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


In this chapter, we learned about what needs to happen with data quality after the intense effort of a budgeted data quality initiative. We learned what causes data quality issues to re-occur and how we can minimize that recurrence. We also learned about the need to keep up with business change and manage the baseline of rules effectively as time passes. Then, we learned about how to transition data quality remediation from a fully managed initiative-based process to an embedded activity in a business as usual team. Finally, we learned how to transition from a single initiative into a longer-term roadmap of activity that fully transforms the data quality of your organization.

We’ve now been through the entire data quality improvement cycle that we outlined in Chapter 2. In the final chapter, we will highlight the key best practices and the most commonly made mistakes in data quality work before finishing this book by looking at how innovation might change the field...