The high demand for usage based insurance products is one of the biggest trends throughout the insurance industry. There are several benefits to this model for both insurers and their customers. Usage based insurance enables insurance companies to calculate risk more accurately than they could before. Customers like it because it lets them directly affect their premiums while personalizing their experiences. The combination leads to greater consumer satisfaction, which ultimately benefits insurance companies. The downside is this paradigm creates major data management difficulties. Organizations now have to integrate external unstructured data, quickly generated, at scale. These demands are forcing them to update their data integration practices. Unfortunately, most organizations don't know how. Semantic data fabrics can solve these issues. This modern architecture lets organizations quickly integrate data from all sources. Consequently, they can focus on satisfying customers—instead of managing integrations—to realize the promise of usage based insurance.
The main data integration challenge usage based insurance creates involves integrating data from real-time sources. By definition, usage based insurance requires basing coverage on the individual data customers create. In auto insurance, such data might include the speed of customers' vehicles, the time of day they're driving, and how hard they're breaking. In healthcare or life insurance, wearables produce data revealing consumers' exercise habits or center on specific metrics around known medical concerns. These might include data about cardiac conditions or blood pressure. The real-time demands of integrating these sources are similar to those of integrating Internet of Things data. Although these data sources don't have to be processed in real-time for usage based insurance, their speed still creates other challenges. The sheer volume of constantly generated cardiac data, for example, requires integrations at a scale most companies aren't used to. The same challenge applies to integrating all of the individual data generated by motorists each day. Additionally, these data sources are either semi-structured or unstructured. Integrating them with internal structured data sources creates data modeling complications. These factors make such integrations time consuming and labor-intensive.
The Ease of Data Fabrics
The data fabric approach simplifies integration efforts for each of these issues. This architecture provides a unified semantic layer on top of any data management tool, source, or technology. It supports any use case across verticals by harmonizing all data with standard data models and terminology. Once the fabric is implemented, it's irrelevant whether data sources are internal or external, or include structured or unstructured data. All data are modeled according to predefined standards that evolve to include new sources or requirements. This semantic blending of data in knowledge graph settings speeds up the data discovery process. Business users can understand data in relation to one another and to their objectives because the standardized terminologies describing data are defined in business terms relevant to those setting up policies. This advantage lets them quickly find all the pertinent data for renewing a healthcare policy, including biometric data. Best of all, the graph technologies supporting semantic data fabrics are highly scalable. They also excel at supporting real-time use cases. Insurance companies can leverage them to integrate an array of patient data generated from wearables for the most up to date information to base policies on, enabling companies to decrease policy renewal cycle times, for example.
Usage based insurance benefits customers and insurance companies only if insurers can quickly integrate diverse data types at scale. Data fabrics specialize in this task without having to constantly wait on IT to update data models. They're a modern approach for the modern advantages of usage based insurance.