Increase insights by using a Graph.
Your enterprise has invested in digital capabilities, but your analysts are still struggling with the basics of gaining new data insights based on data relationships. Is this a mindset problem associated with the combined technology and business teams, or is it a data enablement problem associated with the tools that you have selected?
Graph technologies refer to graph data management and analytics techniques. This group of technologies enables the exploration of relationships between entities. The RDF graph explicitly models every node and edge combination as a set of triples of the form <subject> <predicate> <object>.
Through the use of a graph, you can model complex real-world relationships. A simple data structure associated with medical data used for diabetes-based modeling includes the following data attributes: diabetes status, age, insulin level, glucose level, blood pressure, and BMI.
It is rare for you to model a disease and not need additional data attributes or the need to assign relationships between entities to gain new insights. In the example above, the small, simple data structure does not account for the fact that medical outcomes are tied to the success of your overall care. Moreover, success in managing diabetes is also linked to your ability to provide self-care and the status of your family relationships.
Extending these relationships in a traditional DB is difficult at best. Here, the graph could be trivially extended to include: hospital quality, nursing quality, & preventable admissions when evaluating the quality of care. Attributes of family relationships could consist of the level of communication and support. Finally, attributes for self-care could include diet, blood glucose monitoring, & foot care.
The graph represents the data in a format that can be utilized for Artificial Intelligence (AI) and Machine Learning (ML). Through ML, you can provide insights into traditional diagnostic, predictive data and integrate new knowledge gained from the models into the graph.
In this particular case, new knowledge can include classification information used by machine learning models, behavioral science rules used for treating patients that are at risk, or others.
Through these capabilities, if you have been attempting to provide faster insights into data via their relationships, using a graph is the right tool.
If your business and technology strategy requires the delivery of business insights based on synthetic, new knowledge-based data context-driven analytics, look to utilize a vendor product that uses an underlying graph while not restricting you to arbitrary data structures that do not conform to your real data.
There are multiple types of graphs. Use a Resource Description Framework (RDF) graph to facilitate storing semantic facts. Using this technology, data is maintained as a network of objects with materialized links between them. This makes an RDF graph the preferred choice for managing highly interconnected data.
RDF supports native and virtual formats, including data loading, consistency, and security, with the ability to scale up and scale out. RDF supports multiple types of interactions ranging from simple node and edge traversal and triple pattern matching for transactional uses to complex multihop queries, reasoning and inference, and algorithms that run across the entire graph structure and across multiple graphs.
To learn more about the use of Graphs & how Manetu can help, request a demo.