Data Quality: Dimensions, Measurement, Strategy, Management, and Governance

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(as of Dec 23,2024 12:05:19 UTC – Details)

ASIN ‏ : ‎ B0BQJSFMN1
Publisher ‏ : ‎ ASQ Quality Press; 1st edition (March 18, 2019)
Publication date ‏ : ‎ March 18, 2019
Language ‏ : ‎ English
File size ‏ : ‎ 64190 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 718 pages

Data Quality: Dimensions, Measurement, Strategy, Management, and Governance

In today’s data-driven world, ensuring the quality of data is more important than ever. Poor data quality can lead to incorrect decisions, wasted resources, and damaged reputation. Therefore, organizations must have a comprehensive approach to managing data quality.

Data quality can be assessed through various dimensions, such as accuracy, completeness, consistency, timeliness, and relevance. These dimensions help organizations understand the overall health of their data and identify areas for improvement.

Measurement of data quality involves the use of metrics and key performance indicators (KPIs) to track the quality of data over time. By measuring data quality, organizations can identify trends, patterns, and potential issues that need to be addressed.

Developing a data quality strategy is essential for organizations to ensure that data quality is maintained at all times. This strategy should outline the processes, tools, and resources needed to continuously monitor, evaluate, and improve data quality.

Effective data quality management involves establishing roles and responsibilities, implementing data quality tools and technologies, and creating processes for data validation, cleansing, and enrichment. By investing in data quality management, organizations can ensure that their data is accurate, reliable, and trustworthy.

Data governance is another critical aspect of ensuring data quality. Data governance involves establishing policies, procedures, and guidelines for managing data across the organization. By implementing a robust data governance framework, organizations can ensure that data is managed, protected, and used in a consistent and compliant manner.

In conclusion, data quality is a crucial aspect of any organization’s success. By focusing on dimensions, measurement, strategy, management, and governance of data quality, organizations can ensure that their data is accurate, reliable, and valuable for decision-making.
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