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

What this book covers

Chapter 1, The Impact of Data Quality on Organizations, explains the importance of data quality and defines what is meant by bad data.

Chapter 2, The Basics of Data Quality, explains key data quality concepts, including the typical roles involved, the data quality improvement cycle, and the overall fit of a data quality initiative into a wider data management program and organization.

Chapter 3, The Business Case for Data Quality, explains how to calculate the costs and benefits of a data quality initiative, combining these with qualitative matters into a compelling business case for funding.

Chapter 4, Getting Started With a Data Quality Initiative, identifies the activities which are required immediately after a business case approval, such as supplier and tool selection, hiring, early remediation activities and planning. It provides a framework to ensure that all these activities make progress at the required rate early on.

Chapter 5, Data Discovery, explains how to understand business strategy and how it links to data, processes, and analytics. Once this is understood, the chapter explains how to perform a data profile and interpret the results to derive the first data quality rules.

Chapter 6, Data Quality Rules, explains how to derive a full set of business data quality rules, covering all the key elements including defining rule scope, thresholds, dimensions, and weightings. Well developed rules identify the data which does not meet the required standard efficiently and in a repeatable fashion.

Chapter 7, Monitoring Data Against Rules, outlines the various dashboards and reports required to efficiently and effectively monitor data quality against business rules.

Chapter 8, Data Quality Remediation, explains how to use the data quality dashboards and reports to prioritize and then deliver data quality improvement activities.

Chapter 9, Embedding Data Quality into Organizations, describes how to ensure that data quality improvement does not finish when the active initiative ends, , by ensuring it becomes part of day-to-day business practices.

Chapter 10, Best Practices and Common Mistakes, outlines the key best practices for a successful data quality initiative and the common mistakes that reduce the effectiveness of the work. The book ends with an analysis of how new technology such as generative AI will impact work in this field.