Data-driven practices have revolutionized the homeowner insurance market in two important ways. The first is via insurtech options that use publicly available data to issue quotes and process claims in a matter of minutes. The second is with the real-time data of the Internet of Things.
IoT data enables homeowners to remotely monitor their residences to proactively identify insurance issues. Insurers can offer lower premiums because of the effectiveness of this approach, which reduces risk. This model also empowers consumers to actively protect their homes while realizing the benefits of better policies.
The drawback is that integrating, discovering, and accessing IoT data with typical enterprise sources creates significant data management challenges. Organizations must quickly determine which data are relevant for timely question asking, answering, and necessary action. Moreover, they have to do so on data that’s largely external, unstructured, and incredibly fast.
Fortunately, semantic data fabrics specialize in these capabilities. They’re the crowning piece of an architecture designed to seamlessly blend data in business terms for rapid data discovery and access. By quickly integrating real-time IoT data with other sources, semantic data fabrics are becoming vital for maximizing the benefits of improving homeowner insurance with the IoT.
IoT data drastically improves the coverage afforded in the homeowner insurance market. The IoT enables consumers to gain several distinct advantages. The most important is remote access for controlling different parts of their homes commonly related to insurance claims. For example, there are several choices for increasing security with smart devices for door locks, video cameras, and more. Most of these options enable consumers to monitor, lock, or unlock their properties with mobile devices. These capabilities help them respond to potential issues quicker than they otherwise could, minimizing their effects.
They also allow homeowners to prevent potential issues. There are several IoT systems that monitor data related to leaks, fires, and other devastating homeowner insurance events. Some issue real-time alerts at the first traces of smoke, for example, so insured parties can act to prevent dangerous fires from occurring, regardless if they’re home or not. Insurers benefit by reducing the risk of claims—and payout amounts. IoT data also enables them to monitor consumer habits to personalize policies and create new products or services. This foundation is essential for offering lower premiums and increasing customer satisfaction.
Smoothing Data Management Concerns
Most of the preceding benefits require low latent action on IoT data. The biggest challenge is most of these data are unstructured, continuously generated, and at scale. Simply integrating this data with internal sources related to consumers and their policies is a chore, especially for meeting homeowner insurance demands. But by leveraging semantic data fabrics as an integration layer atop their various data systems, organizations can blend diverse data models, vocabularies, and semantics into a standardized format. This approach harmonizes all data (regardless of differences at origination) so companies can integrate external and internal data.
The business terminology of these blended datasets democratizes data access for greater subject matter expertise. It also supports unlimited question asking and answering so users can personalize policies based on consumer behavior. Finally, these data are machine readable for automated alerts and real-time action, like contacting security or municipal authorities for break-ins.
The semantic data fabric approach simplifies data integration, data discovery, and low latent action needed to perfect homeowner insurance with the IoT. It enables organizations to blend IoT data with homeowners’ mobile devices and internal sources for responsive and predictive capabilities. This architecture makes leveraging the IoT for homeowner insurance practical—and even necessary.
You might also be interested in reading, “Taming the Integration Demands of Usage Based Insurance”.