Data Quality Management

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

Glossary

DQM: Data Quality Management //ISO: International Organisation for Standardization