Review Summary
See a synthesized overview of the key takeaways from verified reviews of Microsoft Purview Data Governance.
See a synthesized overview of the key takeaways from verified reviews of Microsoft Purview Data Governance.
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The most useful feature for me is the data lineage view. When looking at a dashboard used for seasonal reporting or campaign analysis, it's very helpful to see where the numbers originate and how they move between systems. The data catalog is another big plus. Instead of asking multiple teams where certain datasets live, you can search and understand what data exists and who owns it. That saves a lot of time when building reports or preparing presentations. I also appreciate the sensitivity and classification features, especially when working with consumer data or campaign performance data that has privacy implications across different markets.
It provides a relatively complete list of original data and integrates fully with Azure, enabling enterprises to have a comprehensive understanding of their existing data assets and providing assistance in data inventory and basic governance.
Purview automatically scans and classifies data across Azure, MS 365, SQL server, and other connected sources. Because we are heavily invested in Azure and MS 365, purview fits seamlessly into our stack. Integration with Azure, Power BI and SQL makes lineage tracking and governance practical. The ability to define classification rules and policies in one place, and apply them across data sources, provide much more control and consistency than our legacy tools.
From a business user perspective, the interface can feel quite technical. It's clearly built more with data teams in mind. Another challenge is that the value depends heavily on how well the catalog is maintained. If datasets aren't properly documented, it becomes less useful. Finally, adoption can be slow because many teams don't naturally think about governance when they just want quick access to data.
1. The metadata editing function lacks flexibility and has limited customization options. 2. The overall cost is relatively high, especially in big data scenarios. 3. Its value needs to be clearly demonstrated through more practical application scenarios.
Configuring scanning, access control, and policies across different data estates requires planning and internal alignment. MS rolls out updates often, but sometimes the documentation lags behind. Its pricing is tricky to predict upfront, and then only after scanning a large number of sources. Budgeting requires careful planning.