Object Comment Intelligence and Governance Focus Analyzer
Description
This recipe transforms everyday user comments and questions on catalog objects into actionable governance insights. By filtering out system noise, cleaning comment text, mapping comments to their respective data assets, and applying AI-based intent categorization, the recipe highlights what users are actually struggling with—whether it’s data quality, unclear definitions, access issues, or trust concerns. The result is a prioritized, object-level view of governance focus areas driven by real usage and feedback, not assumptions.
Details
User comments and questions on data assets contain rich signals about data quality gaps, unclear definitions, access issues, and trust concerns. Today, this feedback is scattered and manually reviewed, making governance reactive and assumption-driven. This recipe structures collaboration data and converts it into clear, object-level governance focus areas, enabling proactive and data-driven prioritization.
Business value
- Transforms collaboration feedback into measurable governance inputs.
- Reveals dominant issues per data asset using real user signals.
- Improves governance prioritization accuracy and impact.
- Reduces manual review of comments and questions.
- Drives accountability for metadata, quality, and access remediation.
Who benefits
- Data Governance Teams
- Data Stewards
- Data Owners
- Platform Administrators
- Catalog Adoption Teams
What you receive
How the recipe works (guided flow)
Step 1 — Collect object-level comments
Ingest all user-generated comments and questions linked to real catalog objects, excluding system alerts and wall-level noise.
Step 2 — Extract core comment signals
Create a focused dataset with user, object type, object ID, and comment text to reduce noise and improve classification accuracy.
Step 3 — Clean and normalize comment text
Remove HTML tags, encoded characters, formatting artifacts, and non-informational content to retain true user intent.
Step 4 — Resolve catalog object context
Join comments with catalog metadata to enrich each record with object name, connection, and stewardship context.
Step 5 — Categorize user intent using AI
Classify each comment into governance-relevant categories such as data quality, definition clarity, access issues, trust concerns, usage questions, or enhancements.
Step 6 — Aggregate insights per object
Group comments by asset to identify dominant issue types and derive a recommended governance focus area.
Step 7 — Visualize governance signals
Produce tables and charts showing comment distribution by category for fast assessment and planning.
Sample insights (illustrative)
| Insight Category | What the recipe discovered | Business Impact |
|---|---|---|
| Definition clarity issues | Majority of comments on a sales table relate to unclear metric definitions. | Prioritize glossary enrichment and steward review. |
| Data quality complaints | Repeated comments highlight missing and inconsistent values. | Trigger data quality remediation and monitoring. |
| Access-related questions | Users frequently ask how to request access to a finance dataset. | Indicates need for clearer access workflows and ownership visibility. |
Make sure the following ingredients are available in your workspace:
- Connected datasets are crawled and profiled
- Columns have distinct counts and top values generated