A records supervisor's hunch about a few offense codes turned into a two-and-a-half-year data cleanup — and a reporting process that catches the next one.

The Situation

After joining the department, a new crime analyst noticed discrepancies in the categorization of several offenses. The department initially expected that only a handful of codes would require correction, but leadership wanted to address the issue proactively. They asked Insight Analytics to review the agency's statute and offense codes and determine whether its coding documentation, reporting practices, and training materials were accurate and up to date.

The Approach

Insight Analytics began with an initial assessment of the department's data. This type of preliminary review helps establish the scope of a project while giving an agency a clearer understanding of where potential problems may exist.

The department provided what it understood to be its statute table, which Insight Analytics cross-referenced against its offense data and applicable incident-based reporting requirements. To evaluate how the codes were being applied in practice, Insight Analytics also requested case-level reports and reviewed narratives associated with potentially misclassified offenses.

The department had developed strong internal reports and was consistently submitting data to the state. However, the assessment uncovered several underlying coding and system-relationship issues that required closer examination.

What We Found

The review indicated that the department's original statute list appeared to have been inherited during a prior system implementation rather than developed specifically for the agency. Some of its codes were inaccurate or were not properly aligned with California Department of Justice reporting requirements.

The audit was particularly complex because the primary CJIS classification code was not contained in the department's statute table. Determining how individual incidents should have been classified required a forensic review that combined statute records, offense tables, system relationships, and case narratives.

The review identified several recurring issues, including:

• Aggravated assaults and simple assaults that had been incorrectly classified, including simple assaults overcoded as aggravated assaults
• Incidents classified as motor vehicle theft that were more accurately categorized as embezzlement or fraud
• Vehicle-theft reports initiated by the city even though the vehicles had been stolen in other jurisdictions and recovered locally
• Additional inconsistencies in the relationships among statutes, offense codes, and reporting classifications
• A vendor-provided monthly reporting tool that generated crime totals without applying CIBRS standards correctly

The Outcome

Insight Analytics provided the department with a case-level correction list covering approximately two and a half years of records, along with a cleaned and updated statute table and supporting training documentation.

The engagement also produced practical tools designed to improve accuracy and reduce the amount of manual work required each month:

• A push-button monthly crime report that expanded the department's existing one-page report into a four-page analytical briefing
• Month-over-month and year-over-year comparisons
• Five-year crime trends
• Internal performance gauges selected by the department
• An XML extraction tool that converts system output into an audit-ready worksheet
• A searchable data dictionary showing whether a statute is a Group A or Group B offense, its correct CJIS classification, and other information needed for accurate reporting

What began as a question about a few offense codes ultimately gave the department a more accurate understanding of several years of crime data — and a stronger reporting process going forward.

Why It Matters

Crime statistics are only as reliable as the coding structure beneath them. An agency can submit reports consistently and still produce inaccurate results when inherited statute tables, system relationships, or vendor tools do not reflect current reporting standards.

This project did more than correct individual cases. It gave the department a validated coding foundation, clearer training resources, stronger quality-control tools, and a repeatable reporting process. The result was greater confidence in both the department's historical data and the numbers it would use to guide future decisions.