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Ava: From Data to Insights Through Conversations

Enterprises increasingly employ a wide array of tools and processes to make data-driven decisions. However, there are large inefficiencies in the enterprise-wide workflow that stem from the fact that business workflows are expressed in natural language but the actual computational workflow has to be manually translated into computational programs. In this paper, we present an initial approach to bridge this gap by targeting the data science component of enterprise workflows. In many cases, this component is the slowest part of the overall enterprise process, and focusing on it allows us to take an initial step in solving the larger enterprise-wide productivity problem. In this initial approach, we propose using a chatbot to allow a data scientist to assemble data analytics pipelines. A crucial insight is that while precise interpretation of general natural language continues to be challenging, controlled natural language methods are starting to become practical as natural interfaces in complex decision-making domains. In addition, we recognize that data science workflow components are often templatized. Putting these two insights together, we develop a practical system, called Ava, that uses (controlled) natural language to program data science workflows. We have an initial proof-of-concept that demonstrates the potential of our approach.