Tomorrow Jobs, the work to come...
New skills and new jobs for companies hurtling towards an ever more digital future: smart startups dealing with the big web data represent the evolution of the business and professional world
Italy, November 23, 2017
by Giampaolo Colletti
Jobs evolve, change paradigms and models, demand new skills and relational dynamics. They improve through the technologies used for research, production, distribution, sales, and customer contact. Jobs change. And with them, the job description also changes.
According to researchers Carl Benedikt Frey and Micheal Osborne of the University of Oxford, over the next thirty years, machines could replace man in half the current occupations. And there are those who predict that tomorrow, 65% of today’s students will do jobs that don’t yet exist: this is the essence of the extensive international study on new career paths promoted by Tomorrow Jobs.
A future still to be mapped out, drawing on a much more varied palette of colors than the past: from augmented reality expert to social media strategy, from data scientist to reputation manager. Retail sales are also subject to studies by the many brains dealing with big data. It’s happening now in Turin, home to the generation of data scientists, hybrid figures who today are part of an Anglo-Italian startup of predictive analysis: by cross-referencing data and contexts, the team of analysts can predict future sales in one or more sales outlets. And all this results in savings in storage costs, but also in the ability to sell in a much more targeted manner.
This is what Evo Pricing does. This startup was launched four years ago by Fabrizio Fantini, 38, born in Jesi, with a Master from Harvard and ten years as a consultant in Italy, the United States, and England. “With predictive analysis we help companies become more competitive and we already have customers around the world, from Mexico to California,” says Fabrizio proudly.
The Evo Pricing method has more than three hundred contributors every week in Italy alone, including sales clerks and store managers, thanks to a structured system: variables such as past sales, geographic area, climate, and product characteristics are used for the initial data prediction.