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Andrea Senso, data science and analytics manager, Rolls-Royce Motor Cars

Andrea Senso

Path to power

I first joined Rolls-Royce Motor Cars in a commercial role in 2014, having previously worked in the luxury automotive sector, garnering experience in sales and market intelligence positions, first in Shanghai and then Italy.

 

In late 2016, I was given the opportunity to explore data science at Rolls-Royce to determine if it could benefit the rarefied world of super luxury. With small volumes, bespoke commissions and the pinnacle of luxury brand experience to protect, the challenges were both significant and unique.

 

While defining the proof of concept, I realised that my prior strategic and operational experience gave me a unique insight into how data and analytics could benefit stakeholders and, ultimately, most importantly Rolls-Royce patrons. The results from these initial experiences were so positive that in 2018 I established and led the first ever dedicated Rolls-Royce data science team.

 

What is the proudest achievement of your career to date?

I was presented with the unique opportunity to establish a new function and team from inception, which is undoubtedly a personal highlight. This is even more rewarding considering it is for Rolls-Royce Motor Cars, a truly global and celebrated brand.

 

Who is your role model or the person you look to for inspiration?

Sir Henry Royce, the co-founder of Rolls-Royce Motor Cars, was a pioneering engineer, who created the world’s best car through a combination of innovation and precision. I strive to apply the same values when creating the tools which will support my stakeholders in delivering the world’s best customer experience.

 

Did 2019 turn out the way you expected? If not, in what ways was it different?

2019 was the second year of operations for the Rolls-Royce Motor Cars data science team. As such, we have witnessed a steady increase in the benefit of data science to the business and its customers. We have exceeded expectations with several highly successful initiatives.

 

One highlight was that we successfully designed, developed and delivered a fully integrated cloud-based data infrastructure in partnership with our solution architects Jaywing. This is a key ingredient to an enterprise level data science and analytics platform, which will allow us to deploy our models and deliver insights in real-time.

 

What do you expect 2020 to be like for the data and analytics industry?

Over the last few years the barriers to entry for “datafication” have steadily reduced, meaning the ability to incorporate data and analytics into everyday operations is no longer exclusive to tech companies and giant multinational businesses.

 

As it becomes easier and cheaper for traditional and niche industries to leverage data, their ability to do so will provide an essential competitive advantage. I anticipate more companies will take advantage of this trend.

 

Data and technology are changing business, the economy and society – what do you see as the biggest opportunity emerging from this?

I work at the pinnacle of the luxury industry, and as such, our customer service must demonstrate these values.

 

Typically, technology has been the antithesis of meaningful human interaction. Using data science, however, we are demonstrating that data and technology can augment our ability to provide a highly personal, unparalleled level of customer service for our global patrons.

 

Exciting developments to predictive CRM are key to this strategy, reducing the time customer-facing staff spend on administration and increasing the time they dedicate to the customer experience.

 

What is the biggest tech challenge you face in ensuring data is at the heart of your digital transformation strategy?

Exclusivity and rarity are central to Rolls-Royce Motor Cars, however, with small numbers of cars and customers, come small quantities of data. Therefore, our data scientists are challenged every day with making machine learning work with a comparatively small amount of data.

 

This challenge has pushed us to innovate and use cutting-edge programming languages, such as STAN, to ensure every piece of data counts. With these strategies we can be truly predictive, despite our boutique quantities of data.

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