Exploring Data Through Natural-Language & Conversational Analytics

Non-technical users have significant challenges in getting insights from data. Even though huge amounts of structured data are collected and made available to users in enterprises as well as to the general public, only skilled data analysts and data scientists can really make sense of the data. In this talk, I'll talk about recent developments in the space of "Conversational analytics", that promises to be a game-changer to drive data democratization.

Conversational analytics 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. A key technology is natural language to SQL translation, where we translate natural language queries from a user into SQL queries that can go against backend databases, and that can be easily trained on different schemas and databases. This NLP technology needs to be further combined with dialog management, natural-language generation/narration, data understanding and modeling, augmented analytics and automated visualization generation in order to achieve the goal of "Conversational Analytics". Using such a technology, a user can ask questions in plain English such as: "How many cases of Covid were there in the last 2 months in states that had no social distancing mandates by type of transmission", and then dig deeper into the results in a conversational manner to uncover hidden insights from Covid datasets.