Posts tagged "ai-native-platform"
- Who is the data model for?
A data model for AI isn't a new table shape. The model did two jobs: a diagram for people and a build instruction for machines. AI retires the second.
- 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.
- The economics of meaning flipped
Stated meaning now costs a sentence, not a table. What an ontology adds to AI accuracy on the benchmarks, and where the AI still has to stop.
- 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.
- 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.
- This is why we need data engineers
Will AI replace data engineers? We built Ask AI into DeltaVault, then recorded it losing a race to a data engineer building one view by hand.
- AI drafts the tedious part. You keep the decision.
AI-assisted data modeling in DeltaVault: skills draft keys, joins, views and descriptions as proposals you read first. Nothing writes until you accept.
- Meaning Leaks at Every Handoff
Business definitions in one tool, the technical data model in another, lineage in a third: meaning leaks at every handoff. DeltaVault keeps them in one model.
- 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.
- You already own the stack
You already own the lakehouse, storage, and orchestration. The missing layer is data warehouse automation: business meaning in, Data Vault and workflows out.
- 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.
- Should Software Be Free?
Free software still burns hosting, AI tokens, and human hours. A working position on honest software pricing and a free tier for the Data Vault community.
- Show Me Your Silver Layer
Any AI tool can land data in your bronze layer. The medallion architecture's real test is an integrated silver layer, and that is a people problem.
- 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.
- The Meeting Is the Model
Turn a Teams meeting transcript into a conceptual data model in one workshop run, with every proposal traceable back to what was said in the room.
- AI Data Modeling: Enough Talk. Let's See It in Action
The AI business modeling workshop drafts concepts, relationships, and facts from your own content, shows its reasons, and writes nothing until you apply.
- How to stop the AI Guessing
The AI business modeling workshop checks your stories before it proposes a single concept. Here are the rules it applies.
- Where, Oh Where, Has Integration Gone?
An AI can model every source you own and still leave your warehouse un-integrated. Data integration is a decision about business keys, and AI can draft it.
- 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.
- Built, Not Painted
When the source does not hold the model's shape, AI helps build the bridge: discovered relationships, drafted views, and column-level lineage end to end.
- 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.
- Build or buy: the cost AI hides
Build or buy a data platform? With AI-assisted coding, building your own looks cheap. The code was never the expensive part: owning it is.
- Business context is not an AI prompt
Business context for AI in DeltaVault is not what you pasted into the chat box. It is a governed setting every AI skill reads, written once in your own words.
- 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.