1. Damn that data: A book on world’s fixation with data overcomplicates things, much like big data it criticises

Damn that data: A book on world’s fixation with data overcomplicates things, much like big data it criticises

Christian Madsbjerg in his book, Sensemaking: The Power of the Humanities in the Age of the Algorithm, tries to put a theory to the well-documented fact that companies need someone to put a human element to data, and society at large requires humanities as much as it requires science.

By: | New Delhi | Published: May 14, 2017 3:07 AM
For a critic of big data and the methodical approaches emerging from it, the book ironically follows a methodical approach that most technology books follow.

There can be no better parable than that of telecom major Nokia to illustrate how wrong data can be if it’s not put into perspective. One of the world’s top mobile phone manufacturers at one time, the company missed the bus when it came to smartphones. While Samsung and others were developing new smartphone systems, Nokia stuck to the logic that feature phones were here to stay. The data it got supported that hypothesis. So while on-the-ground reality changed, Nokia didn’t. From a 14-year lead in the mobile phone industry, the company lost 90% of its market share and, within no time, had to shut down and sell its business.

Nokia, however, is not the only company to have suffered like this—there have been many others as well. What the decline ensured, however, is space for ethnographers in Silicon Valley to predict not just what data does, but what it says. The success of companies like Netflix has depended on people who can put a face to data and not just look at numbers.


Christian Madsbjerg in his book, Sensemaking: The Power of the Humanities in the Age of the Algorithm, tries to put a theory to the well-documented fact that companies need someone to put a human element to data, and society at large requires humanities as much as it requires science. Although Madsbjerg does it with a lot of drama and panache, coining a lot of terms along the way and using some from the past, the point remains the same. The author highlights how many companies have been making the same mistake time and again by using more and more big data and reducing humans to data points. He also provides examples of companies that have succeeded in trying a different approach of relying on the human element and, from thereon, you know how the book is going to flow. He defines ‘sensemaking’ as a method of practical wisdom ground in the humanities. “We can think of sensemaking as the exact opposite of algorithmic thinking: it is entirely situated in the concrete, while algorithmic thinking exists in a no-man’s-land of information stripped of its specificity,” he says.

The chapters are divided as per what the author describes as the five principles of sensemaking: Culture—Not Individuals, Thick Data—Not Just Thin Data, The Savannah—Not the Zoo, Creativity—Not Manufacturing and The North Star—Not the GPS. Each chapter focuses on our over-reliance on technology and data while ignoring humanity. Madsbjerg tries to weave a story around each of these, using quotes from fiction writers, scientists and, of course, sociologists and psychologists. The author, in his usual ‘damn-that-data’ manner, says we can’t understand the urgency of sensemaking without dismantling the assumptions upheld in the ‘Silicon Valley’ state of mind.

For a critic of big data and the methodical approaches emerging from it, the book ironically follows a methodical approach that most technology books follow: stating a problem and then deliberating on solutions. Although Madsbjerg tries to weave a story around each of his principles, the book loses the plot in trying to become a literary piece than an analysis of what’s wrong with considering data points. Some examples, like that of investor George Soros, strike a point, but there are too many quotes and inferences all throughout. Madsbjerg, in the end, seems more like a movie buff who quotes inferences from some classics to make his point, only that he does so with sociologists—this, in all fairness, might seem an intelligent approach to some.

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More importantly, Madsbjerg often seems like someone who has a bone to pick with data, as many of his inferences seem too over the top to the point of becoming anti-realist propaganda. There are interesting case studies, but those, too, get lost in the sloganeering. Sensemaking overcomplicates things, much like the big data Madsbjerg criticises.

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