Idea
A built-in Fabric feature where users can enable Data Quality Watch for any dataset, with automated monitoring — no code required.
🔧 Key Capabilities
📊 Quality Rules — Auto & Custom
Missing values detection
Duplicate record checks
Data type & schema drift detection
Out-of-range / threshold alerts
Primary key & foreign key validity
Distribution shift detection
Nulls in key business fields (e.g., CustomerID, Amount)
Option to add custom rules using DAX or Power Query UI
📈 Data Quality Score
A model-wide 0–100 score calculated based on:
Completeness ✅
Accuracy ✅
Consistency ✅
Validity ✅
Timeliness / Freshness ✅
Display score trend over time
Highlight areas with most impact on score
Automated Insights
Copilot explains findings:
"Column email has 8% invalid values — common typo patterns detected like .con, missing @."
"Sales amount shows extreme values, likely data input error."
Real-Time Alerts
Email / Teams notification
Power BI alert integration
Fabric event triggers for pipeline correction
Example alert:
"Customer table completeness dropped to 94%. Null customer IDs detected in last batch."
Wizard Setup
User clicks: Enable Data Quality → Select Rules → Schedule → Save
No coding, just UI dropdowns:
Frequency: hourly / daily / per refresh / live
Sensitivity levels
Self-Learning Rules
System suggests rules based on historical data patterns:
"Phone number pattern inconsistent"
"New values seen in Country column — check validity"
Auto-Fix Recommendations
Not just alerts — intelligent suggestions:
| Missing product names | Fill using lookup table |
| Outlier sales values | Flag for review |
| Wrong date format | Standardize automatically |
Output
A Power BI-like dashboard showing:
Data quality score
Trend line
Failed rule summary
Affected tables & rows
Suggested fixes
Last check & next schedule
Benefits
| Manual data quality scripts | One-click automated quality checks |
| Hard to detect silent data issues | Alerts + score + trend |
| Late discovery of bad data | Freshness & schema drift monitoring |
| Tech-heavy validation | Low-code, business-friendly UI |
Future Enhancements
✅ ML-based anomaly detection at refresh
✅ Data quality benchmarking by industry template
✅ Export rules as YAML / JSON
✅ Integration with Fabric Data Pipeline error handling
✅ Score impact synced to Lineage View & Workspace Health
LinkedIn-Ready Caption
Building reliable analytics starts with clean data.
Fabric needs a Low-Code Data Quality Monitor that gives every dataset a Health Score, auto-detects errors, alerts users in real-time, and recommends fixes.
Data quality shouldn't require scripts — it should be as easy as turning on Power BI refresh.
