Book Image

Practical Data Quality

By : Robert Hawker
Book Image

Practical Data Quality

By: Robert Hawker

Overview of this book

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.
Table of Contents (16 chapters)
1
Part 1 – Getting Started
6
Part 2 – Understanding and Monitoring the Data That Matters
10
Part 3 – Improving Data Quality for the Long Term

Preface

Practical Data Quality is about how to take your organization from a basic awareness of a data quality problem to a position of having data good enough to truly underpin success.

The book begins by explaining how bad data can affect an organization’s process efficiency, decision-making, and ability to remain compliant. It then establishes the key concepts you need to understand to be successful with data quality and the end-to-end process I have used to transform data throughout my career.

The book goes on to explain each step of the data quality journey, starting with creating a business case and managing the hectic period at the start of an initiative. Then the book establishes the typical stakeholders you will need to engage with through the process, how to work with them to identify which data to focus on, and the specific rules that the data should comply with.

Next, it shows how to monitor data against the rules that have been established and how to actually start correcting the data.

To close, the book explains how to embed good data quality practices into the day-to-day activities of your organization and outlines best practices and challenges to be avoided in your work.

By the end of the book, you will have a complete outline of how you can transform data quality in your organization, armed with examples to catch the interest of your stakeholders, and templates to accelerate your work.