Engineering and the complex world of manufacturing have always fascinated me, so in my career of 15 years, I’ve enjoyed the opportunities to develop advanced analytics products and solutions for leading automobile, manufacturing and BFSI companies. I feel that though we have great analytics products being spoken about in the field of fintech, life sciences and healthcare, not much is heard about such solutions in manufacturing or engineering.
But this is changing slowly. Of late, the combination of IoT and automobiles for autonomous driving has been creating buzz and driving growth in the market. But there are a lot of areas to tap into beyond “Flying Cars” – maybe less attractive but complex and challenging nevertheless. Some examples are the automation of defect detection in spare parts and calibration of emission channels, 3D design of new spare parts, and the evolution of safety systems to automatically learn scenarios to save lives. These interesting opportunities drive me in my work at Mercedes Benz Research & Development, India. I also make it a point to promote AI opportunities in manufacturing consulting in my speaking engagements at prestigious forums.
Over the years, I’ve realized that none of the journeys in analytics are smooth and its evolution and adaptation have been quite slow. Academically, it hasn’t been a big focus area in India, and we still lack a lot of talent. Since projects are often ramped down due to the pressures of cost and effort, the result is great projects involving ML models in practical use cases do not get into production, something I call “Lab to Factory Failure.” Having said that, there has been a slow shift in the perception and analytics is now being seen as a gamechanger. Slow and steady wins the race and, probably, that is what is happening to analytics as a field now.
Another development that excites me is the new dimension of “laymen” end-users of analytics becoming “Citizen Data Scientists.” In fact, they comprise 80% of the user base and find real value in analytical insights. I hope data scientists constantly try to make analytics solutions easily consumable for the larger part of the organization and society.
My advice for other data scientists – as Thomas Edison said, “There’s always a better way to do it better – find it.” Be open to thoughts, challenges and, lastly, great cuisines!
Meet Chiranjiv Roy: Data Science Enthusiast, Foodie and Passionate Practitioner of Analytics in Engineering.