“Heuristics in Analytics” reviewed

Heuristics in Analytics

By Carlos Andre Reis Pinheiro and Fiona McNeill. Wiley, 2014

With all the hype about big data, it’s refreshing to find a book that discusses the practical aspects of analytics. Heuristics in Analytics makes a clear case for adding human experience and common sense to technology in order to solve real world business problems.  Written in clear, non-mathematical language, the book explains how using heuristics together with analytics is often the fastest way to deliver decisions that are suitable for a specific use case, and quickly enough to fit the fast pace of business. The descriptions of heuristics concepts and guidance for how to use an heuristic approach to analytics should make this book a valuable addition to the manager’s and practitioner’s libraries.

With its roots in statistics, analytics tends to be highly theoretical and analyses are often misunderstood.  One of the concepts that the marketplace has difficulty understanding is that analytics looks at trends, and that therefore there will be outliers and inconsistencies within any data set; that we are examining a collection of data in the aggregate and that each data point can not be expected to fit what many perceive to be a rule. Heuristics in Analytics makes this point adeptly:

“Unexpected events will always take place, and they will always impact predicted outcomes. Analytical models work well for the majority of cases, for most of the observations, and in most applications. Unexpected or uncontrolled events will always occur and typically affect a few observations within the entire modeling scenario.”

Many a sentiment analysis tool has been rejected because we expect precision and accuracy from it rather than broad trends. Marketing managers remain suspicious because they find errors in classification, not realizing that people are just as error-prone.  The difference is that computers make dumb computer errors, while human errors in judgment may be due to differences in interpretation, in bias or pure exhaustion at the end of a long day.

I hope that Heuristics in Analytics will help to correct some of this misunderstanding.  And like its authors, I also recommend the seminal works they note:

  • Leonard Mlodinow: The Drunkard’s Walk:  How Randomness Rules Our Lives
  • Malcolm Gladwell:  Blink: The Power of Thinking Without Thinking

To these I’d add a couple of my own favorites:

  • Mitchell Waldrop:  Complexity: The Emerging  Science at the Edge of Order and Chaos. New York: Simon and Schuster. (1992)
  • Nate Silver:  The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t
  • Nicholas Nassim Taleb.  The Black Swan

All of these address the role that probability plays (or should play) in how we make decisions.

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