Considered as one of the key professions of the future, that of the Data Scientist is still a figure to be defined. It is not just an analyst or a business strategist, as well as we cannot call him a marketer or an information manager. The Data Scientist is the perfect synthesis of all these elements, but it represents more than the sum of its parts. His work consists in the analytical resolution of complex problems and in providing the management with information useful to the company’s decision-making and strategy processes.
Over 90% of the data available to us today has been created over the past two years: data about our web search and purchase habits, our social sharing, geolocation, reviews, huge amounts of photos and videos that accumulate in the archives, are only a small part of the total. This incredible asset of raw information gains its true value after an appropriate treatment that allows them to analyze, but above all, to interpret them.
In fact, data analysis is not an emerging discipline but, better known as Statistics, has a history of more than two centuries. However, since the 1990s, the need to overcome traditional statistical analysis paradigms has emerged, in order to handle information natively available in digital form and address the acceleration of the digitization process that has spread in the last few years in all the industrial sectors.
All of us are called upon to contribute, in a more or less conscious and structured way, to the immense data, information and content archive of the web. This frantic activity, besides creating knowledge and progress, is to be held responsible for one of the major problems that marketers, business strategists and companies are facing today: to conquer and retain the attention.
In the digital economy, attention is seen as the real exchange currency, the raw material with which to build the future of companies. Although this is a purely instrumental vision, attention is the first filter, the first barrier between us and those who are trying to communicate with us. Already in 1971, Herbert Simon, Nobel Prize in Economics, wrote: “What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” In our present society, in which information is overwhelming, the study of data provides a concrete tool to address the problem and ‘guide’ the user.
That of the Data Scientist thus becomes one of the most promising and demanded professions of the future, where the real problem will be represented by the low supply as opposed to the ever-increasing demand. Data Scientist integrates in its profession a range of skills that enable companies not only to generate competitive advantage by leveraging the information contained in the data, but also to help create new business models. Among the various capabilities required by a Data Scientist, the following are:
“Data Scientists are similar to Renaissance men, because data science is intrinsically multidisciplinary,” says John Foreman, Vice President of Production Management at MailChimp. A figure that requires transversal skills ranging from statistics to computer science and economy, finally touching distant fields such as marketing and communication.
Also in Italy, the figure of the Data Scientist is increasingly requested, with a boom in job offers, both among SMEs and multinationals. Companies are looking for profiles formed in interdisciplinary contexts that are able to adequately qualify students to deal with the complexity of the tasks they are going to perform. Traditional graduate courses fail to meet these needs and to deepen, in addition to the statistical aspects, even technological-informatics and economic aspects.
Bologna Business School offers, among its full-time programs, the Master in Data Science, in English language, designed to provide highly specialized training to those who have gained a deep curiosity in data processing and want to perfect it.