Overview
Product Information on Microsoft Data Quality Services
What is Microsoft Data Quality Services?
Microsoft Data Quality Services Pricing
Overall experience with Microsoft Data Quality Services
“Enhance Data Consistency and Accuracy using Microsoft DQS and its Features”
“Simple Interface And Easy Start Make DQS Suitable For Fundamental Data Tasks”
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Company Description
Microsoft enables digital transformation for the era of an intelligent cloud and an intelligent edge. Its mission is to empower every person and every organization on the planet to achieve more. Microsoft is dedicated to advancing human and organizational achievement. Microsoft Security helps protect people and data against cyberthreats to give peace of mind.
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Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
User Sentiment About Microsoft Data Quality Services
Reviewer Insights for: Microsoft Data Quality Services
Deciding Factors: Microsoft Data Quality Services Vs. Market Average
Performance of Microsoft Data Quality Services Across Market Features
Microsoft Data Quality Services Likes & Dislikes
I really like its knowledge driven approach I use for data cleansing. Also I am able to create a knowledge base and define domain rule and reference data of my choice to make the data more simple and increase the data quality also I can reuse the same knowledge base in multiple datasets. Also it was really easy for me to integrate it with my existing SQL sever and one more plus point is that it automatically cleans my data also I can take control of the cleaning process.
It works inside SQL Server, there's no extra install needed. It's easy to use for basic cleaning tasks.
What I like most about DQS is its ability to create a reusable knowledge base for data cleansing, once you've defined your rules and domains, you can apply them consistently across datasets. This saves time and ensures cleaner and more reliable data over the long term.
Creating knowledge base for the first can be little tricky for someone new to it.
For big data or very complex rules it is not as strong as other enterprise tools. The interface is simple but sometimes it feels old and slow.
It struggles with NoSQL systems and unstructured formats which can be a dealbreaker for some
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Peer Discussions
Microsoft Data Quality Services Reviews and Ratings
- Teamcenter Developer<50M USDIT ServicesReview Source
Enhance Data Consistency and Accuracy using Microsoft DQS and its Features
My Experience with Microsoft Data Quality Services has been vert good one. Its really useful when it comes to doing data cleansing and making the data standardized. The Knowledge base it provides to define domain rules make it really simple to make the data consistent. Also the data matching feature really helps in my day to day tasks like checking for duplicate entries and when I need to merge the data it really saves a lot of time. - Manager, Customer Experience - Metrics a50M-1B USDBankingReview Source
Knowledge Base Feature in DQS Enhances Data Consistency Over Multiple Datasets
DQS is a practical tool for cleaning and managing structured data, especially if you're working within SQL Server. It's not the most modern solution, but it's reliable for maintaining data integrity in enterprise environments. - Finance Team Leader50M-1B USDTravel and HospitalityReview Source
Human-In-The-Loop Interface Useful Yet Limited By Rule-Based Approach
Overall it has a good data stewardship workflow w/c offers a human in the loop interface - Architect<50M USDConstructionReview Source
Filtering Duplicates in Project Data Contrasted by Persistent Microsoft Sync Issues
Excellent knowledge base management that works with all our office's Microsoft-based applications. - Expert Operations Associate50M-1B USDHealthcare and BiotechReview Source
Data Quality Management Simplified by Microsoft DQS, With Limitations in Performance Noted
Using Microsoft DQS has helped us clean and standardize our data without needing a huge data team. It's not super flashy, but it gets the job done. It's good for a growing startup trying to build solid data foundations.



