Jennifer Zaino
SemanticWeb.com Contributor

If we can mangle the old saying to make a point, a rose by any other name (if the rose is certain business terms) will not smell as sweet but will create confusion in the property and casualty insurance industry.

In fact, the lack of a standard data model of business terms hampers the ability of IT in this sector — and in the insurance industry at large — to be as agile as the business wants it to be, and externally creates roadblocks in communications among insurance companies. But plans are afoot to bring the industry more in line with other sectors that have already developed standard data models, such as banking and health, and semantic standards are expected to play a role in the design.

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ACORD, the standards organization that sets the data collection standards for the industry, and the Object Management Group (OMG), which develops enterprise integration standards, agree on the need to create information models to address the needs of the property and casualty insurance community. To that end, the OMG has solicited proposals from about 20 property and casualty organizations to deliver within the next year a business glossary, conceptual and logical data and data warehouse models, and a data traceability map.

“Data in the financial services arena is the lifeblood. What we really produce is from the data, how we run the business is based on data,” says William Jenkins, CIO at Penn National, who sits on the OMG Insurance Working Activity Group and has been a leader in this effort. “Data is doubling in size every 12 months, and as a result we are trying to be more efficient in how we handle and administrate data.”

The National Insurance data model that Penn has created for internal use will be used as one of the sources, where applicable, to jumpstart the effort. But because the results will have to be applicable across all 50 states and domains outside the U.S., and Penn does business in only nine, the insurer’s efforts don’t comprise the complete business glossary or data model that will be the much-needed outcome of this project.

Jenkins says that not having a complete industry standard data model and common business glossary causes big problems for insurers.

For example, as Penn expands its market share in personal insurance line (homeowners and auto insurance, for instance), having a standard data model would eliminate the requirement to stop and build a data model behind new systems. Ultimately, as new systems are built using a common data model to replace legacy infrastructure, organizations could eliminate a lot of the data conversion and integration work that now has to be done whenever businesses try to tie together disparate systems with their own databases. Thanks to the work and cost that goes into this, IT in the insurance industry typically lags the business requirements by 18 months.

And, for reporting to regulatory agencies, to other carriers, to reinsurers, “we all want to run off the same language and the same source of truth,” Jenkins says. “Our thrust also is that when we develop our data warehouse as one source of truth, that the backend reporting systems are replaced by that one source of truth.”

A common data model, then, translates into better data quality. “You don’t have redundancies,” Jenkins says. “It’s a pre-requisite for having a proper services-oriented architecture (SOA). Clean data is the DNA of SOA. And it is hard to build a true SOA model [to use SOA to talk both internally and externally] without semantic standards behind it.”

Components of the property and casualty ontology model that is being developed for this effort to support semantic reasoning, will include UML-based representation using the UML profiles for the Resource Description Framework (RDF), the Web Ontology Language (OWL) from the OMG’s Ontology Definition Metamodel (ODM), an ODM-compliant XMI representation, and a description logics (DL) compliant RDF/XML serialized representation, according to the OMG’s Request for Proposal.

Ultimately, the work that comes out of this effort could help drive the creation of data models for other segments of the insurance industry as well. Among tools or services contributed by vendors for the project are a metadata repository tool from Adaptive, a rules modeler from Unisys, and coding software from Embarcadero.

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