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Case Study

Improving Sentencing Guidelines in the District of Columbia through Predictive Analytics

Client Challenge

The District of Columbia (DC) Sentencing Commission (SCDC) conducts annual reviews of DC sentencing data, policies, and practices by researching sentencing policies and practices both locally and nationally. SCDC then makes recommendations to the DC City Council to increase the fairness and effectiveness of sentences in our nation’s capital.

In support of its mission, SCDC required an enterprise-wide, analytics-driven case management system to analyze, visualize, and conduct trend analysis on large volumes of criminal justice data; conduct predictive analytics to predict future sentencing trends; and integrate with other DC and federal law enforcement agencies.

Our Solution

Mindcubed was engaged to design, develop, and implement the new case management system. We employed an agile development approach using a Scum framework and cross-functional teams of Mindcubed and client personnel. As part of this highly collaborative process, we led more than 1000+ daily standup meetings, developed 1500+ user stories, conducted 40 sprints, and delivered 5 major system releases.

The SCDC’s work is highly analytics driven, with large volumes of data for conducting research and analysis and generating policy recommendations. We built extensive reporting and analytics functionality into the solution, including the ability to automatically encapsulate and visualize data on role-based user dashboards. We also integrated the system with other law enforcement systems using a services-based approach.

Using R Code and IBM’s SPSS, we incorporated modeling, simulation and predictive analytics capabilities into the system providing the SCDC the ability to analyze historical case data and accurately anticipate future sentencing trends

Key Results

With the new case management system, SCDC has the tools it needs to monitor judicial compliance with City sentencing guidelines, and to promote fair and consistent sentencing within the District of Columbia. SCDC now uses automated visual and predictive analytics capabilities to anticipate sentencing trends and provide up-to-date, fact-based policy recommendations. The advanced, services-oriented architecture (SOA) based integration with other criminal justice information systems, provides the current and historical data needed by SCDC to perform its work, and a scalable platform to easily integrate with additional systems in future.
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