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jmarquardt

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Jul 21, 2014

What are your challenges with information quality? Tell us and you could win a book

In their recent book, Information Quality: The Potential of Data and Analytics to Generate Knowledge, authors Ron Kenett and Galit Shmueli provide a framework (InfoQ) for data analysts to assess data quality and the potential to find meaningful insights.

 

They explain how to integrate the notions of goal, data, analysis and utility – the building blocks of data analysis in any domain. And they discuss issues surrounding the implementation of InfoQ in academic programs and business or industrial projects.


Resized_20170203_110248.jpegSo what are some of the challenges you face with information quality? How are you facing those challenges? If you leave me a comment by 20 February on this blog post explaining your information quality challenges, you could win a copy of Kenett and Shmueli’s new book. Your comment should be between 50 and 75 words long. We will randomly select two winners from the eligible comments. Only one book per commenter, and only commenters in the US and Canada are eligible to receive the prize.

 

Of course, only two people will win. But you can buy the book at a 20 percent discount using the code VBN54. Bring it with you to Discovery Summit Europe, where the authors are featured keynotes, and you can have it signed by Kenett and Shmueli!

 

What the experts are saying about this book:

 

"Kenett and Shmueli shed light on the biggest contributor to erroneous conclusions in research – poor information quality coming out of a study. This issue – made worse by the advent of Big Data – has received too little attention in the literature and the classroom. Information quality issues can completely undermine the utility and credibility of a study, yet researchers typically deal with it in an ad-hoc, offhand fashion, often when it is too late. Information Quality offers a sensible framework for ensuring that the data going into a study can effectively answer the questions being asked." Peter Bruce, The Institute for Statistics Education.

 

"Policy makers rely on high quality and relevant data to make decisions and it is important that, as more and different types of data become available, we are mindful of all aspects of the quality of the information provided. This includes not only statistical quality, but other dimensions as outlined in this book including, very importantly, whether the data and analyses answer the relevant questions." John Pullinger, National Statistician, UK Statistics Authority, London, UK

 

"A glance at the statistics shelves of any technical library will reveal that most books focus narrowly on the details of data analytic methods. The same is true of almost all statistics teaching. This volume will help to rectify that oversight. It will provide readers with insight into and understanding of other key parts of empirical analysis, parts which are vital if studies are to yield valid, accurate, and useful conclusions." Professor David Hand, Imperial College, London, UK

3 Comments

One challenge I encountered often was the lack of operational definitions (or worse yet, multiple operational definitions) for a critical to the business metric. For example, for 4 years I worked as a sales forecaster for a large multinational company. On a monthly basis I had to prepare multiple reports for internal constituencies within the business. Folks from marketing, sales, distribution, manufacturing, supply chain, you name it these folks were in our monthly operating reviews. You'd be amazed at how many different definitions people had in their minds for the concept of 'sales'. One simple example, one project manager counted non binding letters of intent from customers as a 'sale'. I counted a 'sale' when an accounts receivable entry was created. Supply chain counted a 'sale' when the product physically moved from last point of company title ownership to the goods. Try taking that to Finance and having them count a letter of intent? Or take that to supply chain...and they wonder why inventory demand doesn't match what they are seeing? So spend some time understanding these operational definitions before making business decisions...and pick the definitions that match the business needs or requirements.

Thanks for sharing your experience, @Peter_Bartell