SBIR Phase II: Democratizing Data Science Through Conversation

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to increase the number of users within an organization that can carry out sophisticated data analysis. The proposed approach, if proven successful, can also open a new vertical in the analytics market in which text-based chatbots aid humans in carrying out the task of creating, deploying and running complex data science pipelines. Such a positive outcome could lead to the creation of a sub-market in the existing analytic software market, and it could also help improve the productivity of the (non-technology) sectors of the economy that increasingly require high-quality and fast insights from both archival and real-time datasets. This Small Business Innovation Research (SBIR) Phase II project targets the issue that it currently can take substantial human effort and time to extract meaningful insights from data. The company aims to change this cumbersome process by training text-based chatbots to perform complex analysis tasks on enterprise data. These chatbots then allow users to acquire answers about their data by chatting in (a controlled subset of) written English. Instead of dedicating hours or even days to answer a single question, large datasets could then be queried multiple times in minutes, enabling businesses to make informed decisions in real-time. Thus, this technology aims to dramatically improve human productivity in gathering insights from data and democratize data analytics by making it available to a broad class of users within an enterprise. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.