Looking around northern California and inside many technology kitchens makes me believe that we are about to see the Data Scientist bubble burst. And then I read the Fortune Magazine article on Peter Thiel - and the excerpt on Zero to One (his new book) in that article and it dawned on me that is one of the intersting ways to look at the Data Scientist bubble.
Thiel's Classification of Innovation
Without trying to simplify and/or bastardize mr. Thiel's theory, the example in the Fortune Mag article will make this visible to most people (I hope). In the article the analogy is; going from one type writer to 100 type writers is 1 to N, inventing a word processor is moving us from 0 to 1. In other words, true innovation dramatically changes things by giving previously unknown power to the masses. It is that innovation that moves us
from 0 to 1. Expansion of existing ideas - not true innovation - moves
us from 1 to N. Of course, don't take my word on this but read the article or the book...
The Demise of the Human Data Scientist
The above paradigm explains the Data Scientist bubble quite nicely. Once upon a time companies hired a few PhD students who by chance had a degree in statistics and had learned how to program and figured out how to deal with (large) data sets. These newly minted data scientists proved that there is potential value in mashing data together, running analytics on these newly created data sets and thus caused a storm of publicity. Companies large and small are now frantically trying to hire these elusive data scientists, or something a little more down to earth, are creating data scientists (luckily not in the lab) by forming teams that bring a part of the skillset to the table.
This approach all starts to smell pretty much like a whole busload of typewriters being thrown at a well-known data analysis and data wrangling problem. Neither the problem nor the solution are new, nor innovative. Data Scientists are therefore not moving us from 0 to 1...
One could argue that while the data scientist quest is not innovative, at least is solves the problem of doing analytics. Fair and by some measure correct, but there is one bigger issue with the paradigm of "data scientists will solve our analytics problem" and that is scale. Giving the keys to all that big data to only a few data scientists is not going to work because these smart and amazing people are now becoming, often unbeknownst to them, an organizational bottleneck to gaining knowledge from big data.
The only real solution, our 0 to 1, is to expose a large number of consumers to all that big data, while enabling these consumers to apply a lot of the cool data science to all that data. In other words, we need to provide tools which include data science smarts. Those tools will enable us to apply the 80% common data science rules to the 80% of common business problems. This approach drives real business value at scale. With large chunks of issues resolved, we can then focus our few star data scientists on the 20% of problems or innovations that drive competitive advantage and change markets.
The bubble is bursting because what I am seeing is more and more tools coming to market (soon) that will drive data science into the day-to-day job of all business people. Innovation is not the building of a better tool for data scientists or hiring more of them, instead the
real 0 to 1 innovation is tools that make make all of us data scientists
and lets us solve our own data science problems. The future of Data Science is smarter tools, not smarter humans.