Complex business ecosystem intelligence using AI-powered visual analytics

Business ecosystems are complex, dynamic systems characterized by a multitude of entities, including companies, ventures, and technologies, as well as activities and trends. Understanding the state of business ecosystems is an increasingly critical strategic imperative for many decision makers, but it is a resource-intensive activity as relevant information sources are dispersed, often highly unstructured, and not integrated or curated to deliver actionable insights. In this research, we present the design and implementation of an interactive visual analytic system that integrates artificial intelligence and graph visualization techniques to augment decision makers' understanding of the complex public narrative associated with business ecosystems entities. Our system is driven by a real-time content engine of 100,000+ global data sources including press releases, news articles, industry reports, analyst blogs in multiple languages organized across several domain-specific repositories. Following a user-specified query, the engine extracts both domain-agnostic and domain-specific entities and concepts for each document in the result set. We then model and visualize the resulting data as a dynamic, multipartite network and implement graph pruning algorithms and interactive data controls to enable users to interactively explore and discover the underlying business ecosystem from multiple perspectives. We illustrate and discuss the value of our system using representative use cases. Our study makes multiple contributions to visual decision support theory and practice, including mining unstructured data, constructing and interacting with knowledge graphs, and designing visual analytic tools for ecosystem intelligence. We conclude the study with implications and future research opportunities.