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

Overall remediation process

The overall process of remediation is typically cyclical in nature. It is usually not possible to work on all the issues uncovered by data quality reporting at the same time. The remediation is usually handled in tranches.

Our process for remediation has the following steps:

Figure 8.1 – End-to-end process of remediation

Figure 8.1 – End-to-end process of remediation

The following table provides a more detailed description of each step:

Step Name



Identification of the most important data quality failures so that these can be targeted early.

Identify the approach

There are a number of different ways to remediate data. Here are some examples:

  • Manual record-by-record corrections
  • Collection/upload of data from a third party
  • Automatic...