An analyst in the probation department presented the CCP’s performance dashboard and asked for feedback on use, frequency and data scope.

Members said better, more frequent data will help target interventions and research collaborations and suggested technical steps for a data warehouse to allow de-identified cross-system matching.

The probation analyst described the current dashboard as populated by manually transferring data from periodic reports into a spreadsheet feeding Power BI. The analyst said the dashboard breaks recidivism into two measures — rearrest and new conviction rates — and tracks cohorts for two years. “When you look at the year on those, it's a cohort ... they enter supervision in a year, and then they're tracked for 2 years,” the analyst said.

Participants recommended improvements: move from quarterly static reports to a regular PDF distribution as an interim step; develop a centralized data warehouse that allows de-identified matching (for example, fuzzy matches on name and date of birth) to reconcile differences across agency reports; and add more disaggregated metrics (e.g., new conviction breakdowns by misdemeanor vs. felony, SRF hits or field-interaction contacts relevant to AB 109 populations).

Specific data issues were raised. Tanya Clark questioned whether the mental-health-court numbers on the dashboard were limited to mental-health court or included diversion programs; the analyst and Tanya agreed to meet offline to reconcile differing counts. Members also noted that some metrics have a probation-centric view and asked for more balanced cross-agency indicators.

Next steps agreed in discussion (nonbinding): circulate the dashboard for review; distribute PDFs more frequently as an interim measure; invite departmental analysts and data leads to a dedicated data meeting to refine requirements; and explore a long-term data-warehouse solution with de-identification and matching.

No formal vote was taken. Several members encouraged outreach to external researchers and university partners who may help analyze county data.