We talk to Jules, Product Analytics Manager at GoPro, ahead of the Predictive Analytics Innovation Summit, taking place in San Diego between February 18th and 19th 2016, about his career in data science.
How did you get started in data science?
My journey into the world of data science began in 2012 when I started working on my Masters Degree in Predictive Analytics at Northwestern University. Since then it has really been the combination of new job roles, work projects, the Masters program, MOOCs, and Meet-ups that have enabled me to develop the skills and explore the complex world of data science.
There is so much to learn and so many applications that no single program, course or job can fully prepare you for work in advanced analytics or data science. A Masters or PhD degree in data science for example is just the warm-up act to a long journey of learning and discovery.
What are the unique challenges of working at GoPro?
A few of the challenges I’m facing include keeping up with the exponential growth of our data and implementing the tools that can handle performing analytics at scale. For example, an Impala query that works today and runs in 5 minutes may take 5 hours the following week because of the massive data growth.
Therefore, it becomes imperative to think exponentially in terms of data growth, data diversity and the tools that are capable of keeping up with that exponential growth and diversity of data. I’ve tested and broken more tools at GoPro than all other companies I’ve worked for combined. And that’s a good thing.
How much has predictive analytics changed since you’ve been working in the field?
Predictive analytics has gone from an interesting set of tools and techniques used by insurance companies and data scientists that work in obscurity in basements, to being mainstream and an imperative for all companies across all industries that want to compete on analytics or compete period. In the next 10 years, companies that are not in the process of operationalizing advanced analytics today, including predictive analytics, will be out of business or on their way out.
The change that I’ve observed is not in the predictive analytics models and techniques themselves, but in the sense of urgency to apply them to solve business problems. to answer business questions, and to enable strategic and tactical data informed decision-making.
What do you see as the next game changer in data science and predictive analytics?
My fellow colleagues and I are observing two things: A dramatic shift in the adoption and implementation of massively scalable and flexible data architecture technologies, and development and adoption of advanced AI based technologies, for example IBM’s Watson and self driving car tech. So, I would say AI and deep learning being applied to business applications is a game changer. They will also unlock hidden value and enable currently hidden business models no one has thought of today, like Uber did for example.
What will your presentation at Predictive Analytics San Diego be about?
My presentation will cover the type and characteristics of data architecture and tools that are necessary to enable analytics on massive data sets. In addition, I will cover how the analytics provided by the data architecture and tools can drive and enable data-informed decision making across an entire organization, with a focus on how it can enable informed product design and engineering decisions.