What Is Data Quality Assurance?

Data quality management (Data Quality Management) refers to the identification, measurement, monitoring, and monitoring of various types of data quality issues that may arise from each stage of the planning, acquisition, storage, sharing, maintenance, application, and extinction life cycle. A series of management activities such as early warning, and further improve the quality of data by improving and improving the management level of the organization.

Data Quality Management

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Data quality management (Data Quality Management) refers to the identification, measurement, monitoring, and monitoring of various types of data quality issues that may arise from each stage of the planning, acquisition, storage, sharing, maintenance, application, and extinction life cycle. A series of management activities such as early warning, and by improving and improving the management level of the organization,

Data quality management is a cyclic management process. Its ultimate goal is to increase the value of data in use through reliable data, and ultimately win economic benefits for the enterprise.

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Those familiar with Six Sigma management should know that Six Sigma emphasizes fact-driven management. But the truth is to speak with data. Mapping to Six Sigma management methods, MTC-DQM recommends a ten-step data quality management method.

1. Define and agree on issues, opportunities, and goals to guide the overall data quality management effort.

2. Collect, summarize, and analyze relevant forms and information environments. Design capture and evaluation scenarios.

3. Assess data quality in terms of data quality dimensions.

4. Use various techniques to assess the impact of poor data on the business.

5. Identify the real causes that affect data quality and distinguish the levels of data quality that these causes affect.

6. Finalize recommendations for action and develop programs for data quality improvement, both data-level and organization-level.

7. Establish a data error prevention program and correct current data problems.

8. By improving the organization's management process, the data quality problems caused by management flaws can be minimized.

9. Monitor data and management to maintain improved results.

10. Communication runs through management, and the organization and management processes are evaluated cyclically to ensure that the results of data quality improvement are effectively maintained.