<p style="font-weight:700;color:rgb(15,17,17);font-family:'Amazon Ember', Arial, sans-serif;font-size:14px;font-style:normal;background-color:rgb(255,255,255);"><span style="font-weight:700;">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</span></p><span>Key Features</span><ul><li><span style="color:rgb(15,17,17);"><span>Get a practical explanation of data quality concepts and the imperative for change when data is poor</span></span></li><li><span style="color:rgb(15,17,17);"><span>Gain insights into linking business objectives and data to drive the right data quality priorities</span></span></li><li><span style="color:rgb(15,17,17);"><span>Explore the data quality lifecycle and accelerate improvement with the help of real-world examples</span></span></li><li><span style="color:rgb(15,17,17);"><span>Purchase of the print or Kindle book includes a free PDF eBook</span></span></li></ul><span>Book Description</span><p style="color:rgb(15,17,17);font-family:'Amazon Ember', Arial, sans-serif;font-size:14px;font-style:normal;font-weight:400;background-color:rgb(255,255,255);"><span>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.</span></p><p style="color:rgb(15,17,17);font-family:'Amazon Ember', Arial, sans-serif;font-size:14px;font-style:normal;font-weight:400;background-color:rgb(255,255,255);"><span>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.</span></p><p style="color:rgb(15,17,17);font-family:'Amazon Ember', Arial, sans-serif;font-size:14px;font-style:normal;font-weight:400;background-color:rgb(255,255,255);"><span>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.</span></p><span>What you will learn</span><ul><li><span style="color:rgb(15,17,17);"><span>Explore data quality and see how it fits within a data management programme</span></span></li><li><span style="color:rgb(15,17,17);"><span>Differentiate your organization from its peers through data quality improvement</span></span></li><li><span style="color:rgb(15,17,17);"><span>Create a business case and get support for your data quality initiative</span></span></li><li><span style="color:rgb(15,17,17);"><span>Find out how business strategy can be linked to processes, analytics, and data to derive only the most important data quality rules</span></span></li><li><span style="color:rgb(15,17,17);"><span>Monitor data through engaging, business-friendly data quality dashboards</span></span></li><li><span style="color:rgb(15,17,17);"><span>Integrate data quality into everyday business activities to help achieve goals</span></span></li><li><span style="color:rgb(15,17,17);"><span>Avoid common mistakes when implementing data quality practices</span></span></li></ul><span>Who this book is for</span><p style="color:rgb(15,17,17);font-family:'Amazon Ember', Arial, sans-serif;font-size:14px;font-style:normal;font-weight:400;background-color:rgb(255,255,255);"><span>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.</span></p><span>Table of Contents</span><ol><li><span style="color:rgb(15,17,17);"><span>The Impact of Data Quality on Organizations</span></span></li><li><span style="color:rgb(15,17,17);"><span>The Basics of Data Quality</span></span></li><li><span style="color:rgb(15,17,17);"><span>The Business Case for Data Quality</span></span></li><li><span style="color:rgb(15,17,17);"><span>Data Quality Roles and Their Challenges</span></span></li><li><span style="color:rgb(15,17,17);"><span>Data Discovery</span></span></li><li><span style="color:rgb(15,17,17);"><span>Data Quality Rules</span></span></li><li><span style="color:rgb(15,17,17);"><span>Monitoring Data Against Rules</span></span></li><li><span style="color:rgb(15,17,17);"><span>Data Quality Remediation</span></span></li><li><span style="color:rgb(15,17,17);"><span>Embedding Data Quality into Organizations</span></span></li><li><span style="color:rgb(15,17,17);"><span>Best Practices and Common Mistakes</span></span></li></ol>