Clearly Big Data and the pervasiveness of analytics and predictive analytics can be seen in virtually every corner of the commercial world today. There are numerous reasons for the escalation of data and data analytics, Big Data and the utility of all of these things in virtually every vertical around the globe. Those reasons have been discussed numerous times on our blog and elsewhere and are well evidenced. But I’d like to take three seemingly unconnected examples of the pervasiveness of Big Data, data analytics and predictive analytics to illustrate three trends which I think are very important and that will continue to increase in significance over time.

IIoT. The internet of industrial things as it relates to commercially viable business-to-business analytics. My example of this theme is General Electric. Yes, it appears this may not be your mom and dad’s GE anymore. General Electric has announced its exit from and divestiture of virtually all of the assets which comprise GE Capital.(1) In doing so, GE has made a pivot as one of the great leaders of the American (world-wide really) industrial economy away from financial markets towards its industrial roots. But that pivot is more specific than just away from all things GE Capital. Very observant followers of that company have noted that GE is really attempting to become an industrial analytics company. Nicholas Heyman of William Blair offered the following to CNBC, “GE may have found the next big thing . . . after the company’s latest earnings report beat expectations. ‘That’s GE software data analytics which could become as important early next decade as GE Capital was over the last couple of decades.’” (2) Heyman is quoted as saying that “predictive analytics for [GE] is now moving way beyond the one trillion installed base of industrial products. In fact, they are able to use their predictive software to predict what’s going on around the world with virtually all industrial equipment.” (3) So it is incredibly interesting to me that in a company that is as well-known, well-branded and financially skilled as General Electric, which is also rooted in driving industrial finance and finance around the globe, has made a strong, clear pivot to the industrial analytic space. GE seems to be placing an extremely large bet on the fact that the internet of things and connectivity related to industrial processes will become virtually completely data driven, and predictively so, in the next decade.

Virtually Everyone Needs Better Data to Drive Decisions. The next interesting phenomenon for me is that predictive analytics is being sought after by virtually everyone, like Monsanto which is a large provider of agricultural products for farmers. Clearly Monsanto makes money by helping the world bring food products to the table and feed animals (they provide seeds, products and things that make things grow faster, bigger etc). What is interesting is in recent years they acquired Climate Corporation which is a maker of a leading software platform that crunches weather related data in order to help farmers grow crops more effectively.(4) If you look at the venture backers of Climate Corporation, it was the who’s who of Silicon Valley.(5) And Monsanto—a company whose bread and butter is to help produce the world’s crops— moved even deeper into that vertical space by acquiring a company that has been a platform to allow super specific local weather monitoring, agronomic data modeling and high resolution weather simulations.(6) Essentially Monsanto is not only trying to provide the food producers of the world the products and resources needed to grow crops but also the ability to be extremely effective in their implementation of their products and seed resources. Monsanto has publicly stated that data science relative to its own industry alone could be a $20 billion market.(7)

Not-Just-For the Biggest. The last trend that I would note would be the trend of the democratization of data science. Clearly everyone knows of the story of “Moneyball” and Billy Beane and his use of sabermetrics in an effort to bring about a great baseball product for the Oakland A’s. However, very similar means have been utilized this year by the Sonoma Stompers. The Stompers are an independent professional baseball team based in wine country in California. This year the Stompers allowed Ben Linberg and Sam Miller of Baseball Prospectus Effectively Wild Podcast to serve as their baseball operations department.(8) Essentially those two individuals are utilizing data analytics and predictive analytics to find the best players they can for this independent minor league team. What is interesting about this is that the data that would be readily available for the major leagues would seemingly not be available for an independent minor league baseball team – and so far, the Stompers have been able to find enough data and utilizes enough information to be extremely productive. Through the first part of August 2015 the team was in first place in the Pacific Association standings with a 7-1/2 game lead over the second place team, the Pittsburg Diamonds.(9) So in another interesting way another theme has arisen that what once was thought to only be available to the highest end and largest operators in a vertical has not become something that can be utilized by middle market participants.

So what does all of this mean? First, I think it means that even companies such as GE who are tried and true and have a clear strength and focus have the vision to know that the data revolution is not only coming but it is upon us and to be competitive in the near term it is very important to have a strategy relative to how to deal with making better decisions in an ever increasingly competitive marketplace, now and over the long-term. Secondly, I think the Monsanto example shows that virtually no vertical can be immune from the reach and power of predictive and data analytics. Lastly, I think the Stompers example shows that even medium sized enterprises need help to have the power to grasp and make better decisions relative to the data that is available to them.

Obviously, with all of these themes, it is vital to not only collect, process and analyze the data but have actionable information as outputs. But I truly believe that this is just the beginning of what will ultimately be a much more data-driven view of all the aspects of the world we live in — whether it is how an industrial plant is run, how food is put on the table or even how your favorite baseball player is slotted onto a team.

1 Understanding GE Capital Exit Plan, Forbes;
2 GE has a better idea, analyst says, CNBC;
3 Id.
4 How water technology can help farmers survive California’s drought, Fourtune;
5Monsanto Buys Climate Corp For $930 Million
7 Id.
8Sonoma Stompers Give Baseball Writers A Chance To Run The Team, NPR;