We hear about it everywhere, Big Data, data science…Literally, Big Data refers to a very large set of data that no human or conventional database management tool can work alone. IDC estimates that by 2020, every person on Earth will generate 1.7 megabytes of data per second. To give you an idea, it’s the weight of the MP3 file of a two-minute song.
According to IBM, 90% of global data has been created in the last two years. In other words, the volume of global data is and will be more and more colossal, and it becomes an economic, political and social issue. It’s no surprise then that Big Data and data science make their appearance in a management school, is not it? But is it a fad or a real challenge that schools have decided to take up?
Data science and Big Data
Data science is the science of data: a mixture of algorithm development, technology and analytical capabilities, whose objective is to solve complex analytical problems using data – Big Data. Basically, that’s what makes it possible to use the data to create value for businesses. By diving into Big Data, the data scientist can understand complex trends and behaviors that can help businesses make smarter decisions. Perhaps one of the most telling examples is Netflix: the company studies the views of its users to understand what’s driving their interest and uses that information to choose which series to produce.
In school, what does it give?
Data science is based on mathematics, computer science / programming and notions of machine learning. All this does not sound very “management school” but rather engineering school, which is not totally wrong. Nevertheless, training / courses in data science have exploded in business schools in recent years, especially through double degrees with engineering schools.
But suddenly, what’s the point? It is true that the data scientist strictly speaking must have mathematical, computer and technological expertise. That’s why they are usually engineers. But this is not enough: he must also know how to navigate between different languages, as well as know how to “translate” what he discovers. That’s why he must understand all the business and business issues they are working for, so he can get exactly the information he needs to create value and / or solve their problems. This relevance for business is just as important as the mastery of mathematics and algorithms: the goals of the data scientist must be aligned with those of the company.
Fashion or future?
Above all, companies also need managers who understand the challenges of data science, even if they do not have the technical mastery of tools to solve the problems of the company. Especially since the data science does not seem to be a fashion, but a heavy trend which should be accentuated in the years to come. This is a real business challenge for companies, hence the importance of recruiting managers and executives who understand the ins and outs.
For example, according to Forrester analysts, companies that use Big Data will earn $1.2 trillion more in 2020 than companies that do not use it. The Economist conducted a survey revealing that more than 75 percent of business executives surveyed could integrate Big Data (and Artificial Intelligence) into their business by 2020. According to IDC analysts, Big Data and the analytical tools generated 130 billion dollars in 2016 and this number could rise to 203 billion in 2020.
Of course, it is difficult to predict the development and consequences of Big Data and data science. There will undoubtedly be phenomena that are still unpredictable today. Nevertheless, Big Data does not seem to be a fad; it is rather a turning point that companies must take now if they wish to continue to develop, as was the Internet. Otherwise, the competition risks going beyond them fairly quickly. Hence the importance for managers and future managers to take an interest in the issues of Big Data and data science, even without necessarily developing technical expertise. Data probably represents the future of the economy, it is up to us to adapt to it.