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

Moving remediation to business as usual

In cases where an automated or mass correction approach is applied, often it does not correct all of the data. There may be a difficult 20% of bad data that cannot be automatically matched and where a second approach has to be implemented. Often, difficult decisions need to be made on how far to go in correcting the data. For example, that last 20% might use a manual remediation approach such as 6 or 7. That might be so time-consuming that the cost of implementing it exceeds the benefit. In these situations, it may be most appropriate to apply the approach that gives 80% value and accept (temporarily at least!) the remaining data quality challenge. A “business as usual” remediation method could be applied for the remaining 20%.

To make this a bit clearer, here are further details on the real example in Table 8.5 where supplier bank details were missing:

  • An organization’s ERP system found 65% of its suppliers were...