When you select a table on the Intelligence page, you see its Object Profile: an AI-generated analysis that combines schema metadata with context from freshness, data quality, schema drift, tags, and lineage into a single view. The profile is only as useful as the monitoring you’ve set up. A table with freshness schedules, data quality metrics, and schema drift monitoring will have a much richer profile than one with no monitoring at all.Documentation Index
Fetch the complete documentation index at: https://docs.anomalyarmor.ai/llms.txt
Use this file to discover all available pages before exploring further.
What’s in a Profile
Object Summary
The top card shows the table’s identity at a glance:- Name and path:
warehouse.gold.fact_orders - Warehouse role: Classified as Fact Table, Dimension, Staging, Raw, or other patterns
- Confidence score: How confident the AI is in its analysis (based on available context)
- Summary: One-paragraph explanation of what this table is for
- Business context: What team uses it, what process populates it, what it means to the business
- Domain and tags: Business and technical classifications applied to this table
Findings
Findings are the most actionable part of the profile. They surface issues and observations pulled from across your monitoring domains, grouped by severity:| Severity | Example |
|---|---|
| Critical | ”Column order_total was removed on Tuesday. 3 downstream tables reference this column.” |
| High | ”Table is 6 hours overdue for update. SLA threshold is 2 hours.” |
| Medium | ”Null rate on email increased from 2.1% to 12.4% over the past week.” |
| Low | ”Table has no freshness monitoring configured.” |
Relationships and Lineage
Shows how this table connects to the rest of your data:- Foreign key relationships: Detected column references to other tables
- Lineage signals: Whether this table is a source, derived, or staging table
- Schema-level lineage: Which schemas act as sources vs. consumers
- Hub tables: Central entities that many tables reference
Change History
A timeline of recent schema changes detected on this table:- Column additions (green)
- Column removals (red)
- Type modifications (amber)
Analysis Context
Shows which monitoring domains have data available for this table. The agent uses this context when answering questions:| Domain | What It Provides |
|---|---|
| Schema | Table structure, column types, constraints |
| Tags | Business classifications, PII labels |
| Freshness | Update patterns, SLA status, learned schedules |
| Rules | Alert rule configurations and firing history |
| Data Quality | Metric trends, validity rule results |
Generating Profiles
Single Table
- Navigate to a table in the Schema Explorer
- Click Analyze Table in the top right
- Wait for analysis to complete (typically 10-30 seconds)
- The profile appears with all available context
Entire Schema
- Select a schema in the Schema Explorer
- Click Analyze Schema
- All tables in the schema are analyzed
Full Database
- Select the database root in the Schema Explorer
- Click Analyze All
- Every table across all schemas is analyzed
Making Profiles More Useful
The quality of a profile depends directly on what monitoring you’ve set up:| What You Set Up | What the Profile Gains |
|---|---|
| Freshness schedule | Update patterns, SLA violation findings, staleness history |
| Schema drift monitoring | Change timeline, removed column warnings, drift findings |
| Data quality metrics | Trend data, anomaly findings, null rate tracking |
| Tags | Business context, PII classification, domain grouping |
| Alert rules | Alert coverage analysis, firing history context |
Editing Descriptions
AI-generated descriptions are a starting point. You can edit any description to add context the AI can’t infer:- Business context: “Used by the finance team for quarterly reporting”
- Data sources: “Populated by the Stripe webhook integration”
- Update frequency: “Updated in real-time as orders are placed”
- Caveats: “Does not include cancelled orders before 2023”
Common Questions
What’s in an object profile?
A summary of the table’s role, AI-generated business context, findings grouped by severity, foreign key and lineage relationships, and a timeline of recent schema changes. Every section is grounded in your actual monitoring data, not inferred from the table name alone.Why are my profiles sparse on findings?
Profiles are only as good as the monitoring feeding them. A table with freshness schedules, data quality metrics, and schema drift monitoring produces rich findings. A table with no monitoring produces mostly schema-level context and “consider enabling X” suggestions.How do I generate a profile for many tables at once?
Select a schema and click Analyze Schema, or pick the database root and click Analyze All. Analysis runs in the background and profiles update automatically as each table completes.Do my edits to a profile’s description survive re-analysis?
Yes. Manual edits to descriptions are preserved across future runs. Use this to add business context the AI can’t infer, like which team owns a table or how it’s populated.What does the confidence score represent?
It reflects how much monitoring context was available when the profile was generated. More freshness history, drift detection, metrics, and tags produce higher confidence. Tables with only schema metadata score lower.Next Steps
Ask Questions
Use profiles as context for AI-powered Q&A
Walkthrough
See profiles in action during a real debugging scenario