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

Data Quality Rules

The chapters so far have been about understanding how to shape your data quality initiative – who should be consulted, how to win their support, and how to ensure you focus on the right areas.

Having used data discovery techniques in the previous chapter to identify critical data and identify its flaws, it is now time to define data quality rules. This moves the work into a critical phase, as the rules lead to a data quality score that, ultimately, people will judge an organization’s data against.

This chapter will help you write a clearly understandable business definition of a rule, which can then be converted into a programmatic check of data with a data quality tool. We will explore all the different features of a rule, such as rule thresholds, how they are assigned to data quality dimensions, assigning a monetary value to a rule failure, and weighting important rules over others.

In this chapter, we will cover the following topics:

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