AI Readiness Assessment and Data Governance Workflow
Description
This ETL workflow orchestrates a comprehensive data governance and AI readiness assessment by ingesting metadata from source systems, performing transformations to detect PII columns and masking status, lineage analysis, and column quality profiling. It enforces schema validation by filtering temporary and backup tables, calculates governance flags based on ownership, description, profiling, and data quality indices, and derives discoverability and content quality scores through weighted signal aggregation. The pipeline culminates in an AI readiness decision tiering with remediation priorities, supported by detailed explanations for governance, privacy, and data quality gaps, enabling prioritized data stewardship and compliance actions.
Details
Preparing enterprise data for AI requires more than just accessible datasets—it demands strong governance, metadata completeness, privacy protection, and high-quality content. This recipe automatically evaluates every table across these dimensions, producing AI readiness scores, governance assessments, and prioritized remediation recommendations so organizations can confidently identify trusted data assets for analytics and AI initiatives.
Business value
- Measures AI readiness using governance, quality, privacy, and metadata signals.
- Identifies governance and compliance gaps before AI adoption.
- Prioritizes remediation efforts using automated readiness tiers.
- Improves trust, discoverability, and usability of enterprise data assets.
Who benefits
- Data Governance Teams
- Data Stewards and Data Owners
- Privacy & Compliance Teams
- AI, Analytics, and Data Engineering Teams
What you receive
How the recipe works (guided flow)
Step 1 — Base Metadata
Collects core metadata including ownership, descriptions, usage, and profiling information while excluding temporary and backup tables.
Step 2 — PII Detection
Detects sensitive columns using predefined PII patterns and evaluates masking coverage to determine privacy risk.
Step 3 — Lineage Signals
Identifies upstream and downstream lineage relationships to measure each table's connectivity within the data ecosystem.
Step 4 — Usage Signals
Normalizes row counts, usage metrics, and column counts to prepare reliable inputs for scoring calculations.
Step 5 — Column Quality
Calculates profiling coverage and structural quality metrics to assess metadata completeness and data health.
Step 6 — Governance Flags
Evaluates descriptions, ownership, profiling, and data quality to assign governance strength indicators.
Step 7 — Discoverability Score
Measures how easy it is for business users to find and understand a data asset. The score is calculated from five signals: a meaningful description, an assigned owner or steward, a readable table name, a recent data population date, and usage activity. These signals are combined into a score from 0 to 100 and then grouped into High, Medium, or Low discoverability.
Step 8 — Content Quality Score
Measures how complete and business-ready the table is. The score is calculated from five signals: strong description quality, good discoverability, profiling coverage, assigned ownership, and presence of a governance status. These signals are combined into a score from 0 to 100 and then grouped into High, Medium, or Low content quality.
Step 9 — AI Readiness Decision
Assigns an AI readiness tier and remediation priority using governance, quality, privacy, and discoverability signals.
Step 10 — Readiness Explanation
Generates a business-friendly explanation describing why each table received its readiness classification and recommended next steps.
Sample insights (illustrative)
| Insight Category | What the recipe discovered | Business Impact |
|---|---|---|
| Governance Readiness | Several business-critical tables lacked ownership, descriptions, or recent profiling activities. | Improving governance increases trust and accelerates AI adoption. |
| Privacy Compliance | Sensitive columns containing PII were identified with incomplete masking coverage. | Supports privacy compliance and reduces AI-related data exposure risks. |
| AI Readiness | High-quality governed datasets were distinguished from assets requiring governance review or data cleanup. | Enables teams to prioritize trusted datasets for AI, analytics, and business initiatives. |
Make sure the following ingredients are available in your workspace:
- Connected datasets are crawled and profiled
- Columns have distinct counts and top values generated