Boomerang: Proactive Insight-Based Recommendations for Guiding Conversational Data Analysis

Abstract

Natural-language interfaces are gaining popularity due to their potential to democratize access to data and insights by making the interaction with data more natural and accessible for a wide range of business users. To fully embrace the goal of democratization, it is also necessary to provide effective and continuous guidance support for data exploration. Conversational interfaces enable exploration of the data and insights search space in small incremental steps as the conversation with the data progresses. In this demo, we describe Boomerang, a system that recommends data-driven insights to guide exploration of datasets through a conversational interface. Boomerang aggregates recommendations from a variety of statistical, collaborative, and content-based recommenders, and selects insights that match closely to the user's current state of data exploration, represented as the \em conversational context. Boomerang combines various metrics, such as \em relevance, \em interestingness and \em timeliness, to rank the insights and recommends the insights based on current conversational context. In the demo, we will show how Boomerang enables guided data exploration on a sales dataset, containing information about products, retailers, sales, orders, inventory levels and regions.