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

Data Quality Remediation

In the previous chapter, we described how to set up data quality reporting, which allows you to easily identify bad data. This chapter moves on to correcting the data. As explained back in Chapter 1, this does not mean that the organization should aim for perfect data. The aim should be to get the data to the level where it no longer causes significant impediments to the organization achieving its goals.

This is often seen as the most challenging part of the data quality initiative. There is typically a major resource investment and a long lead time to make progress.

In spite of these challenges, this phase is also an exciting one. This is where the organization starts to see the tangible benefits that we attempted to estimate back in Chapter 3. As the bad data is replaced with correct data, the issues experienced prior to the initiative finally start to reduce in severity and impact.

Processes become more efficient, resource challenges driven by poor...