The speaker is a data scientist at Monsanto, working on genomic datasets to improve seed products. They were solving the problem of analyzing complex genetic family trees in their dataset, which was becoming increasingly difficult due to the large scale and complexity of the data. They discovered that their dataset naturally fits into a graph structure, making it easy to model and query using Neo4j. The company built a platform around Neo4j, creating an ecosystem with over 120 applications and data scientists, and has seen significant results in terms of efficiency and scalability. The team had to deal with legacy infrastructure and sync problems, but learned valuable lessons about modeling and coding graph data, and would do things differently if they could begin again. The speaker believes that graph analysis and Neo4j have many untapped use cases in the life sciences, particularly in areas like literature mining and family trees.