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In this blog, we’ll be discussing Microsoft Purview for Data Governance. We’ll review past limitations within the data ecosystem, see how Microsoft has responded to fill this gap with Purview, discuss where we are today with Purview, and share what to expect in the future.
Where We’ve Been: The History of Data Governance
Before we get started on why we need Microsoft Purview for Data Governance, let’s look to the past.
Those who have been in the data space for many years will remember some of the many issues to contend with. Data was sprawled throughout the organization. Because data was territorial, data silos arose, where specific departments owned THEIR data.
Report writers had difficulty accessing the data, and merging datasets was costly and delayed as a result. There were limited tools to connect to databases. Report writers had to write complex queries or embed logic into reporting tools. They had to track down the business rules as they were infrequently documented. End users would salivate to get a pulse of the business. If they did get reports, they weren’t always accurate or timely.
Within the Enterprise Data Ecosystem, almost no one had a complete inventory of ALL the data. People would show up to meetings to present their reports, only to be ambushed by data inaccuracies across multiple reports.
To summarize, data was NOT treated as a corporate asset. It was a necessary feature, mostly an afterthought on major projects. Report writers did not carry the same weight as they do today.
Data Governance did exist back then. Yet it was often pushed to the side due to cost and time to deliver value. Investments were made in front-end development. Yet, there were early versions of Data Governance.
Microsoft had early versions of data quality as part of its SQL Server on-premises bundle of products. One tool, Data Quality Services, allowed developers to correct data based on business rules and merge it back into the data ecosystem. It also allowed data to be sent to a vendor service to standardize address and corporate data and integrate it back into the analytics.
Microsoft also had a product called Master Data Services, which allowed organizations to create Master Data. This data was modeled, curated and combined with similar data across multiple data sources. Once processed, it was consumed across the entire data ecosystem, resulting in standardized, accurate reports.
Microsoft was an early adopter of an online Data Catalog in Azure around 2015 as part of Classic Azure. Its basic feature was an online Data Catalog or Data Dictionary in Excel, which we traditionally kept in Excel (which was outdated rapidly). Although limited features, many people in the data space could see instant value.
The next iteration of data governance, Azure Purview, was launched in September 2021. It gained traction amongst the Microsoft Data Community because it expanded on the Data Catalog to register resource assets, apply data classification rules, and manage data lineage, as well as a Data Glossary and Data Estate Insights.
Where We Are Today: Microsoft Purview for Data Governance
More recently, Azure Purview was rebranded as Microsoft Purview. With that, we are seeing new features added all the time: Data Policies, Self-Service access policies, DevOps Policies, and Data Sharing to name a few. One major change is limiting one instance of Micorosft Purview per tenant. (Prior, we could set up three instances for dev, test, and prod. This requires new patterns when designing enterprise solutions.)
Where We’re Going: The New Microsoft Purview for Data Governance (Preview)
Today, many organizations leverage the power of Microsoft Purview. With that said, we are on the fringe of a newer version, still in Preview. It leverages all the existing features from the current, plus, it brings over many features from a similar product called Microsoft Purview Compliance.
The new Microsoft Purview for Data Goverance (Preview) version offers a streamlined design and a unified experience. Some examples are:
- Data Management Section: Allows defining and assigning business-friendly terminology.
- Step-by-step Onboarding Guidance: Simplifies setup and configuration.
- Reduced Detection to Investigation Time: Streamlines the process of identifying and addressing data risks.
- Machine Learning Model Highlighting: Help identify relevant models for data governance and compliance.
- Expanded Optical Character Recognition (OCR): Enhances data discovery by extracting text from images and scanned documents.
Clearly, we can see the huge investment from Microsoft in this product and in the Data Governance space. And that’s why many organizations are looking to top Microsoft Partners, such as Quisitive, to partner with and assimilate Data Governance into their Azure Data Ecosystem.
Most recently, Quisitive was awarded the Microsoft Partner of the Year for Analytics. We strive to partner with organizations like yours to start your data journey, treat data as an asset to your organization, grow the business, reduce costs, and streamline processes.
Contact us today to get started. Thanks for reading!
Read more about Data Governance in my recent blog post, Getting Started with Microsoft Purview for Data Governance in Azure.
