Low-Code Data Quality Monitor

[Dhanujaya]

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

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📈 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:

Issue Recommendation

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

Problem Today Feature Fix

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.