Posts tagged "data-governance"
- What the agent reads
An AI agent's answer is only as good as what it reads, and every platform keeps its semantic layer for AI agents inside its own walls. Write the meaning first.
- Context for AI, context by AI: metadata needs a confidence score
Why AI-generated metadata needs a confidence score: business context for AI comes from people building the conceptual data model; a local LLM still guesses.
- Keep AI-generated metadata marked after you accept it
Accepted AI-generated metadata keeps its data provenance: AI badge, confidence score and rationale stay in the data catalog so data governance sees each guess.
- A Model You Can Diff: Metadata Version Control for Data Teams
Pipeline code gets code review and an audit trail; metadata doesn't. Five metadata version control practices bring naming conventions under data governance.
- Top-down and bottom-up data modeling: cart before the horse
Top-down and bottom-up data modeling: join the conceptual data model and business glossary to source system discovery, so staging tables carry data governance.
- The Mapping Is the Deliverable
The source to target mapping that turns a source system into the business model is usually never written down. Record it with who decided, when, and why.
- The template engine is yours
DeltaVault's data warehouse automation templates are plain Jinja, versioned and team-owned, so your standards live in templates you control.
- AI that explains how reality became data
AI governance for data platforms starts with one loop. Every DeltaVault AI action follows five steps: context, content, describe the outcome, review, refine.
- Manage data, not plumbing
Declare table metadata once; DeltaVault generates create-table, merge and workflow code for Databricks, Snowflake or Fabric. That is data warehouse automation.
- Your Database Is Not Your Business
AI can infer a semantic layer from your technical exhaust. It cannot define your business ontology. Why a system of record is not a system of meaning.
- No Human, No Loop
Human in the loop puts a person at the end of an AI process as a checkpoint. In DeltaVault the human starts the loop, so there is no loop without one.
- AI does not fix bad data models
AI data modeling aimed at your source schema returns a faster, shinier source schema. Business meaning comes from your own material, and sources map into it.
- Give AI Content, Not More Prompts
Upload, consolidate, and research business content into DeltaVault's AI knowledge base, then review every draft before it shapes your business model.
- AI-native, not AI-added
What separates an AI-native data catalog from a bolted-on chat panel: governed writes, and AI skills you can open, edit, and extend.
- Meaning before machinery
AI made pipelines cheap to build. The scarce thing now is business context for AI: what your business means. DeltaVault captures it once and builds from it.