Today is Episode 3 of Interview series on Expert-talks @MAAVRUS, with Leaders in the Analytics, AI and Transformation space. For this episode, I am in conversation with Saswata Kar, Senior Director and Global head of Data, Analytics and Data Sciences at Philips Global Business Services. Prior to Philips, Saswata has had an illustrious career at Capital One, HSBC and GE Capital. He is also a Forum Member of Nasscom CoE for IoT and AI.
It was wonderful to speak with Saswata and understand how he uses his background in economics, statistics and corporate finance, to provide impact creating insights & analytics solutions. It was great to learn from his business empathetic practical approach. Am listing below, some key learning from the interview.
- He feels that his majoring in economics, statistics and corporate finance streams, has helped him to combine industry perspectives with analytics techniques, to deliver high impact projects. He has been able to apply practically over 80% of his academic learning.
- Typically in Philips consumer facing business like personal health, they combine insights from marketplaces like Amazon, data from their own e-commerce business, and google mobility trends, to understand customer segments, profiles & migration. This enables them to align supply chain and availability metrics, for enhanced growth opportunities.
- Before embarking on a project, it is necessary to understand expected outcome from Stakeholders. If the expected impact is limited and not strategic, the tolerance levels for accuracy is high and there are time constraints, one should decide to adopt a rapid analytics approach. The focus should be on using tools and techniques which can provide insights faster.
- In the health tech space, machines are largely IoT enabled and so there is a lot of data exhaust available. The key point in how to use it, should be determined. by the problem to be solved, and the impact of preventive maintenance.
- When it comes to collecting data, a good way to look at it` is to see the recency value of data, and the actionability within that time frame. Anything beyond that can be stored as summarized data. Another factor to consider when collecting data, is its at its relevance to already known problems.
- Business situations needing high levels of accuracy in insights or those governed by regulatory requirement, will need a higher level of explainability. In such cases, in addition to domain context, knowing the first principles of algorithms / tools being used, will be absolutely necessary. However when the level of tolerance is reasonable, then good contextual knowledge , and ability to leverage open AI tools may be enough.
- Staying curious and ability to have a contrarian viewpoint and challenging status quo, are skills that the next generation of data analytics professionals will need to mandatory have, given the rapidly evolving business and technology landscape.
I am sure you will find the conversation with Saswata very interesting. You can also watch / listen to the interview on our website, youtube, apple and spotify podcasts on the links below. Please do share your comments and subscribe / follow us on @maavrus.com on LinkedIn, facebook and twitter.
