The rise of Graph Structured Query Language (GSQL), Apache TinkerPop Gremlin and SPARQL Protocol and RDF Query Language (SPARQL) as a Structured Query Language (SQL) interface that queries graph databases will be pivotal to increasing the adoption of graph technologies. This is particularly true among the business/data analyst community that’s already familiar with using SQL.
Sample Vendors: Amazon; Cambridge Semantics; DataStax; Progress (MarkLogic); Microsoft Azure Cosmos DB; Neo4j; TIBCO Graph Database
Range: Midrange (3 to 6 Years)
The range for graph technologies is three to six years from early majority adoption across the total addressable market, due to the wide range of possible applications for graphs and the complexity of addressing them. Graph technology can help in many areas such as improving internal claims handling. The innovation can enable faster, broader searches for data to identify potentially fraudulent activity on a claim. Graph enables data-driven decisions and consistency in claims handling. One primary outcome of this effort is a projected reduction in claim cycle time and improved claim payment accuracy. Graph enables an investigator to deeply explore relationships surrounding a claim: every person, every vehicle, every incident report, every policy. For example, there could be a legacy claim, which is fraudulent — and a connection from the person associated with that claim to a new claimant — the relationship might be two or three times removed, but a graph can uncover this nonobvious insight. Some startups are now starting to productize solutions to apply graph technologies to solve for shortest path, pattern identification, next best action and compliance use cases to support insurance. Despite the rise in graph analytics solutions that make it possible to query graph solutions using SQL, there is still demand for new skills related to graph- specific knowledge, which currently restricts growth in adoption. Insurers generally lack AI and cloud skills, which will hamper use of tools in-house. The new skills required include knowledge and experience with the Resource Description Framework (RDF), property graphs, the Gremlin graph query language, SPARQL Protocol and RDF Query Language (SPARQL), as well as executing graph analysis in Python and R.
Mass: High
Gartner, Inc. | G00786204
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