There is no organization that shouldn”™t be thinking about leveraging these approaches, because either you do — in which case you”™ll probably surpass the competition — or somebody else will. And by the time the competition has learned to leverage data really effectively, it”™s probably going to be too late for you to try to catch up. Your competitors will be on the exponential path, and you”™ll still be on that linear path.
Let me give you an example. Google announced last month that it had just completed mapping the exact location of every business, every household, and every street number in the entirety of France. You”™d think it would have needed to send a team of 100 people out to each suburb and district to go around with a GPS and that the whole thing would take maybe a year, right? In fact, it took Google one hour.
Now, how did the company do that? Rather than programming a computer yourself to do something, with machine learning you give it some examples and it kind of figures out the rest. So Google took its street-view database — hundreds of millions of images — and had somebody manually go through a few hundred and circle the street numbers in them. Then Google fed that to a machine-learning algorithm and said, “You figure out what”™s unique about those circled things, find them in the other 100 million images, and then read the numbers that you find.” That”™s what took one hour. So when you switch from a traditional to a machine-learning way of doing things, you increase productivity and scalability by so many orders of magnitude that the nature of the challenges your organization faces totally changes.
The whole article is well worth a read but that part on Google was downright frightening.