The text discusses the challenges of sharing internal information and knowledge in a distributed team environment, where traditional methods like Slack and Microsoft Teams can make it difficult to find relevant data. A solution is proposed by using Neo4j to build an enterprise knowledge graph, which allows for the identification of relationships between entities such as people, skills, and files, making it easier to analyze and gain insights from the data. The authors demonstrate how this approach can be applied to a real-world scenario, where they built a Slack knowledge graph to determine user skill levels dynamically by analyzing their interactions. The architecture is designed to handle large amounts of data from multiple sources, including Slack, Github, Google Drive, and Salesforce, and provides a custom dashboard for users to explore the data.