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Data Quality Management

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**Data Quality Management**

What is Data Quality Management?

Data Quality Management (DQM) encompasses the processes and technologies involved in ensuring that data is fit for its intended uses in operations, decision making, and planning. It involves profiling, cleansing, standardizing, and monitoring data to ensure accuracy, completeness, consistency, timeliness, validity, and uniqueness. DQM is commonly used to improve business intelligence, data warehousing, data migration, and application integration, leading to better decision-making, reduced costs, and improved compliance.

Find 321 organizations using Data Quality Management on Sumble →

What other technologies are related to Data Quality Management?

Data Quality Management Complementary Technologies

i
information integrity and compliance
No summary available
Data Quality Management ensures information integrity and compliance by establishing rules and processes to maintain data accuracy and consistency. Therefore these two are complementary.
O
OLAP/ROLAP/HOLAP
No summary available
OLAP relies on clean and consistent data for accurate analysis; Data Quality Management provides the means to achieve this.
C
Comprehensive Financial Close
No summary available
Financial Close processes benefit from high-quality data to ensure accurate reporting and compliance. Data Quality Management contributes to this.
Number of organizations that mention technology
321
Data Quality Management
3
information integrity and compliance
4
OLAP/ROLAP/HOLAP
4
Comprehensive Financial Close
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