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

Understanding data quality workstreams

In the previous section, I outlined the following activities, which all need to be started in the first few weeks after the business case is approved:

  • Supplier selection for initiative resources, such as data quality rule developers
  • Data quality tool selection
  • Detailed planning for the data discovery phase, including engagement with IT to get the data quality tool ready for profiling work
  • Internal hiring
  • Communication

In addition to this, it is highly likely that, in the first data discovery sessions, there will be immediate reports of known data quality issues that are causing significant or even severe impacts on the effectiveness of the organization. For example, at one organization, where my team had committed to work on HR data quality, the very first data discovery meeting uncovered that the data required for the calculation of the annual employee bonus was not of a high enough quality. The bonus calculation...