Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework

by Laura Sebastian-Coleman

The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies.

376 pages, Paperback

First published January 1, 2012

Reviews

[Rupa Mahanti · Rating 5 out of 5]

"Measuring Data Quality for Ongoing Improvement" has been one of my reference books on data quality. Data Quality is often mistaken as a wholly technical endeavour and while technical implementation is an important component, data quality is much more that. This is a technology agnostic book and is divided into six sections. The first section focus on data concepts and definitions from different angles, necessary to set the stage for the remainder of the sections and topics. Section two introduces the DQAF (Data Quality Assessment Framework) and section three presents data assessment scenarios. Section four of the book applies the DQAF to data requirements and section five discusses data strategy and section six discusses DQAF in detail.
This is a great book if you want to understand data concepts, data quality measurement, data quality processes and assessment framework and data quality strategy and want to improve data quality in your organisation. I like the writing style of the author and the way the book is organised and logical flow of the content. The book starts with a high-level overview, then drills down to more granular details with the early chapters providing foundation for the later discussions in an extremely logical manner. The book has useful flowcharts and diagram that make the content easy to understand and apply/tailor to your own specific scenarios. It balances concepts with examples from healthcare sector so if you are working healthcare, it is even more valuable. This is a must have book for data practitioners and anybody who wants to have a technology agnostic view of data quality which provides a base for the technical implementations.