(Bruce F. Broussard Jr. is vice president, business development and strategic relationships with Insurity.)

The traditional application-centric approach to core legacy replacement in insurance has long been a mix of modern systems, data conversion, and policy migration at time of renewal. The advanced configuration capabilities offered with modern systems has certainly improved project success rates, but many of the major challenges and risks remain. Replacing the old application with the new offers little long term architectural benefit since both share essentially the same component and integration footprint. 

Perhaps it’s time to change the status quo.  Rather than continue with the traditional application centric approach, a data first approach may be a better way to modernization; resulting in both reduced risk and cost, and a future proof IT architecture.

The data first approach is not exactly new.  Over the past decade a number of insurers have successfully leveraged this path to modernization. Unfortunately, during that time data first has only been practical for larger insurers that enjoyed the luxury of a deep pool of resources and the ability to take on complex custom development and long term maintenance of proprietary data and integration structures. While these resource and complexity realities have kept most insurers on the outside looking in, several developments over the past few years have changed the insurance data landscape, most notably the maturity and acceptance of industry standard ACORD data structures.  As a result, the data first approach is now within reach for insurers of all sizes.

With the application centric approach, the new core insurance application is implemented to run in parallel with the legacy as policies are migrated from the old to the new.  While policy migration at renewal minimizes complexity, this process may take a matter of years before all products have transitioned into the new environment. With the policy portfolio split across multiple applications until the migration is completed, a range of challenges must be navigated, including how to produce consolidated reporting and minimize duplicate maintenance efforts.  The balancing act only gets more complicated as duplication is also required for integration services to connect both the old and new with downstream applications.

Opting for the data first approach eliminates this logistical nightmare.  Focusing effort toward integrating the data of the legacy and new applications in a common repository, which I’ll refer to as the Core Insurance Data Component (CIDC), is the initial step. The CIDC can subsequently be leveraged as the singular source for reporting and integration throughout the duration of the modernization effort and long afterwards. Since the CIDC will always reflect the current and consolidated version of the data, a complete portfolio view is always available, regardless of where any given policy is during the migration process. 

Many have attempted to build the equivalent of a CIDC in the form of an enterprise data warehouse, independent of a core modernization project. Unfortunately, a disproportionately high number of these data warehouse projects fail due to a variety of challenges which typically include cost and resource requirements.

However, in the context of a modernization effort, most of the work required to build a CIDC is necessary to complete the modernization project.  Since data mapping and integration are part of any core modernization project, the only remaining step to take advantage of the data first approach is to create the CIDC. With the recent evolution in insurance industry data standards, three classes of options exist that are technically and financially viable for any insurer:

  • Pure custom development involves the design, build, test, and implementation of the CIDC from scratch.  This approach typically takes the longest to implement and is also the riskiest. Without a proven design to start the process, the risks of creating a repository that is incomplete or performing poorly makes this an unattractive choice.
  • Model accelerated development offers a viable option for many insurers, and has been the favored approach by most of the larger insurers who have successfully delivered modernization projects over the past decade. The design phase for the repository is accelerated by using either proven vendor data models or a replication of the data model for the new application as the starting point; reducing a key risk of pure custom development.  However, the modernization schedule is still lengthened since the repository must be designed, built, tested, and installed before the legacy and new applications can begin to populate the CIDC.
  • Vendor product implementation is a relatively new option, as vendor products with readily implementable insurance repositories have only recently been made available.  This approach is attractive since there is no design or development effort involved, and the product license may be far less costly than building the repository through either of the other two approaches. The vendor repository is already fully built, tested, and ready to be installed, avoiding any unnecessary delay to the modernization project.  By leveraging industry standard integration methods, such as ACORD XML, cost and time are reduced as well as the complexity of populating the repository.

By implementing and leveraging a CIDC through the data first approach the business and other downstream applications are insulated from core application changes, both during the modernization project and long after. The CIDC’s simplified integration structure helps future proof the IT architecture, reducing total cost of ownership and reducing the ripple effect of application changes across other components.  The new environment provides a platform for standardized, balanced and reconciled data stores that lowers costs by eliminating processes required to aggregate and validate data from disparate sources.  Also, the platform offers the opportunity to expand business intelligence and analytics, better leverage Big Data, and deploy a broader and more consistent user experience, including self-service options.

 Given the industry’s disappointing results when it comes to application centric modernization and independent data warehouse projects, there is no better time than the present to take advantage of an approach that kills two birds with one stone; delivering greater benefits at reduced risk. Change the status quo and start with the data first.