Data Quality Management

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===Data Quality===
 
===Data Quality===
  
===Quality Management===
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====Measuring and Defining Data Quality====
 
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===Benefits of DQM===
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Trust
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Improve Analytics Accuracy
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Minimize Unnecessary Costs
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Compliance and Risk
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===Measuring and Defining Data Quality===
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Completeness
 
Completeness
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Timeliness
 
Timeliness
  
==Three Pillars of DQM==
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====Framework: Data Quality Life Cycle====
  
===People===
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===Quality Management===
===Process===
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===Continuous Improvement===
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==Framework: Data Quality Life Cycle==
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ISO 9001, reasons and benefits of implementing a quality management system
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===Fundamental Principles of Data Quality Management===
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====Three Pillars of DQM====
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=====People=====
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=====Process=====
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=====Continuous Improvement=====
  
 
===Components of DQM===
 
===Components of DQM===
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====Quality Reporting====
 
====Quality Reporting====
 
====Remediation====
 
====Remediation====
 
==Establishing a Successful DQM Program==
 
  
  
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==Glossary==
 
==Glossary==
 
DQM: Data Quality Management
 
DQM: Data Quality Management
ISO: International Organisation for Standardization
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//ISO: International Organisation for Standardization
  
 
==References==
 
==References==

Revision as of 14:33, 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

Measuring and Defining Data Quality

Completeness Accuracy Validity Consistency Integrity Timeliness

Framework: Data Quality Life Cycle

Quality Management

ISO 9001, reasons and benefits of implementing a quality management system

Fundamental Principles of Data Quality Management

Three Pillars of DQM

People
Process
Continuous Improvement

Components of DQM

Data Discovery

Profiling

Quality Rules

Quality Monitoring

Quality Reporting

Remediation

Glossary

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

References


Bibliography

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