Wednesday, April 17, 2019

Book Review: The Model Thinker

The Model Thinker: What You Need to Know to Make Data Work for YouThe Model Thinker: What You Need to Know to Make Data Work for You by Scott E. Page

My rating: 5 of 5 stars

As a professional Business Intelligence Analyst (BIA) this is the perfect non-fiction book for my desk. I do data aggregation, reporting, and analysis at my day job. And we are constantly trying to determine what the best correlation, representation or model is for analysis the data available to us.
Scott E. Page starts us off talking about WHY. This is an often overlooked piece of any business work. The why. Why do we do something? Why do we care? Why use X over Y? And so on...
In this case Page is able to eloquently argue that in today's world of "big data" we need to be more aware of what options are available to us for analysis. It's no longer appropriate to use one model to analysis a problem. Instead we need to leverage the multi-model approach and look at complex issues, problems or phenomena (as he calls them) from many angles. As the world has gotten more complex, our data has gotten larger and more granular; which means we need to look at it from many different perspectives.

Seriously Detailed
I'm on a team of BIA's, many more senior than I, and so I chatted with them about the core concepts in Page's book and we all agreed on one thing. It's comprehensive! If there is a major model not represented in The Model Thinker I'd be shocked. Page does a great job of touching on 30+ models and giving three very specific things for each: 

  • The definition and general usage
  • The actual mathematical breakdown
  • A real-life, relevant example of using the model.

The best part of reading any portion of Page's epic selection of models is easily the examples. From health care to criminal form to food quality to the stock market to population growth; we are given applicable scenarios to understand the nuances of each model and why it's the best choice. Plus there are tons of little tidbits and facts that are fun to share at cocktail parties (or if you're me, in random elevator conversations) in here!

Creativity
There is one thing that really surprised me in The Model Thinker and that is Page's emphasis on creativity. As someone who has a BA in Communications, Marketing & Design, and used to work in the magazine industry, I was surprised to find that my current Analytical career had such a basis in creativity. Reflecting upon Page's statements and my job I realized that he is right. When we program/code, develop visualizations or infer outcomes from data; we are looking at something and creatively manipulating it. Perhaps this explains how I went from an Art Director to a Analyst in one lifetime.

Overall
This is a book that I will be purchasing for my office shelf. I have already gone back to my eARC copy multiple times to look things up and to continue learning the models. It's not a book you will likely read cover to cover at any given time. But the first 50 pages of introduction and concepts are superb and well worth reading in order. After that you can jump around to the models you are most interested in, or if you're looking for the right solution for data crunching, read the intros to each chapter to determine if there might be applicable use to your situation. I know that The Model Thinker has already been picked up by some mainstream Universities and Colleges as a required textbook and certainly I can see why. In one book you gain the knowledge of hundreds of years worth of calculations and analyzation. Whether you currently work as a Data or Business Analyst, have a desire to learn to use big data, are a programmer or just a geek that loves graphs; The Model Thinker is likely to fill a void, you didn't even know existed, by giving you more models, examples and calculations than you will ever need.

Please note: I received an eARC of this book from the publisher via NetGalley. This is an honest and unbiased review.

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1 comment:

Leonore Winterer said...

Seems like a great reference book! I did an introductory course in Big Data once, but that was totally unsatisfying - I think I'd have gained more insight from reading a book like this...