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 key features of data quality rules

Now that data quality rules have been introduced, we will focus on the key features that must be taken into consideration when developing rules for the first time. The following diagram summarizes each of these features:

Figure 6.1 – A reference diagram for the key features of data quality rules

Figure 6.1 – A reference diagram for the key features of data quality rules

Each of these concepts needs to be considered when designing a data quality rule. It is important to understand the concepts well before starting the design process to avoid having to revisit every rule and retrofit them later on.

The remainder of this section will explain these concepts in depth and provide examples.

Rule weightings

Rule weightings are used to assign greater or lesser importance to certain rules. Greater weighting will be placed on critical rules. A data quality tool will use the provided weightings when calculating an overall data quality score, such as the following: