---
title: "Data Quality Studio"
slug: "data-quality-studio"
description: "Enhance data quality in Microsoft Dynamics 365 F&SCM with validation, duplicate checks, and enrichment rules for clean, consistent data management."
tags: ["Introduction"]
status: "update"
updated: 2026-03-27T13:53:43Z
published: 2026-03-27T13:53:46Z
canonical: "docs.staedean.com/data-quality-studio"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.staedean.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Quality Studio

Data Quality Studio for Microsoft Dynamics 365 Finance and Supply Chain Management helps you control your data quality. You can apply data validation rules, duplicate check rules, and data enrichment rules to keep your data clean and consistent.

This picture shows the data quality dimensions:

![](https://cdn.document360.io/da662430-6a41-47b1-8c7e-7e4ac37a9756/Images/Documentation/DQS-QualityDimensions.png)

These data quality dimensions are covered by the Data Quality Studio in combination with D365 F&SCM:

- 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 enrichment 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 enrichment rules.