Breaking Barriers: Leveraging Natural Language Processing In Self-Service Bi For Non-Technical Users

Abstract

This paper investigates how natural language processing technologies can be leveraged in self-service business intelligence to improve the experience for non-technical users. It begins by discussing the role NLP currently plays in solving BI tool problems. Then, it covers why existing NLP technologies underperform in self-service BI use cases. Subsequently, it proposes a crowd coding approach, Empower, that aims to reduce the communication barrier between non-technical users and NLP engineers. A preliminary deployment of Empower, involving three real-world NLP engineers and seven survey participants, finds initial evidence supporting the feasibility and usability of the crowd coding approach. The results suggest that there appears to be some general interest in the approach, although its potential for scale and ongoing use would likely need to be further investigated.
Today, self-service BI platforms promise to democratize data access for non-technical users. Natural language processing—a subfield of artificial intelligence—makes processing natural language data much faster than manual tagging or annotations while solving some of the users' pain points. Unfortunately, our interviews with non-technical users and NLP engineers make it clear that existing NLP tools, regardless of their performance in handling natural language data, are still inadequate for the rapidly changing and quickly evolving self-service BI use cases. On one hand, self-service BI use cases vary by time and by individuals, making it difficult to train and supervise individuals or groups of NLP engineers to match such needs. On the other hand, NLP engineers share no common language or context with the business domain and face a high communication barrier. In this paper, we posit that translating natural language BI tasks into a semantic format may further help technical users solve repetitive BI tasks. Our research represents an initial study in this direction.