This blog is the second in a three-part series meant to help insurers address the typical challenges that come with modernizing IT operations. The first part outlined "4 Common Data Challenges Faced During Modernization," to read it, click here.

Mid-market property/casualty carriers looking to offer customer self-service usually face four key challenges:

1. Customer data is fragmented across multiple source systems

2. Data formats across systems are inconsistent

3. Data is lacking in quality

4. Systems are only available in defined windows during the day, not 24/7

Here are four solution patterns that are commonly used to meet these challenges, along with their pros and cons, and applicability:

1. Service-Oriented Architecture

SOA consists of independent, message-based, contract-driven, and possibly, asynchronous services that collaborate. Creating such an architecture in a landscape of disparate systems requires defining:

Services that are meaningful to the business — for instance, customer, policy, billing, claims, etc.

Common formats to represent business data entities

Messages and message formats that represent business transactions (operations on business data)

Contracts that guide interactions between business services

Organizations such as Object Management Group and ACORD have made a lot of headway towards offering industry-standard message formats and data models.

After completing the initial groundwork, the next step is to enable existing systems to exchange defined messages and respond to them in accordance with the defined contracts. Simple as it might sound, this so-called service-enablement of existing systems is often not a straightforward step. Success here is heavily dependent on how well the technologies behind the existing systems lend themselves to service-enablement. An upfront assessment would be entirely warranted.

Assuming service enablement is possible, we’re still not in the clear. SOA only helps address issues of data format inconsistencies and data fragmentation. It will not help with issues of data quality, and can offer only limited reprieve from the unavailability of systems. Unless those can be addressed in concert, this approach will only provide limited success.

2. Data Warehouse

A data warehouse is a data store that accumulates data from a wide range of sources within an organization and is ultimately used to guide decision-making. While using a data warehouse as the basis of an operational system (such as customer self-service) is a choice, it is really a false choice for a couple of different reasons.

Building a data warehouse is a big effort. Insurers usually can’t wait for its completion. They have to move ahead with self-service now.

Data warehouses are meant to power business intelligence, not operational systems. If the warehouse already exists, there’s a 50-percent chance that it was built on a dimensional model. A dimensional model does not lend itself towards serving as a source for downstream operational systems. On the other hand, if it’s a “single version of truth” warehouse, the company is well on its way to addressing the data challenges under discussion.

3. Modernizing core systems

Modern systems make self-service relatively simple. However, unless modernization is already well underway, it, too, cannot be waited for, because implementation timeframes are so long.

4. Instituting a Data Management Program

A data management program is a solution that deals with specific data challenges, not the foundational reasons behind those challenges. To overcome the four challenges mentioned at the beginning of the article, a program could consist of a consolidated data repository implemented using a canonical data model on top of a highly available systems architecture leveraging data quality tools at key junctions. Implementing such a program would be much quicker than the previous three options. Furthermore, it can serve as an intermediate step towards each of the previous three options.

Also, as an intermediate step, it has a risk-mitigation quality that’s particularly appealing to mid-sized organizations.

The particular solution a carrier pursues will ultimately depend on its individual context. In the final part of this series, we’ll discuss practical steps that a carrier can take towards instituting its own data management program.

Samir Ahmed is an architect with X by 2, a technology company in Farmington Hills, Mich., specializing in software and data architecture and transformation projects for the insurance industry.

Readers are encouraged to respond to Samir using the “Add Your Comments” box below. He can also be reached at

This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.

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