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 Discovery

Regularly in my data quality career, customers and stakeholders have told me that they know their data "inside out". However, from my experience, the application of data profiling will surprise even these stakeholders. For example, at one organization, the procure to pay process owner assured me that no suppliers were on “pay immediately” terms (meaning that invoices would be paid as soon as they were issued). Data profiling revealed that in fact, 40 suppliers were set to these terms, with a total spend of several million dollars being paid immediately instead of accruing interest for the organization.

Data profiling helps to identify the data quality rules that organizations would like their data to comply with by pointing out the “extremities” of the data. Often, these extremities are examples of something that has gone wrong with the data and needs to be corrected.

To detect these extremities, a tool typically evaluates...