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

Identifying the approach to remediation

Now that the priorities are understood, it is time to work on the approach to remediating the bad data. There are a number of different approaches that can be applied and the effort involved varies hugely.

Typically, each prioritized rule can be categorized into a particular approach. Most often, only one approach will apply to each issue. Sometimes there might be the possibility to apply two or more approaches to a particular issue.

For example, if supplier email addresses are missing in the ERP system to send remittance advice details, three approaches might apply:

  1. The data might be in another system (for example, a contract management system) for 40% of the vendors who are missing the data. For these, the data would be migrated across to the ERP system in a batch.
  2. The data might be available on previous supplier invoices for a further 40% of the vendors and could be collected and keyed in.
  3. The data might have to be collected...