Microsoft Purview Data Quality Thresholds: More Control, More Trust

What Are Data Quality Thresholds?

A data quality threshold defines the minimum acceptable score for a rule to pass. Instead of applying a single fixed standard across all data, organizations can now set expectations that align with business context and criticality.

For example:

• An email column may require 99% completeness
• A product description column may only require 85% completeness
• Financial or regulatory data may require 100% accuracy

With customizable thresholds, quality expectations become more meaningful and business-aligned.

Figure 1: Data Quality Thresholds graphic

Why Does This Matter?

Previously, using a single hardcoded threshold could lead to misleading quality scores. Critical data might appear “healthy” even when it didn’t meet business standards.

With Data Quality Thresholds, you can:

• Define rule-level expectations
• Align quality scores with business risk
• Increase trust in DQ reporting
• Improve governance decision-making

Data Asset-Level Quality Threshold

Users can define data quality thresholds at the data asset level to measure how suitable a dataset is for specific business use cases. This allows organizations to quantify the overall health and fitness of a data asset before it is used in analytics, reporting, or data products.

Figure 2: Setting the Data quality score threshold

If the measured data quality score falls below the predefined threshold, the system can trigger notifications to the data asset owner or steward, prompting them to take corrective actions.

It is important to note that not all data assets are equally critical. Therefore, thresholds should be context-driven and use-case specific.

Example Scenario

A marketing dataset used for campaign analysis may tolerate a lower quality threshold (e.g., 80%), since minor inconsistencies may not significantly impact insights. However, a financial reporting dataset used for regulatory filings may require a very high threshold (e.g., 98–100%), as even small errors can lead to compliance risks.

Data Quality Rule-Level Threshold

Thresholds can also be defined at the individual rule level, particularly for rules applied to specific columns. This provides more granular control and ensures that critical data elements are held to higher standards.

Not all attributes have the same importance, so thresholds should reflect business criticality.

Figure 3: Thresholds defined at the individual rule level

Example Scenarios

Email vs. Gender (Customer Contact Data)
A completeness rule for a customer’s email address should have a higher threshold (e.g., 95–100%), since missing or invalid email addresses directly impact communication and engagement.

In contrast, a gender attribute may have a lower threshold (e.g., 70–80%), as it is often less critical for most use cases.

Billing Address vs. CRM Address
A billing address is highly critical because it directly impacts:

• • Invoice generation
  • Tax calculations
  • Timely delivery of invoices

Therefore, the threshold for billing address quality should be very high (e.g., 98–100%).

On the other hand, a CRM address used for general customer profiling may have a lower threshold, as occasional inaccuracies may not significantly affect business operations.

The Impact

By enabling flexible, context-aware scoring, Data Quality Thresholds help organizations move beyond generic quality checks and toward business-driven data quality management.

Summary

Data Quality Thresholds define the minimum acceptable score for data quality rules, allowing organizations to move beyond a one-size-fits-all approach and align quality expectations with business context and criticality.

Instead of using fixed thresholds, organizations can set custom thresholds based on how important the data is. For example, financial data may require near-perfect accuracy, while less critical fields can tolerate lower thresholds.

Thresholds can be applied at two levels:

• Data Asset Level: Measures the overall fitness of a dataset for a specific use case. Critical datasets (e.g., financial reporting) require higher thresholds than less critical ones (e.g., marketing analytics).
• Rule Level: Applies to individual columns or rules, ensuring that critical attributes (e.g., email, billing address) have stricter quality requirements than less important ones.

This approach improves:

• Alignment with business risk and priorities
• Trust in data quality reporting
• Governance decision-making
• Focus on high-impact data issues

Overall, data quality thresholds enable more meaningful, context-aware, and business-driven data quality management, helping organizations prioritize what matters most and build confidence in their data.