Data Quality Management
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==Abstract== | ==Abstract== | ||
| − | Data quality management (DQM) serves the objective of continuously improving the level of data quality within an organisation | + | Data quality management (DQM) serves the objective of continuously improving the level of data quality within an organisation. |
As part of the digital transformation, data has become more readily available and more important than ever before. Organisations are performing data analytics to leverage key resources and optimise processes to gain a competitive advantage. As such, data is becomingly increasingly valuable to project and program managers who are driving decision making based on data insight. The value of the data is highly reflected by the quality of the data. If the data quality is poor, managers risk taking misguided decisions based on unreliable data. Therefore, it is imperative that a proper data quality management system is in place to ensure data of the highest quality. | As part of the digital transformation, data has become more readily available and more important than ever before. Organisations are performing data analytics to leverage key resources and optimise processes to gain a competitive advantage. As such, data is becomingly increasingly valuable to project and program managers who are driving decision making based on data insight. The value of the data is highly reflected by the quality of the data. If the data quality is poor, managers risk taking misguided decisions based on unreliable data. Therefore, it is imperative that a proper data quality management system is in place to ensure data of the highest quality. | ||
Revision as of 21:49, 17 February 2018
Contents |
Abstract
Data quality management (DQM) serves the objective of continuously improving the level of data quality within an organisation.
As part of the digital transformation, data has become more readily available and more important than ever before. Organisations are performing data analytics to leverage key resources and optimise processes to gain a competitive advantage. As such, data is becomingly increasingly valuable to project and program managers who are driving decision making based on data insight. The value of the data is highly reflected by the quality of the data. If the data quality is poor, managers risk taking misguided decisions based on unreliable data. Therefore, it is imperative that a proper data quality management system is in place to ensure data of the highest quality.
Overview
Data Quality
Measuring and Defining Data Quality
Insert Diagram! Completeness Accuracy Validity Consistency Integrity Timeliness
Framework: Data Quality Life Cycle
Insert Diagram!
Quality Management
ISO 9001, reasons and benefits of implementing a quality management system
Fundamental Principles of Data Quality Management
Explain each principle
Three Pillars of DQM
People
Process
Improvement
ISO 8000 Framework for DQM
Detailed Structure DQM Framework Components
Glossary
DQM: Data Quality Management //ISO: International Organisation for Standardization
References
