How the third-largest US county uses Elastic to accelerate investigations
As law enforcement organizations benefit from readily available online information and databases, they also face the challenge of combing through vast amounts of information, often housed in siloed systems and databases. Across the US, law enforcement teams in state and local government are turning to solutions such as Elastic to save time and improve accuracy as they use data for investigations.
Harris County, Texas, is one such government seeing success with accelerated, consolidated investigative search. Harris County is the third-largest county in the US by population, and it includes most of Houston. With over 70 law enforcement agencies and over 50 fire agencies, the county conducts a large number of investigations, where time is of the essence.
Shing H. Lin, CTO Harris County’s Central Technology Services, developed the Law Enforcement Network Search (LENS) tool, built on Elastic, to help law enforcement agencies in the county quickly find the time-sensitive data they need.
How do we correlate data so law enforcement can find it quickly and easily?
When conducting investigations, law enforcement agencies had to sift through siloed technology and systems, including court data, records management systems (RMS), citations, computer aided dispatch (CAD), license plates, and more.
Typically, teams would perform searches in each database separately, then manually link the data from the different searches together. This process was time-consuming, prone to error, and slowed down critical investigations.
Law Enforcement Network Search (LENS) built on Elastic
Initially, when Lin and his team experimented with technology to solve their problem, they found that other products’ licensing limited the number of data sets they could use. They instead turned to Elastic, which was able to pull data from all the sources they needed.
The team built an application that layers on top of Elastic core capabilities, providing an easy way for law enforcement to search across multiple data sources and correlate related information. A single search for a person (such as “suspect male teardrop tattoo”) can pull up 911 call transcripts, an arrest record, associated incidents, and all locations and people linked to the suspect — all in a single view. Within an incident record, users can drill down into 911 call notes, an entire narrative report and supplements, and CAD call details.
Using machine learning (ML) capabilities, the tool is even able to provide recommendations of similar cases, helping agents decrease investigation time by focusing on only the most relevant information.
Users can also conduct a map-based search, which localizes information to a specific precinct or area, again reducing time needed to sift through data.