Data is an increasingly important business asset and enabler for organisational activities. With growth in data sets and data volumes, it's becoming ever harder to manage. Data quality - the fitness for purpose of data - is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability. This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.
Managing Data Quality- A practical guide
| Tim King (Author), Julian Schwarzenbach (Author) | BCS, The Chartered Institute for IT
Table of Contents
Part 1. The Challenge of Enterprise Data
Chapter 1. THE DATA ASSET
“What are data?”, “What is data quality?” ,“What is data quality management?”, Summary .
Chapter 2. CHALLENGES WHEN EXPLOITING AND MANAGING DATA 15
The complex data landscape, Complex decisions, Virtuous circle or downward spiral, Unclear data ownership, Backups and data quality, Data quality and lack of transparency in business cases, The data triangle, Data as a raw material, The data machine: expectations vs reality, “Do your data trust you?”, The challenge of managing enterprise data quality, Summary.
Chapter 3. THE IMPACT OF PEOPLE ON DATA QUALITY
Comparisons between data quality and health and safety, People and data, The Data Zoo, How data behaviours interact, Individuals as part of a team, Teams within the organisation, Data demotivators, Summary.
Chapter 4. CASE STUDIES AND EXAMPLES
Real-world examples of the impacts of poor data
Case study – Mars Climate Orbiter
Case study – Maintenance productivity targets degrading data quality
Case study – Railtrack
Case study – Statutory reporting
Case study – Oversized trains
Case study – Retail fail
Case study – Inappropriate controls and haste degraded data quality,
Summary.
PART II. A FRAMEWORK FOR DATA QUALITY MANAGEMENT
Chapter 5. THE PURPOSE AND SCOPE OF DATA QUALITY MANAGEMENT
The difference between data management and data quality, management, Key principles for data quality management, Summary.
Chapter 6. THE ISO 8000-61 APPROACH 5
The scope of ISO 8000-61, The processes in ISO 8000-61, Summary.
Chapter 7. DATA QUALITY MANAGEMENT CAPABILITY LEVELS 6
Capability Level 1, Capability Level 2, Capability Level 3, Capability Level 4, Capability Level 5, Overall capability model, Summary.
Chapter 8. ISO 8000-61 PROCESSES
Data processing 69, Provision of data specifications and work instructions, Data quality monitoring and control, Data quality planning, Data-related support, Resource provision, Data quality assurance, Data quality improvement, Summary.
Chapter 9. THE MATURITY JOURNEY
Planning the journey, Assessing maturity, Summary.
PART III. IMPLEMENTING DATA QUALITY MANAGEMENT
Chapter 10. PREP ARING THE ORGANISATION FOR DATA QUALITY MANAGEMENT
What does a data-enabled organisation look like?, Improvement opportunities in typical organisations, The data quality management journey, The case for change, The changing organisation, The role of the chief data officer, Preparing the organisation, Summary.
Chapter 11. IMPLEMENTING DATA QUALITY MANAGEMENT
Overall approach to data quality management implementation, Senior-level sponsorship, Understand the context, Identify synergies, Choose an implementation approach, Agree the ‘footprint’, Change management, Ethical use of data, Dealing with challenges and issues, De-risk existing projects, Securing budget and resources, Starting implementation, Summary.
Chapter 12. THE HUMAN FACTOR – ENSURING PEOPLE SUPPORT DATA QUALITY MANAGEMENT
People are the solution, Behaviours and culture, The employee data agreement, Strategies for changing data behaviours, Organisational influences on behaviours, Summary, Conclusions.
LINK FOR THE BOOK
