5 things to consider when implementing data-driven policing

There is no shortage of cities that are suffering from a shortage of police officers. San Diego’s ranks are short by 200 officers or more. Agencies in North Carolina are struggling to attract the next generation of officers. And even in cities that are fully staffed, there’s a need to get a bigger return on their law enforcement investment.

Many see data as the answer. With data and analytics, they’ll know where to send officers to make the biggest impact, right?

Maybe, but not necessarily. There is a real danger in blindly trusting data if you haven’t built a solid plan for using it first. Does your data really tell you everything that you think it does? Are there key elements that are missing? Are your assumptions and priorities correct?

SAS, a Council Global Lead Partner, has put together a great checklist of issues to consider before you fully launch a data-driven policing effort. Use it to start your discussions. — Kevin Ebi


By David Kennedy, SAS

Since the 1990’s, law enforcement agencies have been focused on more effectively utilizing data to prevent and solve crime. Today, almost every aspect of policing has been automated and leaves a digital footprint. Key public safety data resides in systems such as computer-aided dispatch (CAD), records management systems (RMS), criminal history, jail, intelligence, and dozens of other databases. But just because the data is captured, it doesn’t mean that it can be utilized by investigators. The key is to provide law enforcement personnel with access to crucial data along with the tools to uncover vital insights.

Police departments today face challenges that make identifying and utilizing data very difficult. First, agencies lack resources, both sworn officers and civilian personnel, to explore the data. Second, the criminal landscape keeps changing. Current issues such as opioids and other narcotics, gangs, and cyber-crime continually force agencies to alter how they fight crime. And third, an influx of new data types changes how analysts perform search and analysis. Unstructured data, social media data, and emerging technologies make it more difficult for law enforcement to uncover clues and prioritize investigative activities. As a result, police need innovative solutions to help them implement effective data-driven policing.

Data-driven policing is a valuable initiative that can improve public safety and police-community relations. Raw data is valuable, but in order to achieve effective data driven policing, the following items should be included in an agency’s strategy:

  1. Data Integration – Providing access to key data regardless of where it is housed or how it is formatted is the first step. A variety of approaches can be considered: ongoing integration jobs to disparate systems or federated searches to data sets; in any event, this data needs to be accessible.
  2. Entity Resolution – The integrated data sets contain people, places, and things in structured and unstructured formats. Investigators need tools that can automatically identify entities hidden within the data as well as solutions to automate the process of resolving each entity into a single record.
  3. Data Quality – Law enforcement sometimes skips the process of data quality because they want to know different information (such as aliases) that an individual provides. However, even basic improvements to the quality of each entity can have a tremendous impact on the investigative value of the data. Standardizing names, addresses, and phone numbers can help analysts identify associations and patterns within the data.
  4. Social Network Analysis – Even the hardest working analyst cannot uncover all the hidden connections available between disparate data sets. Because entities have been resolved across the integrated data, social networks can be built in seconds to uncover links and connections. Investigators can then prioritize investigative activities based on the intelligence.
  5. Operationalize the Analysis – The final, continual step is identifying how the analysis can be utilized to fight key public safety initiatives. Data can be used to combat narcotics distribution, human trafficking, terrorism, gang activity, violent crime, and cyber-crime. The analysis can help understand the problem, determine police tactics, and impact policy decisions.

A final item to remember is that analytics continues to provide additional value as more data is added to the system. Identification of social networks via resolved entities is improved as more databases are integrated, managed, and analyzed. North Carolina experienced tremendous operational value through their data integration project, NC CJLEADS. Federal, state, and local databases have been integrated and the project has resulted in tens of millions of dollars in improved efficiency and enhanced decision making.

The effective use of data by law enforcement agencies can help enhance public safety, operational efficiency, and improve outcomes for the citizens they serve.

David joined SAS in 2014 as a Senior Industry Consultant with the Justice and Public Safety team within SAS' U.S. Government Practice. He works to help justice and public safety agencies utilize data and analytics throughout their organization. David helps agencies improve public safety and justice processes by introducing how analytics can be utilized by officers, analysts, command staff and justice personnel. Prior to joining SAS, David spent eight years working with two technology companies that delivered interoperability and data sharing solutions to criminal justice and public safety agencies. David holds a Bachelor of Business Administration from the University of Miami and a Master of Business Administration from Nova Southeastern University.