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

The first few weeks after budget approval

In Chapter 2, the data quality improvement cycle outlined the activities that were then costed and planned in Chapter 3. For simplicity, the cycle and the example project plans in those chapters show each of the activities happening in a sequence – generally starting only after another has been completed.

The reality is somewhat different though, for example:

  • While working on data discovery work (covered in Chapter 5), you will inevitably uncover some live and critical data quality issues that cannot wait until the remediation phase to be addressed.
  • Typically, the phase where data quality rules are built by developers will start while design work is still going on. Usually, groups of data quality rules that were agreed upon at the start of the design phase are released to developers to work on in parallel to the design work – to extend the development phase and to allow for some refinement/clarification of the rules...