Data Quality Management
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Data quality management (DQM) serves the objective of continuously improving the quality of data relevant to an organisation, program or project<ref>Pg. 3, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent</ref>. It is important to understand that the goal DQM is not about simply improving data quality in the interest of having high-quality data, but rather to achieve desired outcomes that rely on high-quality data<ref>Pg. 3, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent</ref>. | Data quality management (DQM) serves the objective of continuously improving the quality of data relevant to an organisation, program or project<ref>Pg. 3, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent</ref>. It is important to understand that the goal DQM is not about simply improving data quality in the interest of having high-quality data, but rather to achieve desired outcomes that rely on high-quality data<ref>Pg. 3, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent</ref>. | ||
| − | Data quality has a significant impact on both the efficiency and effectiveness of organisations <ref>Pg. 2, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent</ref>. 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. However, if the data quality is poor, managers risk taking misguided decisions based on unreliable data. It is therefore imperative that a proper data quality management system is in place to ensure decisions are being driven based on high-quality data. | + | Data quality has a significant impact on both the efficiency and effectiveness of organisations <ref>Pg. 2, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent</ref>. 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. However, if the data quality is poor, managers risk taking misguided decisions based on unreliable data. It is therefore imperative that a proper data quality management system is in place to ensure decisions are being driven based on high-quality data. This article explores the fundamentals behind DQM using references to industry best practices and ISO 8000-1 and ISO 9000-1 guidelines. |
==Overview== | ==Overview== | ||
Revision as of 22:38, 17 February 2018
Contents |
Abstract
Data quality management (DQM) serves the objective of continuously improving the quality of data relevant to an organisation, program or project[1]. It is important to understand that the goal DQM is not about simply improving data quality in the interest of having high-quality data, but rather to achieve desired outcomes that rely on high-quality data[2].
Data quality has a significant impact on both the efficiency and effectiveness of organisations [3]. 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. However, if the data quality is poor, managers risk taking misguided decisions based on unreliable data. It is therefore imperative that a proper data quality management system is in place to ensure decisions are being driven based on high-quality data. This article explores the fundamentals behind DQM using references to industry best practices and ISO 8000-1 and ISO 9000-1 guidelines.
Overview
DQM can be considered one of many building blocks to establishing a successful data governance program [4].
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
Structure and Components of the DQM Framework
Glossary
DQM: Data Quality Management //ISO: International Organisation for Standardization
References
- ↑ Pg. 3, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent
- ↑ Pg. 3, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent
- ↑ Pg. 2, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent
- ↑ Page 3, 2014 ed. Building a Successful Data Quality Management Program, Knowledgent
