1. Define an AI Data Product
From “data as asset” → “data as product” → “AI data product” (GenAI signals + agent-managed freshness).
Core Characteristics (DATSIS) — upgraded for AI
2. Build: Pipelines + GenAI + Agents
Engineer the product surface, then add agentic operations that keep it current, compliant, and stable.
The AI Data Product Enablement Team
AI Data Products require cross-functional skills across data engineering, evaluation, governance, UX, and agentic operations. Use the role mix below to compare staffing.
Role Focus:
Select a role to see details.
3. Evaluate Fit & Utility (with AI)
Test signal validity + user utility, and verify GenAI outputs are reliable, explainable, and cost-effective.
Criterion 1: Signal Validity (Is the “Chocolate Bar” Real?)
When we combine raw data into an abstracted indicator (e.g., “Device Health”), we must ensure the abstraction matches reality. For AI data products, that also means checking the *generated/derived* parts: are GenAI enrichments stable, grounded, and consistent? A good signal stays stable when nothing changes, reacts when something truly changes, is explainable (“which ingredients drove it?”), and is verifiable against outcomes.
Criterion 2: User Utility
Does it solve a real problem? Select utility drivers. For AI data products, include “trust + explainability” and operational readiness so users aren’t surprised by changes.
Product Fit Assessment
Based on signal + utility, here’s a recommendation for what to do next.
ADJUST INPUTS
Interact with the tools above to get a strategic recommendation.
4. Validate, Launch & Keep Fresh
Trustworthiness + consumability + discoverability—plus agent-driven freshness, drift response, and continuous QA.
Trustworthy
Grounded generation, evaluation suites, and data/LLM quality gates—continuously monitored by agents.
Consumable
Explicit contracts + schemas + docs, plus stable endpoints and versioning for data, prompts, and models.
Discoverable
Catalog + “AI Card” metadata: what is generated vs sourced, grounding rules, evals, owners, and SLOs.