Boston — While some say data is the essence of the insurance industry, accessing, managing and using data can be a daunting task, especially when doing this requires a range of technology solutions, including data warehouses, data marts, extract-transform-load (ETL), customer relationship management (CRM), unified presentation layers and business intelligence.

Only a few years ago, technology designed to collect, warehouse and mine data was considered bleeding edge. Today, those technologies are part of standard operating procedure, according to Anthony Hetchler, Jonathan Kost, Karen Linnell and Mike Szocs, all with Marshall & Swift/Boeckh LLC, who authored an INN article at this time last year.

In such a broad space, where insurers may turn to many vendors for help, understanding exactly where each vendor’s solution fits in the context of a specific data mastery initiative is a challenge, according to research firm Celent. Celent uses term “data mastery” in order to focus on accessing and using data, whether it is used to empower knowledge workers (operational data mastery) or to empower business strategists (analytic data mastery), rather than focus on the particular technology solution.

Data mastery initiatives were near the top of insurance CIO priority lists in Celent’s 2008 research, and according to the firm insurers are making investments in technology to master their data.

Data mastery projects in the insurance industry primarily involve seven areas: customer data, sales and marketing initiatives, claims analysis, fraud detection, finance and operations, profitability and actuarial. One of the most difficult parts of a data mastery project is determining exactly what data sources will need to be accessed. Relevant data for any initiative may live in a wide variety of systems, and a wide variety of technological and syntactical formats, according to Celent.

All data mastery projects include the same basic elements: original data sources, which may include a wide variety of systems; data exchange, typically either through an ETL process or a live link between systems; a data repository or data warehouse, essentially a large relational database; an analytic tool to derive information from the data by subjecting the data to various levels of analysis; and the presentation screens or user interface, which—typically Web-based—enables the viewing of reports and dashboards. In addition to these technology components, data mastery projects include a set of business intelligence domain content, such as insurance-specific object models, analysis structures, and pre-built reports.

According to Celent, most data mastery solutions are designed to enable a best-of-breed strategy, which is the path many insurers take. But Celent warns insurers to make sure the vendor understands the insurer’s business—and business people since data mastery, even more than most IT projects, requires active participation and assistance from the business side.

Sources: Celent, INN archives

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