How does Decision Intelligence use Graph technologies?

Graph technology is a sort of catch-all phrase that includes graph theory, graph analytics and graph data management. Graph represents the next major evolutionary step to enhance analytics delivery. As data volumes grow, traditional analytics often fails to address complex business operations, delivery and analysis problems. Graph technology helps find unknown relationships in data that are not being identified or analyzed through traditional means. A graph database stores nodes and relationships instead of tables, or documents. Data is stored just like you might sketch ideas on a whiteboard. Your data is stored without restricting it to a pre-defined model, allowing a very flexible way of thinking about and using it. Ultimately the output from graph analytics emerges through visualization tools, knowledge graphs, specific applications and even some advanced dashboard capabilities of business intelligence tools. Graph is also often used in all three parts to make systems run more efficiently and even support data management in a dynamic approach. In this way, there is a direct link between graph theory and analysis, and analysis can always use graph databases.

Gartner predicts that by 2023 graph technology will play a role in the decision-making process for 30% of organizations worldwide. In fact, many businesses are already using it in a variety of ways. Decision making has to be even quicker and made in ever-changing and complex environments, and graph technology will help enable this shift. From allowing for a better understanding of customers to detecting fraud to optimizing crop yields, graph technology has powerful applications across a variety of industries.

Data and analytics are more critical than ever before, and the use cases for graph technology will only explode as businesses need ways to drive decision making and stay competitive. Decision intelligence leverages the core capabilities of graph technologies to simultaneously connect to multiple data points rather than to just one data point at a time, as within a traditional relational database. And by connecting multiple data points at once, users (or a system like DI) can more easily discover relationships between data resulting in speed, accuracy and ultimately improve decision accuracy and velocity. Behind decision velocity is the business knowledge graph - interaction over time form the business graph and that is how organizations go from data to decision - how we close the gap from insight to action.