Data Products for Analytics Agility - Implementing Insurance Data Analytics Products

In our previous posts, we introduced the concept of a product operating model for insurance data analytics and examined common pitfalls that can derail implementation. Now, let's explore what these data analytics products look like in practice and how they create value across the insurance value chain.

What exactly are insurance data analytics products?

In a mature state, there are very likely hundreds of data analytics products in an insurance business. Start by thinking about the insurance value chain and the data domains associated with the underlying core business processes. Each data domain is a family of data analytics products, and each data analytics product may include several features (data asset(s), data feed(s), tabular report(s), dashboard(s), inference(s), ad hoc user query interface, etc.).

• Marketing – product development and marketing measures such as product development and marketing acquisition costs; campaign attribution; etc.

• Distribution – producer (agent/sales reps) counts, locations, and hierarchies; appointments and licensing; commissions; etc.

• Flow – submissions; quotes; conversion ratios and cycle times; etc.

• Policy Financial – premium-bearing financial transactions (i.e., issue, renew, endorse, cancel, reinstate); written and in force premium; earned premium, retention and price change; exposure; etc.

• Claim Financial – claim financial transactions (open, reported, paid, and reserve adjustments); frequency; severity; loss reserve and development; cycle time; IBNR; etc.

• Billing Financial – disbursements; receivables; aging/DSO; cash flow; etc.

• Policy Operational – time, effort, and cost of underwriting, customer service, and other policy operations

• Claim Operational – time, effort, and cost of claim adjudication, customer service, and other claim operations

• Customer Experience – wait time; resolution cycle time; satisfaction; etc.

As summarized above, we need to derive many insurance business process measurements (facts) in our data analytics products, but the list is not complete. We also need to produce rich "dimension" products that can be combined and used to filter, aggregate, and relate measurements—enterprise dimensions such as market segment, branch, LOB, product, producer, geography, date, time, etc. These products (and producer responsibilities) may be bundled into the core data domain product families; however, they need special product management considerations—enterprise dimensions must be defined and used consistently within and across data domain-oriented measurement products. Dimension data sources often transcend data domains, and include 3rd-party data. For these reasons, dedicated product management role(s) for enterprise dimension products should be strongly considered.

There's one more thing to wrap up our description of insurance data analytics products—data analytics products can be combined to create new data analytics products. I alluded to a prime example in the product operating model section above—Earned Premium and Reported Loss data products can be combined to create a Loss Ratio data product.

Conclusion

Since the dawn of the insurance industry many centuries ago and the founding of many leading P&C insurance companies over a century ago, we have used data analytics to select, price and manage risk—data analytics, particularly predictive insights, are the business. Our data analytics products are more important, complex, and regulated than in other industries, so we can't count on generalized approaches or expect to copy the successes in or across many other industries. We must do the work of adoption and adaptation for insurance business success, which requires a fundamental alignment of business leadership, product management, and information technology practices.

This concludes our three-part series on Data Products for Analytics Agility. We've explored the need for a new approach to insurance data analytics, identified common implementation pitfalls, and outlined what effective data analytics products look like across the insurance value chain. We hope these insights help you transform your organization's approach to data and unlock new sources of value.