Data quality studio

Data quality studio for Microsoft Dynamics 365 Finance and Operations helps you control your data quality. You can apply data validation rules, duplicate check rules, and data action rules to keep your data clean and consistent.

This picture shows the data quality dimensions:

These data quality dimensions are covered by the Data quality studio in combination with D365 FO:
  • Consistency: Is the data consistent between systems and entities? Do duplicate records exist? For example, you can check data consistency with duplicate check rules or a Data pattern type validation rule.
  • Validity: Are all data values within the value domains specified by the business? For example, you can check data validity with Range expression type validation rules.
  • Accuracy: Does data reflect the real-world objects or a verifiable source? For example, you can check email or address accuracy with Web service type validation rules or use Transformation list type action rules.
  • Integrity: Are the relations between entities and attributes consistent? Within tables and between tables? For example, you can check integrity with Range expression type or Data pattern type validation rules.
  • Timeliness: Is the data available at the time needed? For example, you can check timeliness with Mandatory type or Range expression type validation rules.
  • Completeness: Is all necessary data present? For example, you can control completeness with Mandatory type validation rules or action rules.

If you can't find the information you are looking for, you can:

Set up data quality policies

Set up data quality policy versions

General setup

In the Data Quality studio, you can define several general settings.

Set Data quality studio parameters

Set up dynamic queries

Set up condition table mapping