Vivek Gupta, CEO, Softsensor AI
Describe your career to date
I have been an entrepreneur, thought leader and teacher in the field of data analytics and AI. I have co-founded and scaled multiple startups such as Softsensor.ai, LMDmax Corp, PRr.ai, Essex Lake Group and Map My Learning. I also mentor Ridge Robotics, a high school STEM initiative and co-founded a research-focused 501c3 for coronavirus. My early career includes roles at Decision Craft Analytics and Inductis and later founding of the analytics practice for Opera Solutions in India.
I have worked across multiple sectors and built expertise, built and led high-performance teams, spearheading analytics-driven transformations and delivered innovative solutions. I have been a trusted partner in driving significant top-line and bottom-line improvements and has worked with prominent clients such as JPMC, American Express, TD, Wachovia, FHN, MetLife, Tata Sons, JSW Steel and numerous private equity firms.
I am recently also working on areas in medical AI where we are using computer vision and deep learning to build algorithms to solve complex diagnostic problems. I have started combining robust analytics with technology to deliver implemented and sustainable solutions for companies.
What key skills or attributes do you consider have contributed to your success in your current role?
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Having a questioning mind and digging deeper in a quest for new answers combined with problem-solving skills, has allowed me to approach complex challenges creatively and precisely and help my clients to achieve significantly better outcomes.
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As an entrepreneur, persistence and perseverance have helped me to identify new opportunities and overcome setbacks while founding and scaling startups.
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Fostering a culture of collaboration and continuous learning and inspiring, coaching and mentoring others has been critical for me in building effective organisations. The leadership and mentoring of high-performance teams, leaders to achieve challenging outcomes without creating a cut-throat culture is a fine and important balance.
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An interconnected, global world allows you to pick and choose talent from the global arena, which has been critical for me to build technology faster and at scale.
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Focusing on business or customer outcomes, having an ownership driven model as part of the delivery culture, encouraging people to be more cross functional to combine, business, analytics, science and technology has been a critical factor in successful growth.
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Building a truthful long lasting customer relationship, focusing on reputation and the integrity of companies and their commitments, delivering from a point of knowledge has been critical for winning continuous business.
What level of data maturity do you typically encounter across your client base and what tends to hold this back?
All organisations have some constraints in maximizing their data potential as the problems change with the level and type of data generated and collected. Banking and credit organisations have a higher maturity as compared to manufacturing and medium-sized businesses in the rest of the world. The key reasons for hold back include lack of easy access to technology, talent, knowledge and tools. With cloud computing, there has been greater harmonization and availability of capabilities for organizations of all sizes and sectors to leverage. This increased accessibility has enabled even those organizations that lack deep technical and analytical skills to adopt data-driven decision-making and governance practices. As a result, we are witnessing a gradual shift toward higher levels of data maturity across the board, although there is still much progress to be made in closing the gap between industries and regions. Company specific differences arise from their own management maturity, desire to be data driven, competitiveness and their IT systems maturity.
What trends are you seeing in terms of the data and analytics resources your clients are demanding from you?
Here are few trends which I see:
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Cloud knowledge and ability to use cloud tools for analytics is a must and needed for all the resources.
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Visualization of outcomes and being able to add value to the business beyond analytics is critical for success at a customer.
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Python has become the mainstay programming language over SAS or earlier systems.
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Deploying analytics inside apps, embedded systems is another area for growth.
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