Data and Analytics Organizational Practices: A Snap Poll for the Celent Executive Panel
Available Only for Members of the NA Celent Insurance Executive Panel
Snap polls reflect questions posed by members of the Celent Executive Panel, a group of C level executives in the insurance industry. This question came about from a member who was looking for insights on how others have organized their data and analytics functions. This deck provides a summary of the responses to a Snap Poll conducted in March 2023. 36 insurers responded to this survey over the course of one week.
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The question that was posed was:
The insurer is looking at how they have organized their data and analytics capabilities.They’re considering a centralized model where all data and analytics resides in a single organization that provides services across the company.But they’re unsure where the best location is for that function to report to.They’d like to understand whether others are using a centralized or a federated model, how they should think about the pros and cons of each option, and if a centralized model is used, where that resides within the organization.
Do you use a centralized model for data and analytics or a decentralized/federated model (spread throughout the organization) or a hybrid?
If you have a centralized model,
- Where does it report? (e.g. Finance, IT, Strategy, directly to the CEO, etc.)
- Do you charge back the expenses?
- If yes, what is the basis for the charge back? (e.g premium, projects, headcount, other)
Does actuarial reside within the data team or outside of the data team?If outside, where does it reside?
If you use a federated model, is there any centralized oversight of the various units?
Do you have a Chief Data Officer? If yes, who does s/he report to?
What do you see as the pros and cons of the organizational structure you have when it comes to data and analytics?
Which of these responsibilities does the data and analytic team have?
- Business reporting
- Predictive Modeling
- Cat Modeling
- Other data science
- Data governance
- Third party data vendor management
- System selection and maintenance