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

Common mistakes

Data quality initiatives are not easy and unfortunately many fail to deliver real value. I have had many difficult moments over the last 16 years where mistakes have been made. I have seen initiatives that have done the following:

  • Overspent or been delayed
  • Never been completed
  • Delivered as planned but failed to capitalize on greater opportunities that presented themselves during implementation work due to a lack of flexibility

This section reflects what was learned from each of these experiences.

Failure to implement best practices

This section is noticeably briefer than the best practices section. This is partly because many of the common mistakes in data quality initiatives involve a failure to identify and implement the best practices. In other words, the mistake is the inverse of the best practice.

This section will highlight just two of the best practices from the previous section and outline the potential cost of missing these....