Over the last year, I've had the opportunity to work with a wide variety of insurance companies in North America. Outside of a few large insurance companies, many insurers continue to struggle with understanding how "analytics" fit within the organization. What's causing all of the trouble? The first challenge is definitional: The term "analytics" has become so ubiquitous as to lose meaning. So let's toss the word away and get focused on what's really important: Your business goals and strategy.
Over the past decade, insurers have made significant investments in their operational systems, but very few have used that opportunity to capitalize on the new wealth of information or capabilities that those systems enable. Here's a tale of two insurers:
In one example, an insurer was in the process of installing a new policy administration system. A policy administration system replacement may take years and cost millions of dollars to implement, depending on the complexity of the business and the system, and with any large scale technology project, cost and time overruns are common. When it comes down to the wire, information based projects (such as data warehousing initiatives) tied to the implementation are often de-scoped or scrapped to ensure that the implementation aligns better with a project schedule or budget, the thought being that the information delivery project can be picked up at a later date. More often than not, though, limited thought is given to the information needs or requirements, and post-implementation, the data isn't in a format that's easily consumed, or needed data elements may not be persistent in the system. Backfilling information delivery requirements down the road can be expensive and time-consuming.
The insurer in question had a data-warehousing roadmap that directly aligned with the implementation of its administration system. Information consumers participated in the requirements gathering so that necessary data elements would be captured in the warehouse as the new system was being built. The carrier is driving intelligence out of their operational system that will ultimately be used within new high-value business initiatives to offer new services or products to customers and agents, increase retention of profitable customers and better manage and monitor exposure and risk. Not only had the insurer tied an information strategy to its operational implementation, but it also included quantifiable analytic goals as part of the implementation success criteria.
In another example, an insurer was going through a similar transformation with its administrative systems, but was struggling to develop a strategic vision for leveraging information. The business executives, while they recognized that they were missing opportunities to perform deeper analysis to identify and react to emerging trends, also understood that the area of predictive analytics needed further exploration and development within their organization before they committed to any initiatives. The executives express concern about the overanalyzing of data and their internal ability to develop and execute on analytic insight. In the words of one executive, "We've done some work on [predictive] analytics and we've put it on the shelf -- the jury is out on how to use it effectively...it gets a lot of push-back internally. We need to figure out where we're going to be effective with it."
Several common themes emerged for this insurer: Each functional area identified desired capabilities around increasing data access, data quality, ease of accessing and distributing data and building out a stronger analytic capability; and there was consistent pain in terms of how these functions were performed today. However, there was no consensus on the amount of pain in the business's ability/inability to efficiently gather data and turn it into actionable information. Their pain points weren't painful enough.
Even though this particular insurer decided not to move forward with an information or analytic strategy, they should continue to push forward with ideas for leveraging their data. Once specific strategies have been identified, you can begin to identify the information sources needed to support those strategies, the methodologies, technologies or tools necessary to deliver the insight, and ultimately the process of linking all of those things together. Even if your information delivery project becomes de-scoped, you will be better positioned to pick it up when the time is right.
Call it information delivery, business intelligence, analytics, business analytics, predictive modeling, whatever, but don't get lost in the terminology -- keep your eye on what's important to the business and then begin to identify what you have the ability to influence through the strategic use of information.
About the Author: Rachel Alt-Simmons is senior industry consultant for insurance at Cary, N.C.-based SAS. She has held executive roles at major P&C insurers and was formerly research director for Life and Annuity, within TowerGroup's Insurance practice.