Derive It Once, Use It Everywhere: Introducing the Derived Meaning Store
A practical approach for storing LLM-derived meaning so it becomes stable, governed and reusable across your analytics pipeline.
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Writing on data governance, analytics engineering, and building AI-ready data foundations. Focused on practical approaches that connect modern tools to business trust and value.
#DMBOKwithMeagan
Systematic breakdown of DMBOK chapters
Analytics Engineering Today
dbt, Snowflake, and data modelling
Two Meanings of "Semantic Layer" and Why Both Matter in the Age of AI
How analytics and knowledge management are converging in the AI era. Exploring the analytics meaning vs the ontology meaning of semantic layers.
Read articleSemantic Layers, MCP Servers, and the Fear of Non-Determinism
Exploring if, how, when, and where GenAI actually belongs in Data and Analytics through semantic layers and MCP servers that bring determinism back to LLM-powered queries.
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