Data Quality Management
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Revision as of 13:06, 18 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 supreme 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]. DQM is is the management of people, processes, technology and data through coordinated activities aimed at directing and controlling an organisation in terms of data quality"[3].
Data quality has a significant impact on both the efficiency and effectiveness of programs and projects [4]. 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 program and project 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
Data Quality
Data quality is a multifaceted concept which considers various dimensions for measuring quality[5]. As per ISO 8000-2 guidelines, data is defined as "reinterpretable representation of information in a formalised manner suitable for communication, interpretation, or processing" while data quality is defined as the "degree to which a set of inherent characteristics of data fulfils requirements"[6].
Components of Data Quality
The Three Semiotic Levels for Data Quality Syntactic Semantic Pragmatic
Define which level these 'dimensions' fall under 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
- ↑ 2017 ed. ISO 8000-2:2015 Data Quality - Part 2: Vocabulary, ISO
- ↑ Pg. 2, 2006 ed. Data Quality: Concepts, methodologies and Techniques, Carlo Batini & Monica Scannapieca
- ↑ Pg. 6, 2006 ed. Data Quality: Concepts, methodologies and Techniques, Carlo Batini & Monica Scannapieca
- ↑ 2017 ed. ISO 8000-2:2015 Data Quality - Part 2: Vocabulary, ISO
Bibliography
Batini, C. and Scannapieco, M. (2006): Data Quality: Concepts, Methodologies and Techniques. Berlin: Springer. This book explores various concepts, methodologies and techniques involving data quality processes. It provides a solid introduction to the topic of data quality.
Knowledgent (2014): Building a Successful DQM Program. Knowledgent White Paper Series. This paper provides an introduction to DQM within enterprise information management, explaining the basic concepts behind DQM and also explaining the data quality cycle framework.
