Microsoft Purview can be the best data governance tool in the world, but it will still be useless if people do not know it exists, do not trust the metadata, or do not change the way they work. That is the part that often gets missed. We sometimes think that buying or implementing a governance tool means governance is now “done.” I wish it worked that way. I really do. But the reality is that Purview can automate a lot, but it cannot magically fix missing metadata, undocumented business definitions, manual data movement, duplicate datasets, or people who keep building new reports without first checking whether the data already exists.

This blog is about best practices for Microsoft Purview data governance, not the data security and compliance side of Purview. I covered the broader value of Purview data governance in my post, Microsoft Purview: The key benefits of data governance, but here I want to get more practical and a bit more opinionated. Microsoft Purview has many capabilities across governance, risk, compliance, and security, but this post focuses on the governance experience: cataloging data, improving metadata, helping users find trusted data, understanding lineage, organizing data products, and making data easier to use. Microsoft also has helpful guidance, including data governance planning, Unified Catalog planning, Purview deployment best practices, deployment checklist, and getting started but the real lesson is this: the tool is only as good as the operating model around it.

Make Purview part of the data creation process

One of the first best practices is to notify the Purview administrator when new data sources need to be scanned. Purview does not automatically know about every new database, file share, lakehouse, warehouse, report, or application that shows up in your environment. Someone has to tell the Purview team that a new source exists and should be registered and scanned. This sounds simple, but it is a big deal. If new systems are created and nobody tells the Purview admin, then the catalog will always be incomplete, and once users lose confidence in the catalog, it is hard to get that trust back.

A good governance process should make this notification step part of the normal lifecycle for new data projects. When a new source is created, when a new data product is published, or when a new reporting dataset becomes important, there should be a clear step that says, “Has this been registered in Purview?” This is not just an administrative detail. It is how you prevent Purview from becoming a stale inventory that people stop using. If the catalog is always six months behind reality, users will go back to asking around, searching folders, or messaging the person they think might know where the data is.

Train people to search before they build

You also need to train people to use the data catalog before they build new reports, pipelines, notebooks, or ETL processes. This is one of the biggest culture changes with any catalog. If users do not search Purview first, they will reinvent the wheel, create duplicate datasets, rebuild logic that already exists, and possibly use the wrong data. Purview can help people discover existing assets, understand who owns them, see definitions, review classifications, and find related reports or pipelines. But again, this only works if people know the catalog exists and make it part of their normal workflow.

This training should not just be a one-time demo where someone shows the search box and says, “Good luck.” Users need examples that match how they work. A report developer should see how to find an existing certified dataset before creating a new one. A data engineer should see how to locate source tables, understand lineage, and identify the owner before building a pipeline. A business analyst should see how glossary terms, descriptions, and contacts help them decide whether a dataset is trustworthy. The more practical the training, the more likely people are to use the catalog when it matters.

Use Purview to reduce random access requests

Another important best practice is to train people to use the Request Access feature in Purview instead of sending emails, making phone calls, walking over to someone’s desk, or submitting random tickets through a help desk. We have all seen the old way: someone needs access to a table, they ask five people who might know who owns it, and eventually someone forwards an email to the right person. That process is slow, hard to track, and easy to lose. Request Access gives the organization a cleaner way to capture who requested access, what they requested, who approved or denied it, and why.

This is where governance starts to become real. It is not just about knowing that data exists; it is about creating a repeatable process for using that data responsibly. If access requests happen outside the catalog, the catalog becomes disconnected from the actual user experience. But if users can discover data, understand it, identify the owner, and request access from one place, Purview becomes a working part of the data ecosystem instead of just a metadata repository.

Use Purview as a starting point, not just a search tool

Purview should also be used as a jumping-off point for creating reports and connecting to data. For example, instead of emailing an administrator and asking for the fully qualified name of a data source, users can search in Purview and use features such as “Open in Power BI Desktop.” Purview does not store the actual data, but it stores the metadata needed to find and connect to that data. When a user opens an asset from Purview, the qualified name can be passed into Power BI Desktop, making the experience much easier. This is a great example of why a catalog should not just be a passive inventory; it should help people take action.

This point is important because business users do not want governance for the sake of governance. They want to get their job done. If Purview helps them find the right data faster, avoid waiting on an admin, and start building a report with the correct source, then it becomes valuable to them. If it just gives them a long list of cryptic table names, they will avoid it. The catalog needs to meet users where they are and help them move from discovery to action.

Do not assume scanned metadata is good metadata

After a scan runs, someone should review the metadata and determine whether it is correct. This is one of those steps that people sometimes skip because they assume the scan did everything. But a scan can miss a classification, classify something incorrectly, get a data type wrong, or pull in a CSV file that has no headers and shows columns as col1, col2, and col3. That is not very helpful to a business user trying to decide whether the data is useful. The metadata may need to be supplemented with better descriptions, column definitions, tags, data asset attributes, glossary terms, or friendlier names for tables and fields.

This manual review process is not a sign that Purview is failing. It is just the reality of enterprise data. Metadata in source systems is often incomplete, inconsistent, or just plain confusing. A table name might make sense to the developer who created it 12 years ago, but nobody else. A field might be called CUST_ID in one system, PERSON_KEY in another, and CLIENT_NUM in a third. Purview can help bring order to that chaos, but only if someone curates the metadata and adds business meaning where the technical metadata falls short.

Use glossary terms, contacts, domains, and data products

Glossary terms are important because they help connect business meaning across inconsistent technical names. A customer might be called customer in one table, person in another, client in another, and account holder somewhere else. Without a glossary, users have to guess whether these terms mean the same thing or something different. With glossary terms, you can tag related columns and assets so users can understand the business meaning, even when the physical column names are inconsistent. This is one of the places where Purview can make a big difference, but only if the glossary is maintained and actually used.

It is also helpful to manually add contacts (experts and owners) to assets so users know who to contact with questions. This sounds basic, but it is one of the most practical things you can do. When someone finds a dataset, they often want to know who owns it, who understands it, who can approve access, and who can explain whether it is the right source. A catalog without contacts can become a museum of metadata. A catalog with owners and experts becomes a living system that helps people collaborate.

Governance domains and data products should also be created intentionally. Do not just scan a bunch of assets and hope users can figure it out. Manually organize assets into governance domains and data products so people can browse and search in a way that matches how the business thinks. Microsoft’s Unified Catalog guidance describes governance domains and data products as key concepts, and that makes sense because most users do not think in terms of servers, schemas, and storage accounts. They think in terms of finance, sales, customer, operations, claims, inventory, or whatever domain matters to their business.

Treat lineage as something you manage, not something you magically get

Lineage is another area where expectations need to be realistic. Purview can capture lineage from supported tools, and Microsoft has documentation on data lineage, but it cannot automatically know everything that happens outside those tools. If someone copies and pastes data manually, moves files by hand, exports data to Excel, or runs custom scripts, Purview may not know that lineage unless someone tells it. For non-standard movement, you may need scripts or API calls to update lineage. This is especially important for organizations that have a mix of ADF pipelines, stored procedures, notebooks, custom Python scripts, legacy ETL tools, and manual data movement.

Lineage from ETL jobs, stored procedures, and notebooks is especially hard because parameters can change what data is read or written at runtime. Static analysis can only go so far. A stored procedure may build dynamic SQL, a notebook may use variables, or a pipeline may pass different source and target names depending on the job configuration. The most accurate approach is similar to how Azure Data Factory lineage works: capture what actually happened when the job ran, then send that runtime information to Purview after the job completes. That is how you move from guessed lineage to real lineage.

Act when sensitive data and quality issues are found

A strong best practice is to create a process for what happens when sensitive data is found through classifications. For example, suppose a SQL database scan finds Social Security numbers in a comment field. That should not just sit quietly in the catalog as an interesting piece of metadata. The right person, such as the SQL DBA, data owner, security team, or privacy lead, should be notified so they can confirm that the data is protected properly. Classification is useful because it finds potential risk, but finding risk is only step one. Someone has to act on it.

Purview can also help score and monitor data quality, as described in Microsoft’s data quality overview, but it is important to understand what that means. Purview can identify, measure, and expose data quality issues, but it does not magically fix the data. The fixing usually happens in the source system, data pipeline, transformation layer, or business process that created the bad data in the first place. That distinction matters. Purview can shine a bright light on quality problems, but someone still has to walk over and clean up the mess (unfortunately, no magic button yet).

Use APIs and think catalog of catalogs

The Atlas APIs are also worth understanding because they can help with bulk metadata changes, lineage updates, and integration with sources that do not have a native connector. If you have data sources that Purview cannot scan directly (via a pull), you may be able to push metadata into Purview through APIs. If you have ETL jobs outside of Azure Data Factory, you may be able to capture lineage through custom logic. This is especially useful in mature environments where data movement happens through a mix of modern and legacy tools. The API layer is one way to close some of the gaps that automation alone cannot solve.

Purview can also be used as a catalog of catalogs, especially in environments that already use tools such as Databricks Unity Catalog. Many organizations will not have just one catalog. They may have specialized catalogs for different platforms, teams, or technologies. Purview can provide a broader enterprise view across those environments, helping users discover assets even when the underlying metadata is managed elsewhere. The key is to be clear about which catalog owns what, how metadata flows between them, and where users should go for enterprise-wide discovery.

Bottom line

Here’s the bottom line: Purview can automate a lot, but it cannot automate everything. There will always be manual intervention needed to make metadata accurate, useful, and trusted. This is because source metadata may be missing or wrong, data may move manually, lineage may depend on runtime parameters, new data sources may not be automatically discovered, and business definitions may live in people’s heads instead of systems. Purview is a powerful tool, but it is not a substitute for governance discipline.

The best Purview implementations combine automation with human ownership. They scan what they can, curate what matters, train users how to search before building, create clear access request processes, review sensitive data findings, improve lineage, organize data products, and keep business metadata current. That is how Purview becomes more than a catalog. It becomes a trusted starting point for finding, understanding, governing, and using data across the enterprise.