Posts tagged "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. Map & Match records it as reviewable metadata.
- The template engine is yours
DeltaVault's data warehouse automation templates are versioned, team-owned, and open to your own AI assistant, 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 the same five steps: context, content, instruct, 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.