I gained a degree in economics from Bocconi University, Milan. I held various positions in direct marketing across the Fiat Group and AME Spa until 2001, before I was appointed head of the media store online BOL.IT by j.v. Mondadori-Bertelsmann. In 2010, I joined the Bravofly Rumbo Group as business strategy director before being promoted to chief operating officer. Since 2015, when the company bought the iconic brand lastminute.com, I’ve been chief data officer in the lastminute.com group.
I left a big corporation to join a company that was little more than a start-up. I was lucky enough to play a key role in the process to get it a publicly-listed company on the Zurich stock exchange.
To the youngest I would say: don’t start thinking about the career in itself. Embrace your passion for data crunching, the appetite for writing your own queries. You must be fascinated by the truth that is hidden behind any of Trilussa’s chicken-style KPI’s *. If you’re in line with this, your shiny seat in this industry will be a welcome side-effect! To the more senior I would say: make a step on the other side, where data is consumed and is the fuel of business decision-making. Focus yourself on owning the need, more than in finding the solution. (*The Italian poet Trilussa said that statistics is the science according to which if you eat two chickens a day and I eat none, on average you and I each eat a chicken a day.)
GDPR was supposed to be the tyrant of the year and so it was. But, I have to say that the benefits hidden behind the constraints have turned out to be more extensive than expected: the spike of attention toward data protection paved the way for a wider responsiveness around any data-related topic, including data culture and data-driven product development. In a nutshell, GDPR helped me doing my job more than having imposed priorities which were not mine.
I trust, or at least I hope, that the industry will make a bit clearer the eco-system of jobs involved in the modern value chain of data management on the engineering side. We never got off asking ourselves what a data scientist is and now the same kind of doubts seem to be extended to machine learning experts and machine learning engineers, split between ML software engineer and applied ML engineer, with everyone sitting in an ideal line among data engineers and data scientists, but you never know in what pattern. I appreciate that stardardisation can’t be the main focus of a cutting-edge technology, but I guess that such level of confusion acts as a limit to scalability and deeper transformations.
What I see is that, at least in data science, more than by good companies, talents and skills are attracted by talents and skills. By which I mean a strong kernel of hard skills, around a hard core of senior managers, with strong focus on continuous learning/training, sounds like the easiest way to expand the team attracting good talents.
The new youth of the industry could be enough to justify the wider optimism. But even with a less romanticised and maybe more venal perspective, I guess that a clearer and better pricing strategy by the main vendors of computational power will make easier a wider (and safer) application of the well beloved "fail fast" approach.