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 the effort and cost

Once the approach to each prioritized data quality issue has been identified, an approximate effort and cost estimate should be prepared, along with a timescale and plan for each issue.

  • Sometimes it may be necessary to re-visit the prioritization at this point. If any of the issues will be exceptionally difficult to resolve, then it might be better to prioritize a different issue with a simpler resolution. This typically happens in the following situations:
  • The approach selected is very manual and will consume more resources than are feasibly available

An approach involving a third party (that is, paying for correct data) is more expensive than initially anticipated

Momentum is important in data quality initiatives. If an issue is problematic, even where the priority is high, it can be better to move on to an issue that can be progressed efficiently.

In order to properly understand the effort and costs involved in remediating...