Data Quality Management
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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 to gain a competitive advantage. As such, data is becomingly more and more 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, data quality management has become increasingly important for project and program decision makers. | 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 to gain a competitive advantage. As such, data is becomingly more and more 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, data quality management has become increasingly important for project and program decision makers. | ||
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===Benefits of DQM=== | ===Benefits of DQM=== | ||
Revision as of 13:27, 17 February 2018
Contents |
Abstract
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 to gain a competitive advantage. As such, data is becomingly more and more 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, data quality management has become increasingly important for project and program decision makers.
Overview
Data Quality
Quality Management
Benefits of DQM
Trust Improve Analytics Accuracy Minimize Unnecessary Costs Compliance and Risk
Measuring and Defining Data Quality
Completeness Accuracy Validity Consistency Integrity Timeliness
Three Pillars of DQM
People
Process
Continuous Improvement
Framework: Data Quality Life Cycle
Components of DQM
Data Discovery
Profiling
Quality Rules
Quality Monitoring
Quality Reporting
Remediation
Establishing a Successful DQM Program
Glossary
DQM: Data Quality Management ISO: International Organisation for Standardization
References
