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


This chapter has been about ensuring that all readers have a common understanding of the key underlying concepts in the book. Data quality initiatives are normally a subset of the work that happens in a data management team, and therefore, a thorough understanding of data management, and particularly data governance, is important.

The success of any data quality initiative relies on the interest and support of stakeholders at all levels. The chapter has outlined all these roles and how they can help the initiative.

Finally, the chapter outlined the end-to-end process that has helped me be successful with data quality work in my career to date.

The next chapter will describe arguably the most difficult phase of the end-to-end process – the business case for data quality. Most initiatives fail at this point. Indeed, some of my initiatives failed at this point, and the lessons I learned (the hard way) inform what will be discussed.