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)
Part 1 – Getting Started
Part 2 – Understanding and Monitoring the Data That Matters
Part 3 – Improving Data Quality for the Long Term


The early part of this chapter outlined how to properly understand the business strategy of a business. If this element goes well and has the right support, an organization will feel that the data quality initiative truly understands the priorities of the business. This breeds confidence that the work done on data quality will be focused on the right aspects.

The chapter also outlined how to use the information from this discovery phase to properly research the root cause of challenges that impact the strategy. It also outlined how to link these challenges to processes, analytics, and data.

All of this has informed which data to profile. The chapter covered the main outputs that profiling provides and the potential data quality rules it can generate. This is likely to have revealed some surprises about data, even to those who use it every day. The maturity of the data conversation has now reached the point where data quality rules can be fully developed.