Thomson Reuters has been collecting data for over 150 years and identified two major challenges faced by its financial analysis clusters: data silos and the lack of a tool to easily uncover important data connections. To address these issues, they developed an intelligent recommendation engine using a knowledge graph, a graph ETL, and Neo4j, which enables financial analysts to more effectively analyze data and make real-time decisions. The solution was built by listening to customer needs, identifying siloed data as a challenge for CTOs and data overload as a problem for financial analysts, and developing a user-centric analytics tool that links, stitches, and joins data together in the knowledge graph stack. This stack includes a knowledge graph with 50,000 employees' metadata linked to core entities, a graph ETL to extract and transform data, and Neo4j as the analytics platform to deliver insights. The recommendation engine uses Neo4j's BOLT protocol with Cypher to load and match data from the data fusion layer, run shortest path calculations, and provide answers to financial analysts' questions without giving them information overload.