainstorm as a unit how to rectify the problem. This practice has led to the development of a number of reporting tools that repackage existing data assets into more useable formats. A few examples include summary reports of field contacts for front line officers, absence management reports for human resources, and occurrence mapping tools for crime analysis. The point of these reports is not that they contain analysis but that they take existing data that is stored in isolation and synthesize it into an easily read report and where before an individual tasked with reviewing these entities may have spent hours clicking through the RMS they can now see everything in one report.
Perhaps our biggest success in this vein is the introduction of our officer activity report that allows Service members to review their activity. Our RMS captures officer activities such as arrests, charges, and tickets and our new reporting tools allow supervisors to review monthly summaries of this information for their units in a web-based, on-demand format. This reporting tool offers many improvements over the old, self-reported monthlies including accuracy, standardization and time savings. This tool has eliminated the need for members to waste time collecting and collating data that has already been captured in our databases and has resulted in a greater awareness, both for members and for command, of officer activity.
Lessons Learned and the Future
With our success establishing the police analytics unit HRPS has learned a number of lessons that may be instructive to other Services looking to create a data-focused unit:
- You need buy-in from the highest levels of the organization. When attempting to create a data-driven culture of analysis that message needs to be embraced and communicated through management.
- Success is built around people and not technology. Simply buying a piece of software does not solve your problems; you need a small core of dedicated experts who believe in the mission to find success.
- Success takes time and progress will be slow. It is not possible to successfully influence the culture of a large organization quickly and that’s doubly true when the changes are related to a complex matter such as data.
- Change is an iterative process. Only when you start looking will you see how your processes need improvement and once those improvements are in place you’ll need to gather more data and you’ll likely see more necessary changes.
- The unit needs access to senior management. The police analytics unit needs the latitude to communicate proposed changes to senior officers and receive approvals quickly.
HRSP is proud of what our police analytics unit has accomplished in a short time. We have made meaningful contributions to improving the efficiency and effectiveness of the Service and have successfully promoted the value of data-driven decision making in the organization. We also spent 2014 promoting our vision for police analytics to other Services in Canada as well as at conferences in North America and Europe where our ideas have been enthusiastically received. In 2015 we plan to ramp up our software development and update our web-based tools so that officers in the field can access information on their tablets and phones. The volume of data captured by the police is going to keep growing and specialized systems and analysts are needed to extract value from those assets. We believe Police Analytics is the future of law enforcement analysis and we encourage everyone to consider how it might benefit their organization.