AI-Driven Data Profiling Framework in Microsoft Fabric 

Leverages AI to analyze data patterns, detect anomalies, and recommend quality rules. 

Introduction: Where Data Quality Truly Begins

Data quality issues don’t start in dashboards— they are first detected during data profiling. Data readiness directly impacts the accuracy and trustworthiness of insights. When profiling is continuous, teams detect issues early, accelerate onboarding, and build trust across every downstream insight. 

In many organizations, data profiling is a manual, one-time onboarding task. Teams run basic checks, review results, and move on. As data changes, these insights quickly become outdated, allowing quality issues to slip silently downstream. 

This blog introduces an AI-Driven Data Profiling Framework built using Microsoft Fabric that continuously analyzes data patterns, detects anomalies, and proactively recommends data quality rules. 

Traditional profiling approaches: 

• Are manual and time-consuming 
• Capture only a snapshot in time 
• Miss subtle behavioral changes in data 
• Do not scale with growing data volumes 

As datasets grow and schemas evolve, manual profiling becomes reactive and ineffective. 

By the end of this article, you will understand

• Why traditional data profiling fails at scale 
• How AI transforms profiling into a continuous capability 
• Core components of an AI-driven profiling framework 
• How Microsoft Fabric enables intelligent data profiling 

What: Leverages AI and statistical techniques to analyze data patterns, detect anomalies, and recommend data quality rules automatically. 

Business Benefit: Accelerates profiling, enables proactive quality control, and improves trust and analytics readiness. 

Instead of reacting to data issues, teams gain early visibility into changes and risks.

• Profiling Engine 
  Analyzes column-level statistics such as null rates, ranges, formats, cardinality, and distributions. 
• AI Pattern Detection 
  Uses AI and statistical methods to identify anomalies, outliers, and hidden data patterns missed by manual checks. 
• Rule Recommendation Engine 
  Automatically suggests data quality rules (not-null, range, uniqueness, patterns) that can be directly applied to the Data Quality Framework. 
• Drift Detection 
  Continuously monitors schema changes, volume shifts, and distribution drift to flag issues early. 
• Metadata Integration 
  Pushes profiling insights into the central metadata repository to keep rules, tests, and governance aligned. 
• Insights & Visualization 
  Provides dashboards showing data health trends, anomalies, drift history, and rule adoption metrics. 

AI-driven profiling: 

• Learns from historical data behavior 
• Scales across thousands of tables 
• Detects issues humans overlook 
• Enables proactive governance 

Profiling evolves from one-time exercise into an ongoing capability. 

• Large-scale data onboarding 
• Schema evolution monitoring 
• Early anomaly detection 
• Automated quality rule generation 
• Enterprise data governance initiatives

The AI Data Profiling Framework works together with: 

• Metadata-Driven Framework 
• Data Quality Framework 
• Test Automation Framework 

Profiling insights drives quality rules, automated testing, and trusted analytics in a closed loop. 

Modern data platforms should not wait for failures to occur. 

By combining AI-driven profiling with Microsoft Fabric, organizations move from reactive fixes to proactive prevention — ensuring reliable, analytics-ready data. 

In intelligent data platforms, AI doesn’t just analyze data — it protects it. 

Why this matters now: As enterprises consolidate data estates on Microsoft Fabric, profiling becomes a strategic capability—not a checklist step. A repeatable, intelligence-led approach keeps quality signals current as your data evolves. 

AI-Driven Data Profiling Framework is one of the key frameworks of Evoke’s Microsoft Fabric Data Accelerator tool—helping organizations move faster from raw ingestion to governed, analytics-ready data with confidence. 

Explore what’s possible with Evoke’s Microsoft Fabric Data Accelerator: Bring continuous profiling, rule recommendations, and drift monitoring into your Fabric programs to reduce rework and speed up value realization.