Organizations like ours often launch data governance programs with a clear vision, only to find that measuring data quality consistently is harder than expected. At the City of Ontario, California, we faced the same issues many teams encounter: scattered spreadsheets, inconsistent definitions, and manual checks that made it tough to track trends or demonstrate impact.

This session shares how we addressed those challenges by designing and automating agency KPIs and Data Quality Metrics that connect business rules, system validations, and workflow monitoring into a repeatable pipeline. We’ll walk through how we standardized KPIs, automated calculations across systems, and delivered dashboards that both executives and operational teams actively use—along with the data quality measures that reveal the true health of the data.

We’ll highlight the technical patterns (Snowflake SQL, tasks, quality rules, metadata tables), the governance processes (ownership, thresholds, issue lifecycle), and the lessons learned implementing this solution in a large municipal environment with diverse data sources.

• How we identified gaps in our early data governance efforts and turned them into a roadmap for quality measurement
• The framework we built to automate KPIs and data quality metrics across multiple municipal systems
• Technical patterns using Snowflake SQL, tasks, metadata tables, and rule-based validations
• How standardized business logic, definitions, and workflows enabled consistent KPI reporting
• Governance practices that support sustained adoption: ownership, thresholds, and data issue lifecycle management
• Lessons learned implementing automated data quality monitoring in a large, operational city environment

Speaker: Anthony Nunez, Systems Analyst, City of Ontario

Anthony Nunez is a Systems Analyst with the City of Ontario, California. As part of the Data Team for the city, he supports enterprise data strategy across critical municipal services. Anthony's Data Team duties include designing and maintaining end-to-end data pipelines, semantic models, and quality frameworks that empower departments to trust, understand, and operationalize their data. Anthony helps develop data stewardship training programs and builds automated workflows that increase transparency, accuracy, and reporting efficiency across the many different agencies within the city. Anthony works closely with city agencies to translate complex operational processes into well-structured data practices and repeatable logic. He also leads efforts to standardize business definitions, align system rules, and document institutional knowledge that previously lived only in staff expertise. His work strengthens decision-making across the organization by ensuring that high-quality, well-governed data is consistently available to both technical teams and executive leadership.