Empowering Diversion with Data Science, Machine Learning -A Juvenile Court Case Study


The client had diversion data going back to 2016 but they were data rich and information poor.

  • Manual outcome tracking and no effective way of linking diversion outcomes to interventions.
  • No visibility into which interventions were working or not working.
  • Lack of insights resulted in an inconsistency in decision making.
  • Inability to link staff actions to outcomes and understand drivers of outcomes.


eMoksha's Data Science Service-based diversion solution was implemented.

  • Enabled the court to set benchmarks/performance measures, measure metrics and outcomes, and assess the effectiveness of the interventions on the outcomes.
  • Was expanded to include a diversion data entry platform to have rules-based diversion data entry.


The diversion program was completely transformed. A number of program areas were reconfigured and recalibrated such as:

  • Several interventions were adjusted based on better effectiveness of interventions for certain groups.
  • Changes were made in the dosage for each modality of intervention.
  • Several policy areas were reviewed and benchmarked for performance measurement.
  • Staff planning, workload distribution, staff training and staff performance was linked to outcomes.
  • Stakeholders and grant funders were provided evidence of outcome measurements and success.
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