The most popular trades change with digital. But the professionals are still few

The transformation digital represents an epochal event and can contribute to improving the products and services of a company, creating a competitive advantage over competitors. Not having many skills in traditional trades, companies will have to acquire these resources from job market, but digital professionals are not readily available because the demand is high compared to a still limited offer.

To understand these new professions we need to start with some definitions. For example: what are i Big Data? According to the definition of the Big Data Observatory of the Politecnico di Milano, they are “large volumes of heterogeneous data by source and format, which can be analyzed in real time, definable with 5 characteristics: Volume, Speed ​​and Variety (the so-called Big Data 3V model) , Truthfulness and Variability ".

The Big Data market in Italy it has a growth rate of 26% and 56% of companies already have a number of professional figures such as the Data Analyst, 46% of Data Scientist and 42% of Data Engineers. Take one of them, for example the data scientist, as a representative sample of these professions.

The data scientist can be defined as the professional figure that manages the Big Data (the raw data) and draws information from it relevant for different business needs: business, marketing and sales strategies, definition of new products and services, etc. The profile of the data scientist is rather technical since it will have to have knowledge of mathematical-statistical models and algorithms (especially of machine learning) and the programming languages ​​needed to implement them, such as R or Python. In addition he will have to possess skills of business intelligence, semantics, ontologies for information management, methods and technologies for project management data-driven innovative.

These professionals usually have an advanced knowledge of the techniques of data mining as the clustering, regression analysis, decision trees and supporting vector machines. An advanced degree (like a master's or a research doctorate) in computer technology it is usually required for this type of position. We also need transversal skills depending on the sector in which we work: skill necessary in marketing are different from those for public administration or the telecommunications industry. This was highlighted by the research firm Gartner already in a 2016 study, where it defined the preparation of the data scientist as "multidisciplinary" as it is placed between different macro-areas (from statistics to traditional research, from data engineering to marketing).

The sectors that may need data scientists include: finance (where they also deal with fraud detection, security and compliance), e-commerce (help companies improve customer service, recognize trends and develop customized services or products), public administration (to offer a service in line with the needs of citizens), social media (to improve the services offered and to define advertising campaigns) and again health, scientific research, telecommunications.

Also there personnel selection is involved in this transformation, not only because it uses the big data itself to make its business services more efficient and targeted, but also because it is faced with new evaluation paradigms, which "target" less obvious professional goals than status socio-company or to the safety of the workplace. The reasons that support their choices are linked to the innovation of the application sectors, to stimulating projects that feed the need for knowledge and the experimentation towards unexplored areas.

These skills relate largely to young people, who are not insensitive to money because they are aware of their value and therefore their trend pay it is growing rapidly in a very short working time. Dimensions such as corporate affiliation or identification with a brand are not necessarily grounds for appeal. Areas of interest change quickly compared to the concept of obsolescence, perhaps also because of the speed with which innovations take place.

Convergent thinking leaves room for divergent thinking and more companies competitive they are those that know how to capture the best talents with ideas and projects of business diversification that are always stimulating and innovative, structuring one policy of retention to safeguard the investments made in terms of human capital.

The new professions also impose new forms of work organization that enhance individual production, but at the same time reward the ability to know how to integrate into different team work structured with a matrix logic. THE scientist they become naturally leader of project on competence rather than on seniority and the workplace becomes less and less important, with an increasing involvement of resources involved in projects smart working.

Therefore also the evaluation parameters of the performance they will undergo constant adaptations with respect to the classic criteria, as will also be the case for reward and training systems. Finally, solutions could be very effective welfare as well as forms of non-monetary benefits, such as, for example, participation in prestigious international congresses / conventions, solutions already widely used for some time in excellent applied research centers.

* Graduated in Occupational Psychology, she holds a Masters in Business Administration. He has gained many years of experience in the Personnel Departments of complex Business Organizations. For several years he has been a consultant for finance and insurance companies. She is specialized on issues of Organizational Conflict, Work-Related Stress, Mobbing and Straining.

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