Knowledge graphs can connect knowledge from different domains, data models, and heterogeneous data formats without changing their form. Used correctly knowledge graphs can help enterprises harness their collective knowledge. Also, knowledge graphs can connect all the knowledge in an enterprise thereby enabling their Artificial Intelligence (AI) initiatives to deliver recurring value.
The primary cognitive computing benefit of knowledge graphs is their capacity to standardize data from disparate sources. This capability enables enterprises to leverage common data models across the enterprise for any use case including business concepts specific to respective departments. Besides, departments can customize the model to their own needs in terms of business concepts, terminology, and tools of choice. The consistency of this unified model saves enterprises from creating data silos that are costly to integrate across the enterprise.
The harmonization of semantic graph data models offer enterprises recurring business value. For instance, a salesperson can use the data on the previous interactions (across business units) an enterprise had with prospects to achieve better business outcomes. Similarly, a horizontal view of data across the enterprise helps customer service agents in their interactions with clients. Significantly, this method can also be used for developing a comprehensive, 360-degree view of customers. Put simply, graphs help people in an enterprise maximize value from data collected by others in an enterprise.
Knowledge graphs’ common data models propagate enterprise knowledge for driving sales, perfecting customer interactions, or discovering new treatments to improve society. It can help enterprises capitalize on data-driven processes with the aid of artificial intelligence. When paired with elements of machine learning, graphs can automatically map incoming data sources to models and declare business rules in natural language when paired with natural language processing.