Do You Need AI To Map And Understand Your Data?

Another use case that is worth pointing out is knowledge graph. This entails building a graph representation to integrate all of the data to support a specific use case. Knowledge graphs can be narrow and support a specific application or they can be as wide as a data lake or data warehouse that attempts to keep track of everything. The problem with knowledge graphs is how to get all of the data identified and then do an ETL process that adds it to the knowledge graph in a coherent way. It is nearly impossible to do this without understanding the relationships between data and identifying when new data is relevant to the data you have in the graph.