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Property Data Strategies: A Three-Point Model

Anthony Hetchler, Jonathan Kost, Karen Linnell and Mike Szocs December 2007

A routine, "fit-for-purpose" data assessment helps carriers plan wisely.

The world is awash in data. Just spend a few minutes on the Internet and you'll find hundreds of companies offering data purported to support your business and bring you value. 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.

The rapid change in the amount of data, sophisticated software and consulting services focused on business optimization creates opportunities for those companies seeking to dominate their market. The approach that leading companies engage in, and that delivers the greatest value, has three interconnected parts: acquisition, validation and prediction.

These three concepts form the basis of mission-critical, "fit-for-purpose" data strategies. Executives leverage these concepts to gain the most from their investment in business optimization initiatives.

The degree to which a company performs and repeats its assessment of a project's appropriateness on a periodic basis, and readjusts the plan to eliminate substandard results, determines whether it stays competitive, moves to the front of the pack or gets left behind.

ACQUISITION: RELEVANT DATA

The benefits of data acquisition are directly related to fit-for-purpose: the appropriateness of the data for its intended use. Further, the value of that data is highly reliant upon each carrier's business aptitude, and how well the data is mapped and integrated into carriers' business applications.

For several years, competitive carriers have been archiving data captured during the new business process. They also have purchased data from vendors with goal of accessing data at the right point in the underwriting or claims workflow to support critical business decisions.

While data is available from a variety of providers from a wide spectrum of business verticals, inappropriate data and its incorrect application can result in skewed business models and incompatible business decisions. For this reason, optimizing the benefits of data often requires the assistance of expert data analysts.

One of the newest methods for staying ahead of the pack is through a data consortium. An early example of this is the Comprehensive Loss Underwriting Exchange (CLUE) database. This database is one of the first instances of an insurance consortium created to provide the property insurance industry with consumer claim information. In a consortium data-sharing model, each participating carrier agrees to allow a predetermined set of data to be stored and accessed, with appropriate security measures, by other consortium participants. The pool of data from a large number of participants improves the statistical accuracy of data analysis.

Other examples of this type of consortium are being brought to market now. This is especially significant for small- to mid-sized companies, who gain the ability to remain competitive despite the fact that their own market share (and, therefore, database size) is not large enough to use for market observations, product enhancements and evidence-based decisions. Early participants in consortium opportunities gain a knowledge advantage that improves business performance substantially, allowing them to get ahead and stay ahead of the pack of later adapters.

Data sharing is optimized when the data is based on a common platform, data library and layout from within a core vertical market. For that reason, data sharing within the property insurance vertical provides more relevant data, ensures better data quality and provides the necessary data points to drive specific property market-related business models.

In addition, the inherent risks and costs in acquiring third-party data analysis, validation and integration are minimized or removed.

VALIDATION: DATA ANALYTICS

The standard way for property insurers to use data acquired through a consortium or other means is in routine reports that use underwriting and claims data for insight into areas such as agent or staff performance, customer service, cycle time, etc. But, while the reporting tools are sufficient for telling a manager about yesterday, they offer little insight into what tomorrow may bring.

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