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Bruce Broussard, Insurity
Bruce Broussard, Insurity
Commentary
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Discriminating Among Insurance Data Solutions

Insurers' increased awareness of the value of analytics requires them to evaluate available options in unfamiliar territory.

The process of evaluating components of core system modernization projects has become relatively common for insurers. Defining business requirements to differentiate between core system vendor offerings has been refined over time and through experience, as well as benefiting from published industry best practices and research. On the other hand, the industry's increased awareness of the value of analytics has prompted insurers to now aggressively advance their data capabilities, requiring them to evaluate the available data-related options in much newer and less familiar territory.

There's tremendous diversity among data-related products, yet the industry often lumps them into a single "data solutions" category. Recognizing the different subcategories will help you select the right components for your specific need.

Breadth and depth of data needed

Data breadth refers to the data domains needed to accomplish the goal. Examples of data domains are policy, billing, claims, reinsurance, CRM, and financial. Knowing the breadth of data needed is critical since some data solutions are broad, covering multiple domains, while others are focused on a single domain.

Depth of data refers to the degree of data granularity. Some solutions address detailed transactional data while others only summarize and aggregate the data. It's not that one is better than the other, but rather what is right for your needs and expectations.

Bruce Broussard, Insurity
Bruce Broussard, Insurity

Evaluating breadth and depth of data coverage is also helpful in evaluating the viability of a vendor solution. The data model and evidence of how the data solution is performing in similar production environments are key to the assessment of the maturity and viability of a vendor's offering.

[Previously from Broussard: Maintaining Credibility in Project Estimation]

Data-related products versus assets

A data-related product provides out-of-the-box functionality while typically offering little in the way of options for you to self-manage customization or capability enhancements.

A data-related asset is, for lack of a better term, an accelerator that helps you to develop your solution – an asset is not a fully developed application and requires additional development by the insurer to create a finished product that can then be implemented. Some products are advanced enough to be both product and asset; providing the out-of-the-box functionality of an application that delivers immediate value and also provides tools to allow you to extend the product for your unique needs.

Recognizing whether you are looking for a product, an asset, or both is key to narrowing the list of your potential products and vendors.

Data solution subcategories

Since all data solutions are not created equal when it comes to capability, your specific needs will be addressed by one or more of the following offerings.

Data Repositories

As the name infers, data repositories are typically used to consolidate the data into a single location that all relevant applications can access – assuming necessary integration has been done. A data repository might offer a transactional data store (TDS), operational data store (ODS), data warehouse, or data mart, each designed for different uses.

Data Integration

Understanding how other applications and data structures will integrate with your data repositories is an important consideration. Some solutions support XML as well as standard ETL. Some leverage industry standard integration schema, perhaps published by ACORD as opposed to a proprietary integration scheme.

These approaches have different implications for integration effort and complexity. Understanding if you are completely reliant on the vendor to integrate to their product and their experience with your applications are also important.

Reporting

What capabilities are provided for management and operational reporting? What reports are delivered out-of-the-box, and across what data domains? What options are available to add needed reports? If ad hoc reporting is supported, identifying the tools, ease of use, and what data can be accessed are key.

Business Intelligence & Analytics

Business intelligence solutions focus on the discovery of new insights and opportunities from historical data, while analytics take it further by employing data mining and predictive analytics to determine how to best capitalize on the new insights, while also predicting what is likely to happen in the future.

While these tools can provide extraordinary insight into the business, it's only possible if they have the right data available for consumption. Vendor solutions that package multiple data repositories with business intelligence and analytics tools have a distinct advantage by incorporating the processes needed to transform transactional data into the form needed for these advanced capabilities.

Data Entry & Editing

As insurers find themselves increasingly dependent upon their ability to integrate data from external sources outside of their control, many are looking for capabilities to directly enter and edit data in the repositories. Some vendor offerings provide data entry and editing capabilities with well managed processes and auditing, eliminating uncontrolled data manipulation.

While the process of evaluating and selecting the right data solutions is new territory for many insurers, familiar rules still apply. Start with your data domain and data capability needs. Then do the due diligence when evaluating vendors and their solutions by looking at their capabilities, flexibility, and their track record of delivery.

About the Author: Bruce F. Broussard, Jr. is VP, data products and strategy at Hartford-based Insurity.

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bfbroussard2
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bfbroussard2,
User Rank: Apprentice
6/5/2014 | 4:09:37 PM
re: Discriminating Among Insurance Data Solutions
Kathy - that's a great comment and is absolutely a significant challenge. We're seeing a variety of approaches depending upon whether the driving force is from IT, the business, or both. When IT-driven, CIO's and VP's with broad business support responsibilities are often playing this role. When business driven, we're often seeing the need for analytics and comprehensive enterprise reporting as the priority - sometimes from a broad business perspective and sometimes from specific segments of the organization, such as actuarial and underwriting. One newer trend that we've been seeing a bit more of is that insurers are creating distinct enterprise level BI & Analytics groups often led by a Chief Data Officer that are blends of both business and IT. Where we've seen this, these groups have the responsibility for the enterprise view. it's certainly an emerging area, and I agree entirely that there are much more significant challenges when there isn't a clear owner for the objectives and execution responsibilities to coordinate across all of the different disciplines that are required to be successful.
KBurger
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KBurger,
User Rank: Author
6/3/2014 | 8:26:06 PM
re: Discriminating Among Insurance Data Solutions
Bruce, it strikes me that part of the challenge might be that different functions might be more or less involved with different types of data-related tools. That is, architects may be more involved with data integration, business users with BI, data specialists with the other categories. Is there anyone in the typical insurance enterprise who would need to have a big picture view of all of these, how they work together (or not) and where they are best applied? It seems to me that where there is a fractured view there's more risk of silos, redundancy, etc.What are you seeing?
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