PRACTICAL DATA QUALITY learn practical, real-world strategies to transform the quality of data in your organization
What you will learn Explore data quality and see how it fits within a data management programme Differentiate your organization from its peers through data quality improvement Create a business case and get support for your data quality initiative Find out how business strategy can be linked to proc...
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| Format: | eBook |
| Language: | English |
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Birmingham, UK
Packt Publishing Ltd.
2023
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| Edition: | 1st edition |
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Lack of a data culture
- Prioritizing process speed over data governance
- Mergers and acquisitions
- Summary
- References
- Chapter 2: The Principles of Data Quality
- Data quality in the wider context of data governance
- Data governance as a discipline
- Data governance tools and MDM
- How data quality fits into data governance and MDM
- Generally accepted principles and terminology of data quality
- The basic terms of data quality defined
- Data quality dimensions
- Stakeholders in data quality initiatives
- Different stakeholder types and their roles
- Outlining data quality qualitative risks in depth
- Anticipating leadership challenges
- The "Excel will do the job" challenge
- Ownership of ongoing costs challenge
- The excessive cost challenge
- The "Why do we need a data quality tool?" challenge
- Summary
- Chapter 4: Getting Started with a Data Quality Initiative
- The first few weeks after budget approval
- Key activities in those early weeks
- Understanding data quality workstreams
- Workstreams required early on
- Identifying the right people for your team
- Mapping resources to the workstreams
- Summary
- Part 2
- Understanding and Monitoring the Data That Matters
- Chapter 5: Data Discovery
- An overview of the data discovery process
- Understanding business strategy, objectives, and challenges
- Approaches to stakeholder identification
- Content of stakeholder conversations
- The hierarchy of strategy, objectives, processes, analytics, and data
- Prioritizing using strategy
- Linking challenges to processes, data, and reporting
- Basics of data profiling
- Typical tool data profiling capabilities
- Using these capabilities
- Connecting to data
- Summary
- The data quality improvement cycle
- Business case
- Data discovery
- Rule development
- Monitoring
- Remediation
- Embedding into BAU
- Summary
- References
- Chapter 3: The Business Case for Data Quality
- Activities, components, and costs
- Activities in a data quality initiative
- Early phases
- Planning and business case phase
- Developing quantitative benefit estimates
- Example
- the difficulty of calculating quantitative benefits
- Strategies for quantification
- Developing qualitative benefits
- Surveys and focus groups
- Cover
- Title Page
- Copyright and Credits
- Dedication
- Foreword
- Contributors
- Table of Contents
- Preface
- Part 1
- Getting Started
- Chapter 1: The Impact of Data Quality on Organizations
- The value of this book
- Importance of executive support
- Detailed definition of bad data
- Bad data versus perfect data
- Impact of bad data quality
- Quantification of the impact of bad data
- Impacts of bad data in depth
- Process and efficiency impacts
- Reporting and analytics impacts
- Compliance impacts
- Data differentiation impacts
- Causes of bad data