This group establishes the data standards for reinsurance and sets a charter that describes the overall data strategy, identifies how to source information, outlines roles and responsibilities, and explains the objectives and goals of the transformation. The success of this team is dependent on the institutional knowledge and experience of stakeholders from the business, corporate functions, and technology teams.

The data and analytics strategies are different yet synergistic: The data strategy is typically a high-level plan, whereas the analytics strategy usually addresses the specifics. The data strategy is intended to be a broad-based set of goals, measurements, and quality requirements established to answer the following questions that will feed into the analytics strategy:

• What are we going to do, and how will we accomplish it?
• What are the measures that we can and should establish for both internal and external parties?
• What data can and should we source to meet those demands and measures?

The goal of the business case is to help stakeholders understand the cost/benefit of making a likely large investment in data and technology infrastructure. It will be incumbent upon the drafters to provide an honest assessment of the upfront costs weighed against the anticipated or defined benefits. While not applicable for every organization, this step will help build buy-in and support for any investments that are made.

A data governance model should unite and align the broader organization's data management and governance practices with the strategies set forth by the chief data officer and/or chief information security officer. It cannot be overstated how critically important this step is for the success of anything done from a data perspective. The efforts to be undertaken at this step differ when you have a data governance program defined and need to extend it to the reinsurance domain.

The quality of your organization's data is paramount from a transformation and regulatory perspective. Defining the standards and metrics for measuring data quality are critical for an enterprise data management program as these benchmarks can and should vary by domain, element, age of data, or other factors. Further, it’s not a one-and-done; Data quality analysis should be ongoing as your organization incorporates historical data elements that were previously managed manually using spreadsheets or disparate systems.

There are a number of ways for your organization to connect and store its reinsurance data. It typically requires a combination of tools, technology, and talent, however, this should be considered only after advancements and enhancements are made to the broader data infrastructure.