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

Ongoing data quality rule improvement

Once a data quality initiative is completed and a set of valuable rules are in place, it is critical to maintain these. It would be wonderful if the rules could remain consistent for a few years at least, but in my experience, this is never the case. Some rules may stay consistent for 10 years, while others will change within months (or even weeks!) of being initially established. The rules that stay consistent for longer are generally those that address long-standing legislative requirements such as taxation. Those that change more regularly are those most closely tied to how the business operates.

For example, product data tends to evolve quite quickly. If you consider an organization that makes technology products such as mobile phones, the level of change will be high. For example, in 2010, the network capabilities of a phone were limited to 2G and 3G and a rule might have checked that every handset had one of these values. In 2023, 4G and...