Davide Cervellin, Head of analytics, insights and data - global experiences, Booking.com

Path to power

 

Davide was born in 1980 in Verona, Italy. He has a MSc in Electrical Engineering from Politecnico of Milan. His past experience includes companies like Siemens, Vodafone, Pirelli, eBay, PayPal; he has had roles in finance, operations, business development and marketing, and has lived and worked in Italy, Switzerland and England, before moving to Amsterdam, Netherlands. He also advises and coaches several start-ups (among them Metrilo and Avora), large companies (such as Disney and Qlik), and also universities (such as Politecnico of Milan and Ca’ Foscari of Venice). He has been a keynote speaker at over 20 conferences all over Europe. He is also author of the book, “Office of cards: A practical guide to success and happiness in large organisations (and life)” and the blog officeofcards.com, where he shares his insights on leadership and career management.

 

What has been the highlight of your career in the industry to date?

 

I would have to say publishing my first book, “Office of cards,” in the summer of 2018. It is not a book about data, but I do believe people working in data and analytics would benefit massively from reading it. It is a practical guide on how to convince people, understand behaviours, communicate stories to have an impact on how people think and what they do. These are skills I have developed over the course of my career and I have seen how simply “doing great analytics work” was not enough to have impact in the companies I worked for. There was more - the bit that allows a great analyst to fully understand the point of view of their stakeholders, then do great analytics work, and then come back with powerful, action-driving stories. This book will help a lot of analysts and data scientists feel more embedded in their organisations, drive deep and meaningful change, and be happier and more satisfied with their work.

 

If you could give your younger self some advice about how to progress in this industry, what would it be?

 

I would say, “do not focus only on technical skills”. The definition of success for a data expert today is understanding problems (of customers) and influencing behaviours (of stakeholders), not just solving complex mathematical problems or being able to code in 20 programming languages. There are four phases in the routine of an analyst: understand the problem of your customer; frame the problem in analytics terms and solve it; influence your stakeholders to take action on your findings; measure the impact of the work you did. In the first part of my career I was focusing on steps 2 and 4, but I have realised that 1 and 3 are what makes the difference between good and great analysts and data scientists, which is why I have decided to summarise my learnings in the book, hopefully to help other people grow and have bigger impact in their companies.

 

Did 2018 turn out the way you expected? If not, in what ways was it different?

 

For me, the main thing that was hard to predict was the high number of scandals connected to data leaks, ironically happening in the year in which GDPR became effective. I see what happened with the Cambridge Analytica scandal, as well as the longer term discussions on the ways in which data has been misused to influence political outcomes, as a wake-up call for us industry leaders. We have the responsibility and, now more than ever, the opportunity to define the rules of our game, rules that are fair, transparent, and that act as enablers and not barriers to delivering great customer experiences without violating the trust that customers have in giving us access to data about their preferences, their likes and dislikes and other types of information. Ethics and governance must become critical components of the role of any senior leader in the data space. The stakes are simply too high for us to keep looking the other way when we talk about compliance.

 

What do you expect 2019 to be like for the industry?

 

There are several trends that are already underway and I think they will accelerate in 2019. Machine learning will become ubiquitous: traditional retailers, farms, hospitals, mobility…virtually every industry is starting to see the benefits of machine learning and are starting to staff analysts and data scientists to help them make better decisions. As a result, ML and AI start-ups are growing and getting funded at incredible pace. So, I believe in 2019 we’ll see even more concentration of investments on companies who offer data products or services. This will lead to a fight for talent. Traditional education is taking too long to produce enough people skilled in the data space and the demand from jobs is growing a lot faster than the supply of people. This leads to two effects: 1) more job opportunities in senior roles for skilled people with higher salary opportunities; 2) a generic lowering of the bar at junior levels, where people will try to fill the skills gap quickly (typically with online courses) and have access to analytics roles without solid foundations. It will be hard for non-experts to understand what type of analyst or data scientist they need if they have no experience hiring that type of person. Ultimately, this will generate opportunities for recruiters to specialise in this type of talent, which is already happening, but also should stimulate thoughts within boards of directors of large companies which should realise the importance of having data-savvy people within their ranks. These skills are today as important as marketing, sales or accounting which is why, for instance, at booking.com we offer ML and AI training to members of the leadership team. I hope more companies will follow this example!

 

Talent and skills are always a challenge to find - how are you tackling this in your organisation?

 

The challenge is not just to find talent, it’s also to hire them as a very limited number of companies (Google, Facebook, etc) have the natural pull to attract talent. Personally, I look for people who WANT to wake up every day and come to work. Motivation is the number one trait I look for when I interview people because it’s the most useful skill to have when facing seemingly impossible problems or hard-to-deal-with stakeholders. To motivate them, I try to define roles in a way so they provide challenges, they look attractive to people, make them grow and learn. Important caveat: BE good, not just LOOK good. If motivation is there, then I check for commercial awareness (ie, to me an analyst should be theoretically capable of doing the job of the person they support and, for this to happen, they need to have a business mind, as well as an analytical one) and communication (ie, I always ask questions in the interviews I run saying, “can you explain it like you would to your grandmother?” to make sure they would be able to articulate technical concepts to non-technical people). Ironically, I never ask statistics questions or coding. At booking.com we have a specific interview for those skills but, unless the role I am looking for is very heavy on those skills, I would happily consider hiring someone that passes on my questions and fails at the technical ones because I believe people can learn these skills quickly, if they are motivated. Learning commercial awareness or communication is a lot harder, which is why it’s so important that young analysts and data scientists work on improving on these skills early on in their career.

 

What aspect of data, analytics or their use are you most optimistic about and why?

 

Machine learning is pervading our lives in ways we often can’t see - companies are adopting these technologies to offer personalised services like never before. Soon enough, companies like Amazon will be able to know what we want to buy (or watch) like our favourite restaurateur “knows” to save us our “usual” spot, to give us “the usual” meal and to delight us with a new wine bottle they “know” we will like. We are about to go full circle: the intimate connections we had with shopping experiences we had before the internet, when service providers (store owners) were using their knowledge of our preferences as retention techniques, the same connections that the internet initially threatened, forcing customers to choose between those and convenience (often choosing the latter and putting the entire retail industry under pressure for over a decade), are now coming back thanks to machine learning. This is what I am doing today at booking.com, helping the experiences team develop unprecedented levels of understanding of travellers that will help us surprise and delight them with relevant content and offers, at the same time removing as many of the frictions within travel as we can!