Data Strategy Value Drivers

These four value drivers are the most important aspects of any data initiative. They are ultimately business user-focused, and the insights delivered are relevant to specific business objectives. Aligning the data strategy to the business objectives is key and these four drivers deliver the “business first” approach.

1. Business intelligence – For example, a business user wants to see how many claims were processed today, where they are in the claims process, how many have been successfully approved and how many have bottlenecks. This would typically be presented on a user dashboard created in a business intelligence tool.
2. Advanced analytics – A business user wants to predict which types of claims take longer to process based on complexity and other factors (historical data etc.). This outcome will be modelled in some form of algorithm and the user would be presented a risk score either in a dashboard or a decision support system.
3. Monetisation does not imply selling the asset outright; there are two concepts at work here: internal and external. For example, an insurer that wants to maximise their revenue on an outbound marketing campaign can do so by analysing data the campaign data to determine who is most likely to purchase the specific products based on behaviours and patterns. They can do this in-flight and gain insights on such factors as purchasing behaviour, i.e. time of day and demographic. Doing so will enable the insurer to change and optimise mid-campaign to ensure feedback is actioned near real-time increasing conversion. External monetisation occurs when an insurer uses data to benefit another sector, partner, or supplier. For example, insurers may offer retailers anonymized data sets to market their products and services, like gym memberships as part of a healthcare policy retention drive.
4. Innovation is a broad term that refers to the use of data to create a new product, a new platform, or simply a new process that did not previously exist. A few examples could be in the marine insurance space, where insurers could provide customers with near real-time intelligence about cargo, volatility in weather patterns, temperature adjustment controls for vaccines, and so on to manage risk in the portfolio. Another example in the insurance industry is an algorithm accessible to insurance brokers via a simple user interface, that automatically determines the proportion of the risk that it is prepared to underwrite, based on a limited and standardized set of data points that are matched against the insurer’s risk appetite and exposure for a given product line.