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

Prioritizing remediation activities

When you first run your data quality Rule Results Report (or your equivalent), it may be a little overwhelming. There will be failed records for every rule and sometimes the failed records may add up to many thousands. It is not uncommon in larger businesses for 250,000 or more records to fail a rule. For example, if a fast-moving consumer goods organization has a reward card scheme, it can easily have millions of customers. One of the largest of these schemes in the UK has 18 million customers. It would only take a single missing validation on an online enrollment form to generate large quantities of failed data as customers make mistakes when entering data. One organization we worked with required the date of birth of the customer, but did not validate what was entered. Around 1% of customers entered the correct day and month of birth but accidentally entered the current year instead of their birth year. The form was missing a simple validation...