Measuring Data Quality for Ongoing Improvement by Sebastian-Coleman, Laura -- Read -- Imperial Library of Trantor

Index

Cover image Title page Table of Contents Copyright Dedication Acknowledgments Foreword Author Biography Introduction: Measuring Data Quality for Ongoing Improvement

Data Quality Measurement: the Problem we are Trying to Solve Recurring Challenges in the Context of Data Quality DQAF: the Data Quality Assessment Framework Overview of Measuring Data Quality for Ongoing Improvement Intended Audience What Measuring Data Quality for Ongoing Improvement Does Not Do Why I Wrote Measuring Data Quality for Ongoing Improvement

Section 1. Concepts and Definitions

Chapter 1. Data

Purpose Data Data as Representation Data as Facts Data as a Product Data as Input to Analyses Data and Expectations Information Concluding Thoughts

Chapter 2. Data, People, and Systems

Purpose Enterprise or Organization IT and the Business Data Producers Data Consumers Data Brokers Data Stewards and Data Stewardship Data Owners Data Ownership and Data Governance IT, the Business, and Data Owners, Redux Data Quality Program Team Stakeholder Systems and System Design Concluding Thoughts

Chapter 3. Data Management, Models, and Metadata

Purpose Data Management Database, Data Warehouse, Data Asset, Dataset Source System, Target System, System of Record Data Models Types of Data Models Physical Characteristics of Data Metadata Metadata as Explicit Knowledge Data Chain and Information Life Cycle Data Lineage and Data Provenance Concluding Thoughts

Chapter 4. Data Quality and Measurement

Purpose Data Quality Data Quality Dimensions Measurement Measurement as Data Data Quality Measurement and the Business/IT Divide Characteristics of Effective Measurements Data Quality Assessment Data Quality Dimensions, DQAF Measurement Types, Specific Data Quality Metrics Data Profiling Data Quality Issues and Data Issue Management Reasonability Checks Data Quality Thresholds Process Controls In-line Data Quality Measurement and Monitoring Concluding Thoughts

Section 2. DQAF Concepts and Measurement Types

Chapter 5. DQAF Concepts

Purpose The Problem the DQAF Addresses Data Quality Expectations and Data Management The Scope of the DQAF DQAF Quality Dimensions Defining DQAF Measurement Types Metadata Requirements Objects of Measurement and Assessment Categories Functions in Measurement: Collect, Calculate, Compare Concluding Thoughts

Chapter 6. DQAF Measurement Types

Purpose Consistency of the Data Model Ensuring the Correct Receipt of Data for Processing Inspecting the Condition of Data upon Receipt Assessing the Results of Data Processing Assessing the Validity of Data Content Assessing the Consistency of Data Content Comments on the Placement of In-line Measurements Periodic Measurement of Cross-table Content Integrity Assessing Overall Database Content Assessing Controls and Measurements The Measurement Types: Consolidated Listing Concluding Thoughts

Section 3. Data Assessment Scenarios

Purpose Assessment Scenarios Metadata: Knowledge before Assessment Chapter 7. Initial Data Assessment

Purpose Initial Assessment Input to Initial Assessments Data Expectations Data Profiling Column Property Profiling Structure Profiling Profiling an Existing Data Asset From Profiling to Assessment Deliverables from Initial Assessment Concluding Thoughts

Chapter 8. Assessment in Data Quality Improvement Projects

Purpose Data Quality Improvement Efforts Measurement in Improvement Projects

Chapter 9. Ongoing Measurement

Purpose The Case for Ongoing Measurement Example: Health Care Data Inputs for Ongoing Measurement Criticality and Risk Automation Controls Periodic Measurement Deliverables from Ongoing Measurement In-Line versus Periodic Measurement Concluding Thoughts

Section 4. Applying the DQAF to Data Requirements

Context Chapter 10. Requirements, Risk, Criticality

Purpose Business Requirements Data Quality Requirements and Expected Data Characteristics Data Quality Requirements and Risks to Data Factors Influencing Data Criticality Specifying Data Quality Metrics Concluding Thoughts

Chapter 11. Asking Questions

Purpose Asking Questions Understanding the Project Learning about Source Systems Your Data Consumers’ Requirements The Condition of the Data The Data Model, Transformation Rules, and System Design Measurement Specification Process Concluding Thoughts

Section 5. A Strategic Approach to Data Quality

Chapter 12. Data Quality Strategy

Purpose The Concept of Strategy Systems Strategy, Data Strategy, and Data Quality Strategy Data Quality Strategy and Data Governance Decision Points in the Information Life Cycle General Considerations for Data Quality Strategy Concluding Thoughts

Chapter 13. Directives for Data Quality Strategy

Purpose Directive 1: Obtain Management Commitment to Data Quality Directive 2: Treat Data as an Asset Directive 3: Apply Resources to Focus on Quality Directive 4: Build Explicit Knowledge of Data Directive 5: Treat Data as a Product of Processes that can be Measured and Improved Directive 6: Recognize Quality is Defined by Data Consumers Directive 7: Address the Root Causes of Data Problems Directive 8: Measure Data Quality, Monitor Critical Data Directive 9: Hold Data Producers Accountable for the Quality of their Data (and Knowledge about that Data) Directive 10: Provide Data Consumers with the Knowledge they Require for Data Use Directive 11: Data Needs and Uses will Evolve—Plan for Evolution Directive 12: Data Quality Goes beyond the Data—Build a Culture Focused on Quality Concluding Thoughts: Using the Current State Assessment

Section 6. The DQAF in Depth

Functions for Measurement: Collect, Calculate, Compare Features of the DQAF Measurement Logical Data Model Facets of the DQAF Measurement Types Chapter 14. Functions of Measurement: Collection, Calculation, Comparison

Purpose Functions in Measurement: Collect, Calculate, Compare Collecting Raw Measurement Data Calculating Measurement Data Comparing Measurements to Past History Statistics The Control Chart: A Primary Tool for Statistical Process Control The DQAF and Statistical Process Control Concluding Thoughts

Chapter 15. Features of the DQAF Measurement Logical Model

Purpose Metric Definition and Measurement Result Tables Optional Fields Denominator Fields Automated Thresholds Manual Thresholds Emergency Thresholds Manual or Emergency Thresholds and Results Tables Additional System Requirements Support Requirements Concluding Thoughts

Chapter 16. Facets of the DQAF Measurement Types

Glossary Bibliography Index Online Materials

Appendix A. Measuring the Value of Data Appendix B. Data Quality Dimensions

Purpose Richard Wang’s and Diane Strong’s Data Quality Framework, 1996 Thomas Redman’s Dimensions of Data Quality, 1996 Larry English’s Information Quality Characteristics and Measures, 1999

Appendix C. Completeness, Consistency, and Integrity of the Data Model

Purpose Process Input and Output High-Level Assessment Detailed Assessment Quality of Definitions Summary

Appendix D. Prediction, Error, and Shewhart’s Lost Disciple, Kristo Ivanov

Purpose Limitations of the Communications Model of Information Quality Error, Prediction, and Scientific Measurement What Do We Learn from Ivanov? Ivanov’s Concept of the System as Model

Appendix E. Quality Improvement and Data Quality

Purpose A Brief History of Quality Improvement Process Improvement Tools Implications for Data Quality Limitations of the Data as Product Metaphor Concluding Thoughts: Building Quality in Means Building Knowledge in

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