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Andre Balleyguier, chief data scientist EMEA, DataRobot

Andre Balleyguier, chief data scientist EMEA, DataRobot

How is your organisation using data and analytics to support the corporate vision and purpose?

 

In my current role, I head up the applied data science practice at DataRobot in EMEA. DataRobot is the leader in enterprise AI, helping customers around the world to create more value with their data. The mission of my team is to provide expertise and advisory to DataRobot’s customers to ensure their successful adoption of AI. We have a relentless focus on delivering value for our customers by solving practical business problems with AI and we play a critical role in enabling DataRobot to engineer a more intelligent tomorrow thanks to our Enterprise AI platform and our world-class expertise.

 

Some companies are just getting started in AI whilst other more mature companies already deliver value with their data using AI. In both cases, we help them resolve operational challenges preventing them to scale by providing enablement and coaching on highly practical know-how.

 

2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?

 

As we entered into 2020, we were already quickly growing our operations across EMEA due to a rapidly increasing demand for AI. We were acquiring new customers into multiple strategic markets such as France, South Africa, and Germany and focusing our efforts on making our operations more efficient and scalable.

 

The pandemic had a strong impact on multiple sectors of the economy and created a lot of uncertainty, something I know all businesses experienced. We had to react quickly and creatively rethink the way we were engaging with our customers, focusing on their business priorities first and foremost. For example, we started using AI to tackle new business problems, like better capturing and forecasting the impact of the pandemic on their products and customers.

 

We also engaged with key governmental organisations like the NHS to face some of the toughest analytical challenges brought by COVID. The pandemic introduced a massive surge of urgency towards digital transformation and a lot of companies realised how critical it was to expand their use of analytics technologies, in particular AI, in this context.

 

We started to hire again aggressively towards the end of 2020, in particular in EMEA, and we finished the year with very promising growth.

 

 

Looking forward to 2021, what are your expectations for data and analytics within your organisation?

 

Following the impact of COVID-19, a lot of companies have accelerated their digital transformation. AI has already proven it can add significant value in practically any line of business and often the focus is not on experimenting with AI anymore, but on operationalising it at scale, using the same resources.

 

Consequently, our applied data science practice will have a stronger focus on enabling our customers on creating repeatable standards to operate machine learning models, regardless of the tools used. We call this “MLOps” and DataRobot is the leader in this category. We will also improve some of our processes to ensure we define and measure more clearly the value we bring to our customers, with the goal to become more scalable.

 

Is data for good part of your personal or business agenda for 2021? If so, what form will it take?

 

I have always believed that leading tech companies should conduct effective data for good initiatives to inspire the entire tech community to work on these applications.

 

At DataRobot, we launched our AI for good initiative three years ago as one of our senior data scientists, Chandler McCann, collaborated with the Global Water Challenge to predict which water points were likely to fail in multiple developing countries in Africa, eventually impacting millions of people.

 

In 2019, we formally launched an AI for good programme, recruiting organisations around the globe to participate, and in 2020, we opened up our platform for free for those who were focused on solving the toughest challenges caused by the pandemic. In 2021, we will continue to focus efforts on our AI for good activities. We’ll recruit our second AI for good cohort and will continue to provide free access to our platform to the not-for-profit organisations solving the world’s toughest challenges.

 

What has been your path to power?

 

I am an engineer and a mathematician by training, but I have always had a keen interest in practical business applications. I started my career in data as an analyst working within the financial markets divisions of a few banks in Paris. Although I was analysing data every day, I quickly realised most of our tasks were both repetitive and manual, eventually killing our creativity with processes.

 

I started to study smarter ways to analyse data and that is when I discovered AI and its applications in data analytics. After completing a research placement in Singapore, I came back to Europe, eager to become a “data scientist” - a term that barely existed at that time (seven years ago). I worked within the first big data team of Sky UK before DataRobot hired me as one of its first data scientists.

 

DataRobot had less than 100 employees when I joined five years ago. Since then, it has been an incredible journey of exponential growth, hard work, and learning from our successes (and mistakes!), all while focusing relentlessly on delivering practical value with AI. I built our applied data science practice across EMEA, which now has around 30 talented individuals, and continue to make strategic hires in this area as we continue to build our team. I am also part of the DataRobot EMEA leadership team.

 

What is the proudest achievement of your career to date?

 

The achievement I am most certainly the proudest of is building the applied data science practice of DataRobot in EMEA from the ground up in the past 4 years. When I first joined, I was the sole individual contributor working with our European clients, learning the hard way. After about a year, I was tasked with hiring and subsequently coaching data scientists across the region. We now count around 30 very talented experts in EMEA, spread across 12 locations. Helping grow and shape this organisation by encouraging a diverse, open, and highly collaborative culture has been the most gratifying experience of my career.

 

Tell us about a career goal or a purpose for your organisation that you are pursuing?

 

As companies start realising how hard it is in practice to achieve scalability with AI, one of my goals is that DataRobot gets recognised as the definitive enterprise AI enabler and leader.

If a business leads an enterprise-wide AI transformation initiative tomorrow, I want DataRobot to be considered as one of the very few organisations that can provide both a leading technological platform and the applied data science expertise to turn the opportunity into a reality.

 

How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?

 

The applied data science practice I lead in EMEA works at the intersection between the analytics teams and the business teams of our clients. We work closely with the business units to understand their processes and propose new ideas of potential applications of AI, whilst we enable analytics teams on practical know-how.

 

While building the team, I put a particular emphasis on ensuring a high variety of industry experience, so that people could learn from each other while working collaboratively. The team is now composed of subject matter experts covering a wide range of verticals (insurance, healthcare, retail…) to ensure we are always able to interact efficiently with clients and propose innovative ideas.

 

This industry expertise combined with a strong focus on practical results really helps bring the two worlds together. A very common issue for organisations is that their analytics teams don’t speak the same language as the business. I strongly believe analytics teams should have sufficient subject matter expertise, whilst business teams should be educated on the basics of AI and analytics (and their applications) to avoid this issue.

 

What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?

 

A data culture should start with top-down sponsorship and a strong willingness of the C-suite to support a transformation. This type of transformation often requires deep changes at multiple levels, in particular when it comes to using AI at a larger scale.

 

Education is critical. Business executives should follow training programs to help them understand the art of the possible and generate new potential ideas, then they should be involved in bringing some of these ideas into fruition. Moreover, automated, standardised, and scalable processes should be implemented to improve the time-to-value and reduce the costs associated with delivering data solutions to the business.

 

Finally, best practices should be defined and followed: I have seen some distributed approaches based on pairing the business with a centre of excellence work well in certain organisations.

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