Predictive Analytics & Machine Learning – Key Drivers for Competitive Advantage
It is said that we live in the so-called “data-driven economy”, which is an economy where data is the fundamental resource for generating a competitive advantage. To understand how companies can create value in this economic scenario, it is important to understand better the two fundamental factors: big data and machine learning; dispelling some of the clichés that limit their understanding by companies and, therefore, the possibility of extracting value from them.
In the first half of this decade, the term “big data” became popular in the management field. This was first coined in 1997 by two NASA researchers, Michael Cox and Davis Ellsworth. The two researchers, overwhelmed by the difficulty of managing an ever-increasing amount of data generated by their studies, wrote: “A set of generally very large data, such as to put a strain on the capacity of the memory, the hard disk, and even the local disc of a computer. We call this the big data problem.”
In the management field, in 2011, the McKinsey Global Institute defined big data as “datasets whose volume is so large that it exceeds the capabilities of software to capture, store, manage, and analyze”.
In light of these definitions, we are, therefore, often led to identifying the fundamental characteristic of big data in their quantity which is accompanied by a purely technological problem, namely the difficulty of our computers in managing this amount of data. However, this technological problem is certainly not new. Indeed, we could say that it is as old as the invention of the computer. For this reason, reducing big data to a mere technological and dimension problem is at least misleading.
So, what makes big data so special? To understand this, let’s take a closer look at the data that millions of people produce every day on Spotify.
Many of us rightly believe that Spotify has a very thorough understanding of our musical taste. Actually, Spotify knows a lot more: It knows, for example, when we run, when we drive, when we are showering, when we go through sad times or happy times. How do you know? Spotify’s servers are full of “Run, Michael, Run!” playlists, “Driving Songs”, or “Songs to Sing in the Shower”. Most people create playlists with the name of the activity they are engaged in while listening to music. The users themselves say: “I’m running”, “I’m driving”, “I’m taking a shower”.
This is the painstakingly detailed level of knowledge about our daily activities that Spotify has access to (and on which it has built much of its business model). Spotify represents the norm of the data-driven economy. We are surrounded by objects that record whatever activity we do and turn it into data, stored somewhere and ready to be used. The real advantage of big data, therefore, lies not so much in the quantity, but in the ability to provide extremely detailed information about an individual. It is precisely this ability to provide us with detailed information on the lives of billions of people that represents the real advantage of big data. The fundamental change that made it possible to transform simple bytes into big data was the advent and proliferation of social networks, a sociological phenomenon that has forever changed our sense of privacy. It is no coincidence that, almost simultaneously with the growth of social networks, a new term has been coined to describe our society: “sharing economy”. Today, we are willing to share with perfect strangers objects that until recently were absolutely private, almost intimate, such as cars, houses, motorcycles, bicycles. Likewise, we have no qualms about sharing private information about us. The need to share our lives with strangers has meant that hardly anyone finds it strange that every step, every note heard, every thought expressed, leaves the walls of their home to end up in supercomputers all over the world.
