Insights

Practical guidance on data governance, AI governance, software architecture, and the future of agentic AI development. Written by Joshua Garza.

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Data Quality

Designing a Data Quality Rules Engine That Scales

Ad-hoc data quality checks collapse under their own weight as data volumes grow. Learn how to design a rules engine with structured rule anatomy, the five core quality dimensions, execution patterns, a maintainable rules catalog, and scorecards that track quality over time.

Joshua Garza

Recent Posts

Data Quality

Data Observability and SLA Monitoring for Data Pipelines

Learn how to implement data observability across the five core pillars, define meaningful data SLAs with business stakeholders, and build an alerting and incident response workflow that keeps pipelines trustworthy.

Topics We Cover

Data Governance

Maturity assessments, policy development, stewardship, data quality culture

AI Governance

EU AI Act, NIST AI RMF, AI system inventory, bias testing, compliance

Software Architecture

Technical debt, modernization, microservices, API design, cloud migration

Agentic AI

Dark factory principles, scenario-based validation, autonomous agents, Digital Twin environments

Data Quality

Data quality rules, observability, monitoring, SLAs, tools and frameworks

Leadership & Strategy

Fractional CTO, data strategy, technology leadership, team building

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