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)
1
Part 1 – Getting Started
6
Part 2 – Understanding and Monitoring the Data That Matters
10
Part 3 – Improving Data Quality for the Long Term

Tracking benefits

Remediation activities are very time-consuming and challenging. It is very common for data quality initiatives to be so focused on this activity that they do not manage stakeholders properly at this stage. The initiative has promised benefits in the business case stage (even if just qualitative benefits). The benefits may have been used to persuade leaders to take resources away from other work to be dedicated to remediation.

It is therefore often very important to start to show that the promised benefits have actually been delivered by the remediation. Where this is done well, you will see the following:

  • Leaders encouraging you to continue on to the next process area or data domain
  • Previously reluctant stakeholders asking for their area to be added to the roadmap
  • Increased investment in related data activities – such as analytics – because the level of confidence in data increases
  • Additional areas appointing data stewards/data...