Graph is transforming the data analytics landscape, and many organizations are accelerating their data-first modernization by implementing enterprise-scale, graph data platforms. Graph database management systems (DBMSs) are a type of NoSQL database growing in usage and popularity because, through graph analytics ( and the products that support them ), they are helping businesses decode complex relationships between entities.
Large datasets can benefit from graph technology because of the characterization, evaluation, and prediction relevant to the relationships represented by the graphs. Graph analytics helps uncover these connections, called nodes or vertices, which can be developed from datasets such as accounts, customers, devices, groups of people, organizations, products, or locations.
Some examples of data well-suited to graphs are road networks, communications networks, social networks, webpages and links, and financial transaction data. Some examples where graph analytics has been instrumental include-
• Uncovering product or service recommendations from ratings and purchases to increase customer retention
• Identifying fraudulent transactions to reduce shrinkage in retail
• Refining routes to save time and reduce overhead costs for logistics companies
• Identifying potential points of failure in power grids, water grids, and transportation networks to reduce downtime
These are just a few use cases showing the impact of graph analytics for organizations looking to make more data-driven decisions that tell the story behind big datasets.
Vote in the poll below and feel free to share any of your thoughts in the comments!
Do you utilize graph analytics at your org?
POLL: Do You Use Graph Analytics?
- Yes
- No
- Not yet, but interested
- Not Interested
