Graph analytics is essential for analyzing today's connected data, as simple statistical analysis alone fails to capture behaviors within complex systems. The concept of "valence" in big data refers to the tendency of individual data to connect and form new aggregations, with higher valence indicating more connections within a dataset. Valence increases over time but not uniformly, leading to power-law distributions and scale-free networks with hub-and-spoke structures. These dynamics complicate traditional analytics approaches, requiring more sophisticated methods to model scenarios such as network evolution and emergent properties. Graph algorithms can reveal the workings of intricate systems and networks at massive scales, empowering organizations to understand their data in new ways and uncover patterns that are undiscoverable using traditional methods.