Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework (The Morgan Kaufmann Series on Business Intelligence)

Overall, this is a really great book, especially if you're working in healthcare. While there are data quality assessment frameworks available without buying an expensive book, the author has done a great job of making it relevant and useful (and not available on the internet so you have to buy the book).Generally the book is easy to follow and comes with a lot of useful information. My one complaint is that there should be a website where you can pull diagrams and charts from the book (with license restrictions, of course), so that if I want to take them into a meeting and show them I don't have to recreate them from scratch.

I can see myself referencing this book later in my career, if not for clarity and contextualization around data quality.

This book has some good ideas. However, it comes across as an academic research paper rather than trying to convey ideas to folks in normal day-to-day business. It is a VERY difficult read --- to the point that the value is completely lost.

Many systems are developed with Data Quality as an after-thought. The writer clearly outlines the reasons why Data Quality should be thought of as a strategy, not just a one-time activity or the result of using a specific methodology (e.g., Profiling).This should be required reading for Data Quality Practioners, and other related data stakeholders such as Data Architects, Data Modelers, and others who lead data warehousing projects. Those who are in the beginning phases of a project need to understand that this is a shared responsibility and need to structure systems to incorporate appropriate strategies from the onset.The writer has a background in communications and developing web content. Because of this, the writing is well-organized and supremely logical. The book starts with a high-level overview, then drills down to more specific details. For me, it was a little frustrating because I like to jump in "feet first" and get details rapidly. But I found that slowing down and studying the early sections/chapters provided a good foundation for the later material.Be aware that the book may not meet all of your needs and expectations. In the introduction, the author makes an important statement: "...it is important also to point out what the book will not do. It does not, for example, present 'code' for implementing these measurmnents. Although it contains a lot of technically oriented information, it is not a blueprint for a technical implementation. In defining requirements for measurement types, it remains business-oriented and technology independent. It also does not advocate for the use of particular tools."The strength of this book lies in the author's statement: "Many people want to buy tools before they define their goals for measuring. I feel very strongly that people need to know what they are trying to accomplish before they use a tool."So, while this is a great resource for developing a vision and strategy, I still am looking for more information regarding execution of a strategy. There are some recommended books listed, and I've already ordered one of them (Danette McGilvary's book, "Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information").

I have been recently tasked with putting together a plan to improve the quality of data at my place or work, and decided to start researching with some books. Unfortunately there is not a lot available in this field, and jumped on this because of the higher ratings. Unfortunately I could barely make it more than a few chapters through it: the author has this tendency to take a simple concept and spend several pages explaining it with run-on sentences and few examples. Its almost like she wrote a college paper and had to pad it to meet an arbitrary word count. For me, I felt like I was getting dragged in circles and didn't really get anything useful. There might be useful information here but its going to be like an archaeological dig to extract it.

As a business person, this book has forever changed the way I look at data, as well as my perspective on data that’s presented to me. When reading this book, 2 points in particular impressed me.First, it’s true that Business and Technical (IT) people tend to view data differently. IT people tend towards the technical aspects of the data, whereas Business people don’t care so much about the numbers as they do about what the numbers say in terms of business impact. Sebastian-Coleman hits home on the “meeting of the minds” between IT and Business people so they can work together to get the most out of their data.Second, the “data quality dimensions” are a key component in understanding the quality of your data. However, they are conceptual in nature and can be difficult to relate to, particularly for Business people. The DQAF (Data Quality Assessment Framework) outlined in this book presents a dissection of the “Data Quality Dimensions” into a practical, generic “menu” that can serve as a great starting point for any company to begin developing a set of measurements integral to a good data quality program.This book is a “must read” for anyone - IT or Business - working in the data space today.

Muy buena referencia para implementar metricas de data quality! Te muestra algunos ejemplos prácticos y no se requiere de un nivel técnico alto para la comprension de el data quality.

This book will not be the last word on this rapidly evolving field. However, I am grateful to the author as she has given me a framework from which to work with in my own work.

Interessante... traz uma visão prática de QD e isto ajuda bastante na aplicação desta disciplina. Há está carência na literatura em geral sobre este tema.

Very clear and useful guide, as far as I have so far had time to read it. Well illustrated with examples, and relevant references.

Great product. As per expectations.