Peter Thiel, in Zero to One, notes, “Andy Kessler sounds almost gleeful when he explains that the best way to create productivity is ‘to get rid of people.’ Forbes captured a more anxious attitude when it asked readers: Will a machine replace you?”[1]?

And for sure, we live in a world where machines are doing lots of things that people regularly did once. Examples are everywhere…. and almost too obvious to note.  Harvard Business Review notes, “If you doubt the march of worker-replacing technology, look at Foxconn, the world’s largest contract manufacturer. It employs more than one million workers in China. In 2011, the company installed 10,000 robots, called Foxbots. Today, the company is installing them at a rate of 30,000 per year. Each robot costs about $20,000 and is used to perform routine jobs such as spraying, welding, and assembly. On June 26, 2013, Terry Gou, Foxconn’s CEO, told his annual meeting that ‘We have over one million workers. In the future we will add one million robotic workers.’ This means, of course, that the company will avoid hiring those next million human workers.”[2]

The Washington Post queries, “Will smart machines steal our jobs?”[3]

It is an age-old struggle.  Women and man against machine.  Ned Ludd vs. the stocking frames, technologist vs. globalization and machine learning vs. Neo Luddism.   I tend to think generally people will continue to be (very) important even in the age of a driverless car, an internet of everything and sensors that measure innumerable things.  Time will tell if I am right, but when it comes to data analytics, predictive, prescriptive and causal predictive engines, I am virtually certain that there will need to be a symbolic relationship between people and machine, as neither can go it alone.  Or stated differently, a learning machine may be your predictive engine, but it will need a person or persons to point it directionally (i.e., ask high value questions).

Put it this way, super large data is no good if it is not useful.[4]  Data itself has power, but the real advantage of data is using and making it actionable.  And the super advantage of data is when it can predict, shape and inform the future of decision making.

So why are people so important to predictive, prescriptive and causal analytics?   Why I am so sure that for those disciplines, you will need some kind of driver and they will not be driverless?   Nathan Eagle, an adjunct assistant professor at Harvard School of Public Health puts it this way “‘you don’t get good scientific output from throwing everything against the wall and seeing what sticks.’ No matter how much data exists, researchers still need to ask the right questions to create a hypothesis, design a test, and use the data to determine whether that hypothesis is true.”[5]

To analyze and get to the usefulness in data, “we” will need a guide to help frame the right questions.  And I think you will likely always need that guide.  I do not think that I am alone in the prediction (no pun intended).

BuinessNews Daily notes “‘[d]ata can tell you that this ad is better than that one, but human intuition asks why,’ said Mike Mothner, CEO of Wpromote. ‘You need to ask what to improve and why. A machine can’t inherently answer this.’ The key to a successful, data-driven marketing strategy is finding the perfect intersection between technology and human expertise, Mothner said. Powerful tech tools should support and guide, not carry your operations. ‘Humans will always need to play a role in analyzing data,’ added John Huehn, founder and CEO of social media solutions provider, In the Chat.[6]

But I think to really support my conclusion I need only to go back to Peter Thiel, who said it best when he characterized it this way “the most valuable companies in the future won’t ask what problems can be solved with computers alone.  Instead, they’ll ask: how can computers help humans solve hard problems?”[7] (Zero to One, pages 149-150).


[1] Zero to One, page 141

[2] (Harvard Business Review, What Happens to Society When Robots Replace Workers?, William H. Davidow and Michael S. Malone December 10, 2014.

[3] (The Washington Post, Some predict computers will produce a jobless future. Here’s why they’re wrong. James Bessen February 18, 2014,

[4] (Harvard Business Review, Data Is Useless Without the Skills to Analyze It, Jeanne Harris, September 13, 2012,

[5] Harvard Magazine, Why “Big Data” is a Big Deal, by Jonathan Shaw March-April 2014,

[6] (BusinessNewsDaily, Beyond Big Data: Why Human Interpretation Still Counts, Nicole Fallon, May 12, 2014,

[7] Zero to One, pages 149-150[/fusion_builder_column][/fusion_builder_row][/fusion_builder_container]