Data Quality (DQ) for Standalone Data Assets at Microsoft Purview

ShafiqMannan

Microsoft

Feb 19, 2026

Why is Data Quality for your Data Asset critical today?

Many companies today cannot activate their data estate. Our research shows that 75% of the companies today do not even have a data quality program. This is such a major problem because data quality is becoming a major focus of AI. Your AI is only as good as your data.

In the old days, when we weren't necessarily content aware, it was fine because human was always in the loop and corrected data as needed, but in the new world right now where the AI looks at the content, not just the structure but the content of the data and if that content of the data is fundamentally off, whether it's misrepresented, inconsistent, illegible, inaccurate, insecure, incompliance, it will make your AI wrong.

It will make your AI ugly. And it will impact your company and impact you as an employee building AI and BI/Insights. There is nothing worse than building something that doesn't get used.

What Is Data Quality for a Standalone Data Asset?

A standalone data asset is a dataset that is not linked to any data product. Data Quality (DQ) for standalone assets enables organizations to measure, monitor, and improve the quality of these datasets independently—without requiring them to be part of a data product.

Why This Matters

Improve Data Before Linking to a Data Product

Users can profile, assess, clean, and standardize data before associating it with a data product. This ensures higher quality at the time of onboarding.

Make Better Curation Decisions

Understanding the quality of standalone assets helps organizations decide which datasets are suitable for inclusion in governed data products.

Support Broader Use Cases

Not all datasets are used for analytics. Standalone assets may support:

Data monetization
AI grounding or training data
Operational workloads

Unified Data Quality Management

Organizations can use a single Purview Data Quality solution to manage both standalone assets and data product–associated assets, including issue remediation.

Optimize Storage and Reduce Costs

Low-quality or unused datasets can be archived to lower-cost storage or removed using data minimization principles, reducing storage costs and improving ROI.

Accelerate Governance Adoption

Organizations can start measuring and improving data quality immediately—without waiting for formal data product definitions—helping mature governance practices faster.

Measure and Monitor Data Quality for Standalone Assets using Microsoft Purview

To start measuring and monitoring the data quality of a standalone data asset, first add the asset from the data map to a governance domain. If a connection to the data source has not already been configured, you must create one for the selected source system.

Once the asset is added to the domain and the data source connection is established, select the asset and run data profiling. You can accept the recommended columns or customize the selection by adding or removing columns. After profiling completes, review the results to understand the structure and quality of the data.

Based on profiling insights, you can create custom rules, apply out-of-the-box rules, or use AI-enabled rule suggestions. After adding rules to the selected columns, run a data quality scan to generate column-level data quality scores and assess the overall health of the dataset.

To continuously measure and improve data quality, configure data quality error record storage and schedule recurring data quality scans.

You can also associate a standalone data asset—along with its data quality scores and rules—to a data product. This allows data product owners to reuse rules created for standalone assets. The same data asset can be cloned and associated with multiple data products to support different use cases. Note that the data product must be in Draft state before a standalone asset with a data quality score can be associated with it.

Additionally, you can configure alerts to notify your team when a data quality score falls below a defined threshold or when a data quality scan fails.

Because Purview uses role-based access control (RBAC), only users with the Data Product Owner role can associate a standalone data asset with a data product. Users must also have access to the relevant governance domain. A Data Product Owner must have a Domain Reader (local or global) or Data Quality Reader role to browse standalone asset DQ pages and associated assets.

Summary

Data Quality for standalone data assets allows organizations to independently assess, improve, and monitor datasets before or without linking them to data products. This approach increases governance agility, improves decision-making, supports diverse use cases beyond analytics, reduces storage costs, and accelerates enterprise data maturity.