Thinking with Data: How to Turn Information into Insights

Author: Max Shron
Pages: 94
Publisher: O’Reilly Media
ISBN: 1449362931


With the advent of computer systems and the Internet, data has become plentiful and easily accessible. The problem now is to separate the wheat from the chaff, and discover what of it is useful. This book explains how.

About the author

Max Shron runs a data strategy consultancy in New York. His analyses of transit, public health, and housing markets has been featured in The New York Times, Chicago Tribune, and more. Prior to becoming a data strategy consultant, he was the data scientist for OkCupid.

Inside the book

“Even the briefest of projects benefit from some time spent thinking up front,” points out the author, and in this book explains the importance of answering the questions of what goal we want to achieve and why, in order to define how to do it.

The book is slim, only 94 pages, but not a word in it is too much or too little, and this fact adds to the feeling that the author really knows how to order his thoughts and will easily teach us how to do it, as well.

The author did not set to invent the wheel again, and acknowledges how the problem of turning observations into knowledge has been worked on again and again by experts in a variety of disciplines. He uses these previous established frameworks, methods of thinking and offering and proving arguments and adapts them to data science and the discipline of data analysis.

This is not a book that concentrates in any way on the techniques of data manipulation and interpretation. Instead, it addresses the – the author argues – even more important skills of determining what problem one is trying to solve, and how to go about it in a logical manner that improves (if not guarantees) the likelihood of usable results.

“The fields of design, argument studies, critical thinking, national intelligence, problem-solving heuristics, education theory, program evaluation, various parts of the humanities – each of them have insights that data science can learn from,” he points out, and proceeds to explain the use of each.

Asking good questions, and many of them, is ultimately the basis of making a good plan, and the book explains both in theory and with several concrete examples what a difference this approach can make.

Final thoughts

Rarely has the argument for clearly defining and stating our goals before starting on a project been so well put and advocated, and the process of doing it so clearly explained. This is one of the rare books that I started reading for a second time as soon as a finished perusing it for the first. I recommend it wholeheartedly.

The book also includes a small appendix that recommends further reading materials, and I will definitely go through that list.

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