Practical guidance on data governance, AI governance, software architecture, and the future of agentic AI development. Written by Joshua Garza.
Block's use of DataHub's Model Context Protocol Server and the Goose agent framework reveals what enterprise-scale, AI-driven data governance actually looks like in production — and why it matters for every organization managing distributed data ecosystems.
Most data catalog initiatives stall not because of technology, but because of misaligned strategy. Here is how to build one that people actually use.
Most data governance initiatives fail not because of bad tooling, but because no one owns the data. Here's how to build a stewardship program that creates accountability, improves quality, and actually sticks.
Maturity assessments, policy development, stewardship, data quality culture
EU AI Act, NIST AI RMF, AI system inventory, bias testing, compliance
Technical debt, modernization, microservices, API design, cloud migration
Dark factory principles, scenario-based validation, autonomous agents, Digital Twin environments
Data quality rules, observability, monitoring, SLAs, tools and frameworks
Fractional CTO, data strategy, technology leadership, team building
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