What are the Different Types of Graphs? The Most Common Misconceptions and Understanding Their Applications

Over 80% of enterprise data remains unstructured, and with the rise of artificial intelligence (AI), traditional relational databases are becoming less effective at capturing the richness of organizational knowledge assets, institutional knowledge, and interconnected data. In modern enterprise data solutions, graphs have become an essential topic and a growing solution for organizing and leveraging vast amounts of such disparate, diverse but interconnected data. Especially for technical teams that are tasked with making AI “work” or provide value for their organization, graphs offer a programmatic way for explicitly modeling relationships between various data entities, providing business context to tabular data, and extracting knowledge from unstructured content – ultimately delivering what we call Knowledge Intelligence.  Despite its growing popularity, misconceptions about the scope and capabilities of different graph solutions still persist. Many organizations continue to struggle to fully understand the diverse types of graphs available and their specific use cases.