Microsoft Purview: Taming Dirty Data - Who Wins in Microsoft Fabric?

Key insights

• Video summary: This YouTube video explains how Microsoft Purview and Microsoft Fabric work together to find and fix dirty data in your analytics pipelines.
  
• Microsoft Purview: Purview automates data discovery, classification, lineage tracking, sensitivity labeling, and compliance checks (GDPR, HIPAA, CCPA) across cloud and on-premises sources.
  
• Microsoft Fabric: Fabric handles data ingestion, cleansing, enrichment, modeling, and analytics in one platform and connects to BI tools like Power BI and Synapse.
  
• CluedIn and fuzzy logic: CluedIn uses machine learning and fuzzy matching to detect inconsistent or duplicate records, tag metadata, and merge related data without forcing immediate record blending.
  
• Integrated benefits: Combining Purview’s governance with Fabric’s active data quality tools gives end-to-end visibility, automated issue detection, and stronger trust in reports and insights.
  
• Practical takeaway: Use Purview for governance and Fabric (with CluedIn) for ongoing cleaning; run scans, monitor lineage, and fix issues early to keep analytics accurate and compliant.
  

Video summary and context

In a recent YouTube video, creator Guy in a Cube examined how Microsoft Purview and Microsoft Fabric work together to combat dirty data in enterprise environments. The presenter walked viewers through the roles each product plays, demonstrating how governance and automated quality tools can reduce errors and increase trust in analytics. As a result, the video framed the discussion around practical uses rather than theory, showing real-world scenarios where data problems arise and how these tools respond.

Furthermore, the episode highlighted that data quality is not only a technical task but also a governance challenge that involves policy, lineage, and compliance. Therefore, the video positioned Purview as the governance backbone and Fabric, especially when paired with components like CluedIn, as the operational layer that cleans and reconciles records. Consequently, the story offers a useful snapshot for data engineers, stewards, and managers seeking a unified approach to messy data.

How the tools work together

According to the video, Microsoft Purview focuses on cataloging, classification, and policy enforcement, which gives teams visibility into what data exists and how it flows. In contrast, Microsoft Fabric handles ingestion, preparation, and automated cleaning, using machine learning to detect anomalies and fuzzy matches. Thus, the two systems complement each other: Purview governs and documents, while Fabric actively corrects and refines data for analytics.

The presenter emphasized that integration matters because governance without practical cleanup creates friction, and cleanup without governance can produce compliance gaps. Consequently, linking lineage and sensitivity labels from Purview into Fabric pipelines helps maintain regulatory controls as data moves from source to report. This combined approach therefore reduces surprises and aligns technical operations with policy requirements.

Advantages and tradeoffs

The video identified clear benefits, such as improved data trust, faster remediation of quality issues, and better compliance reporting when these platforms work together. For example, automated quality rules and machine learning in Fabric can scale cleaning across diverse datasets, while Purview ensures that sensitive fields remain protected and tracked. As a result, teams can produce reliable reports faster and with more confidence.

However, the presenter also noted tradeoffs: automation helps at scale but can hide edge cases, and strict governance improves control but may slow down exploratory work. Therefore, organizations often face a balance between speed and control, needing flexible policies that allow safe experimentation while enforcing critical protections. In practice, striking that balance requires clear roles for data stewards, iterative tuning of quality rules, and strong collaboration between governance and engineering teams.

Integration challenges and practical limits

The video did not shy away from the challenges of tying governance to operational data processes, pointing out that integration complexity can create delays. For instance, consistent metadata and lineage depend on standardized naming, reliable ingestion pipelines, and accurate mapping across systems, which many enterprises lack initially. Consequently, teams must invest time in cleanup, metadata curation, and training to get the full benefit of an integrated solution.

Moreover, the presenter explained that machine learning approaches like fuzzy matching bring benefits but also risk false positives or merged records that should remain separate, particularly in sensitive domains. Thus, manual review and exception workflows remain essential, and the system should provide transparent audit trails so stewards can reverse or adjust automated actions. Ultimately, automation reduces routine work, but it does not eliminate the need for human judgment.

Takeaways and next steps

In closing, Guy in a Cube recommended a staged approach: start with governance basics in Purview, then layer in automated cleaning in Fabric, and finally iterate on quality rules and steward workflows. This progression helps teams manage risk while showing quick wins, improving data trust incrementally rather than attempting a single massive overhaul. Therefore, organizations can maintain momentum and adapt policies as they learn from real data scenarios.

For teams considering adoption, the video suggested focusing first on high-value datasets where clean data directly affects decisions, and building clear escalation paths for exceptions. By doing so, groups can prioritize effort, measure impact, and refine their approach without overwhelming operations. In short, the combined use of Purview and Fabric offers a promising path to cleaner, governed data, but it requires careful planning, human oversight, and ongoing tuning to realize its full benefits.

Keywords

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