| Description |
1 online resource |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
Identify data quality issues, leverage real-world examples and templates to drive change, and unlock the benefits of improved data in processes and decision-making Key Features Get a practical explanation of data quality concepts and the imperative for change when data is poor Gain insights into linking business objectives and data to drive the right data quality priorities Explore the data quality lifecycle and accelerate improvement with the help of real-world examples Purchase of the print or Kindle book includes a free PDF eBook Book Description Poor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating. Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you'll work with real-world examples and utilize re-usable templates to accelerate your initiatives. By the end of this book, you'll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace. 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 processes, analytics, and data to derive only the most important data quality rules Monitor data through engaging, business-friendly data quality dashboards Integrate data quality into everyday business activities to help achieve goals Avoid common mistakes when implementing data quality practices Who this book is for This book is for data analysts, data engineers, and chief data officers looking to understand data quality practices and their implementation in their organization. This book will also be helpful for business leaders who see data adversely affecting their success and data teams that want to optimize their data quality approach. No prior knowledge of data quality basics is required |
| Notes |
Includes index |
| Subject |
Database management.
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Database design.
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Electronic data processing -- Management
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Information technology -- Management.
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Database design
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Database management
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Electronic data processing -- Management
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Information technology -- Management
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| Form |
Electronic book
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| Author |
Askham, Nicola, writer of foreword
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| ISBN |
9781804619438 |
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1804619434 |
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