.

In the modern world, every organization is propelled forward by data. As organizations continue to collect more data, the need to manage the quality of the data has become more prominent. Data quality management is a set of practices undertaken by a data manager or an organization to maintain high-quality information. This article explains what is Data Quality Management, its importance, and the metrics used to assess data quality.

Why do organizations need Data Quality management?

In the digital age, the proliferation of data has presented a real challenge – a data crisis. The data crisis entails low-quality data and the volume of the data makes it harder for businesses to make sense out of it. Thereby, Data Quality Management has become an important process used to make sense out of data. It helps organizations to point out errors in their data that need to be resolved. Here are the 4 reasons why you need Data Quality management.

  • Better functioning business

  • Efficient use of resources

  • Competitive advantage

  • Good business leads

Data Quality metrics:

Accuracy: Data accuracy refers to the extent to which data is considered to be true, can be relied on, and is error-free.

Completeness: It is considered to be complete when it fulfills certain exceptions in an organization. It indicates if there is enough of it that can draw meaningful conclusions.

Consistency: Data consistency simply specifies that two data values retrieved from multiple and separate data sets should in no way conflict with each other.

Integrity: It is also referred to as data validation, data integrity refers to structurally testing data to ensure compliance with an organization’s data procedures. Such data shows that it has no unintended errors and that it corresponds to its appropriate data types.

Timeliness: When your data isn’t ready when users need it, it fails to fulfill the data quality dimension of timeliness.

Summary

Data Quality Management is a huge hassle. This means if your organization is the one that takes the lead in making its data sound, it will automatically gain a competitive advantage in its industry.

This article details the information needed to maintain high-quality data. Be sure to look out for DQLabs.ai – a leading data quality management platform to help you in keeping your organization competitive in today’s digital marketplace through Data Quality Management.

Views: 334

Tags: dataquality, dsc_dataquality

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

© 2021   TechTarget, Inc.   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service