Dr. Anand Ranganathan
Chief AI Officer at Unscrambl Inc.
Anand Ranganathan is a co-founder and the Chief AI Officer at Unscrambl,Inc. He is a data scientist, AI engineer, Big Data developer, architect and researcher rolled into one person. He is leading Unscrambl’s product development in several cutting-edge areas, including natural language processing, conversational analytics, real-time optimization and decision-making, and automated marketing optimization. He has worked with over a 100 customers worldwide to design, implement and deploy Big Data, Stream Processing and AI-based solutions. Before joining Unscrambl, he was a Global Technical Ambassador and a Master Inventor at IBM. He received his PhD in Computer Science from University of Illinois Urbana-Champaign, and his BTech from Indian Institute of Technology Madras.He also has over 70 academic journal and conference publications and 30 patent filings in his name.
WATCH LIVE: November 2rd at 9:30 am
It often takes a long time for business users in enterprises to get the right data and insights, even if the data is well organized and structured. This is because they may not know where the data is located, how it is modeled, and how to frame the right queries for their needs. As a result, traditionally, access to data and analytics has mostly been limited to power users and specialist data scientists with varying degrees of analytical and technical skills. This is where conversational analytics comes in. This paradigm allows any user to ask text and voice questions, in natural language, of their data to a bot and receive back a natural language and visual result.
In this talk, I shall present our efforts and experiences in building a conversational analytics platform on a chat-based interface. Our approach combines natural-language processing (NLP), dialog management, natural-language generation/narration, data understanding and modeling, augmented analytics and automated visualization generation. I shall describe how we handle complex natural language questions against rich datasets like “How many cases of Covid were there in the last 2 months in states that had no social distancing mandates”, and further, enable users to dig deeper into the results in a conversational manner to uncover hidden insights.