How Data Transformations Impact Later Distribution Management Transformations: Pieces to Consider, Challenges, and Pitfalls
What is the biggest mistake carriers make when undergoing a data transformation?
Carriers often view enterprise data modernization in a silo. While the value of data modernizations is well understood from a BI and Analytics perspective, many carriers fail to recognize the benefit mature data models and modern data infrastructure can have on transforming other areas of the business. Carriers lose out when they fail to carry modern data practices to other areas of their business such as compensation. Carriers may end up carrying existing data issues into their new systems.
We’ve seen examples of carriers who did not take the opportunity to mature their data model, ahead of undergoing a Distribution Management (DM) transformation. This limits the carrier’s ability to successfully transform their Distribution Management model, when the time comes. Some carriers have ended up rebuilding a lot of their existing complexity into their new compensation system. They were limited by their data in designing their Distribution Model, and carried some of their existing issues forward, because they did not prepare for their DM transformation, while undergoing their data transformation. Starting with a data transformation, and ensuring that data is available and consistent, before undertaking a DM transformation could help prevent data issues leading to downstream complexity.
What should carriers do, as a part of their enterprise data strategy, that could set them up for a successful distribution management transformation down the line?
Consistent formatting. Oftentimes, we see carriers fail to account for line of business or product nuances in their data. To account for complexity inherent in their products or lines of business, carriers will customize data attributes based on line of business. This not only builds institutional knowledge into the data, but necessitates product based handling from the compensation system.
Consistent timing. As carriers modernize platforms they move from batch to real-time data integrations. Building a data platform that can accommodate both batch and real-time integrations may be critical to ensuring legacy practices (i.e., batch) are not built into modern systems.
Standardized business handling. We’ve seen carriers establish custom handling practices that vary across lines of business or products. These processes are required due to data inadequacy. They embed institutionalized knowledge into the data and make downstream commission system handling of standard business events (e.g., cancellations and reinstatements) more complicated. That complexity flows down to the carrier’s compensation system, and ultimately makes any future compensation plan updates or transformations more difficult.
Do you have any horror stories you can share where things have gone awry?
We’ve seen many examples of carriers siloing their data and distribution transformations. At one carrier, we saw a dogmatic aversion to modifying any upstream systems during their compensation plan transformation. This ultimately resulted in the carrier passing complexity from their policy admin systems into their comp system; the end result was a ‘transformed’ compensation model that was more complex and costly to maintain, than it would have been, if the carrier had addressed upstream data challenges first.
We’ve also seen a carrier that architected data to mirror their legacy systems. After their DM transformation, the carrier was left with a new comp model which maintained all of the carrier’s legacy issues.
What can carriers who have started, or are ready to start, their DM transformation journey do now, if they haven’t undergone a data transformation?
The first step is to make sure the data required for new compensation plan requirements is available, in a usable format, from upstream systems.
The second piece to think about is the data required from downstream systems. Carriers should determine what data is needed for functions like reporting and accounting, and determine where that data is coming from (e.g., policy admin systems, data layer, compensation system).
If required data from upstream systems, or data required to pass to downstream systems, is not available, this is an indicator that the carrier may need to pause their DM transformation and rethink their data strategy. Data transformation efforts should be the first step in a DM transformation, and can help identify and alleviate data issues, before they result in complexities downstream.
