How is your organisation using data and analytics to support the corporate vision and purpose?
I lead the AI and data science practice for BUPA through their technology and innovation hub, the Customer Lab. The Lab has a simple philosophy, help customers live longer, healthier and happier lives. We create and develop a range of AI solutions with a customer focus, for instance, designing algorithms that can analyse scans to diagnose diseases, predicting customer demand, resource optimisation engines, chatbots, attrition, and fraud, opportunity identification etc.
2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?
I think the effects of 2020 are still not fully understood or even manifested in some cases. Just like any organisation we have had to adapt our ways of working. However, we in the AI community are privileged in that we have been able to work remotely with relatively less disruption compared to other fields. We have used this period to plan our future more carefully, with digital and AI as absolutely critical elements of our transformation.
Looking forward to 2021, what are your expectations for data and analytics within your organisation?
2021 is the year where we really want to deliver value to the business in a tangible way. We have done some foundational work in the past year on creating tools for securing, managing and analysing our amazing data assets. This year we have plans to test them out in practice and create feedback loops so we can continuously learn and improve. I believe that the use of machine or deep learning algorithms on rich datasets will fundamentally transform the insurance industry, so it’s an exciting time in terms of the opportunity space.
Is data for good part of your personal or business agenda for 2021? If so, what form will it take?
We have always been strong on data governance and ethics in the insurance industry. In 2021, we will build on our data foundations to deliver value to our customers. The "Catcher" program is one.
What has been your path to power?
When I was growing up in India, we lived in several cities across the country, as my dad moved for work. Adapting to new cities, schools, friends, languages and cultures was a necessary survival skill. These experiences have also shaped my career, full of diverse challenges and constant change.
I have enjoyed setting up and scaling technology teams in the US, India, Germany, UK, Spain, Hungary, Turkey, and Singapore. Along with Silicon Valley start-ups, I have also worked for larger companies like Vodafone, HP, Albertsons and Microsoft. My Master’s in Applied Mathematics was from UC Berkeley and I balanced out my inner geek with a management of technology from the Haas School of Business, UC Berkeley.
What is the proudest achievement of your career to date?
I started my career in a Silicon Valley start-up, where you learned to roll up your sleeves, build products and bootstrap. I didn’t make any money with my start-up but that work ethic has paid me dividends. Looking back at my career, my proudest moments have always come when creating solutions, these could be simple analytic tools, marketing decision engines or complex enterprise applications.
The joy of creating a solution where none existed before and (hopefully) making someone’s life easier is super rewarding. We recently created a deep learning-based system that can detect human falls via CCTV cameras, called "Catcher".
Imagine a technology that can "see" if a patient in a hospital or care home has suffered a fall and gets them help as soon as possible. That’s an important problem to tackle and an example of how AI can be used in the real world. While the Catcher project did not have a commercial angle, just working on it was a great experience. That’s why I love this field, we get to be creative problem solvers in every single project, that makes work feel very much like play.
Tell us about a career goal or a purpose for your organisation that you are pursuing?
One of our "lighthouse" projects this year is a decision engine that I created last year for identifying opportunities and risks for BUPA, nicknamed project "ai.nstein".
Our industry is shifting from the current detect and repair model to a predict and prevent model. With project ai.nstein we can get advance warning of events like attrition, optimise our current spend, and drive offers that are relevant and useful to the customer. I believe that the value of AI in the industry is still untapped, with project ai.nstein I hope to make it more tangible.
What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?
I have always found that creating innovations is the best driver of a data culture (even if it’s just an Excel spreadsheet that automates or analyses something). If you can make your customers’ life easier they will join you in your journey, despite the implicit disruption. Without innovation, you might always be building on the status quo, or a "faster horse".
At the Customer Lab, we also do a few kinds of engagements with our teams to drive data culture. We have big picture webinars on industry trends, AI and how it affects the business. We also have hands-on workshops, where users get their hands dirty and look at what the best in the world are doing (deep learning or machine learning applications).
Finally, we also have executive friendly sessions, where the lens is more commercial and future focused so we can build support for our efforts, get feedback from the business leaders and incubate future tech. So, it’s art and science and commerce welded together in various messages.