According to a recent PwC survey, 89% of global insurance CEOs said they need a new strategy for customer retention and growth. That is because there are changes in the global insurance industry driven by social, technological, environmental and economic forces such as distribution disruption and customer revolution driving direct choices; disruptive business models and competitive advantage through big data analytics; globalization with a two-tier operating metric in mature versus emerging markets; sophisticated risk models and risk transfer; and new environmental and regulatory considerations. That’s when data virtualization for insurance benefits show up.
To navigate these changes and emerge on top, CEOs and CIOs are looking to leverage more data, more new types of data, across more domains - customer, social, product, risk, and financial data - and with more agility. Enter data virtualization. From global and established players to the disruptive startup, many companies are taking advantage from the data virtualization for insurance benefits that adds a layer of abstraction over existing technologies that in turn deliver speedy access to disparate data, agility to change, and flexible ways to deliver information as a service to systems of engagement including mobile and cloud applications. In addition to being adopted as the foundation of an agile enterprise information architecture, below are some of the specific applications of data virtualization for agile data integration and data services:
A large diversified insurance and financial services organization uses data virtualization to provide a unified view of risk and underwriting data from internal and external source systems (property DBs, claims DBs, catastrophe modeling systems, rating web sites, IOT sensor data, etc.) to enable faster and more informed underwriting decisions.
A crop insurer uses data virtualization to integrate Big Data analytics insights from climate sensors, crop yields stored in several big data systems (Hadoop, Hive, BigInsights, DynamoDB) with other business data such as transaction, sales, and agent data stored in Cloud and enterprise applications, to provide agile and timely reports to their sales and management teams and feed self-service insights into the analytics by customers and agents which enables repeat business.
A business services company required different operating models and systems for their insurance and auto services divisions, yet retain unified customer service. They used data virtualization as an abstraction layer over IT systems in both business entities so that members and operational processes are shielded from underlying source complexities, enable independent migration of data and applications to the cloud, and provide integrated and secure virtual customer 360 views to customers and agents.
Multiple acquisitions by a leading European insurance carrier created disparate data silos which resulted in fragmented customer views. They used data virtualization to speed up the process of integrating all the customer information and combining it with the rest of their internal systems to create a single customer view.
One of the ways advanced and predictive analytics is different from traditional analytics is that there is no pre-determined hypotheses that constrains the required data set. Rather you let ALL the data tell the story as it is, with no pre-judging. A major insurer did that by including new social, customer, Internet of Things data alongside traditional data on customer demographics, policy, claims and environmental data sets. To do this quickly they used data virtualization to enable a logical data warehouse approach to analytics across multiple warehouses and a Hadoop data lake. As patterns emerged they could quickly shift only the relevant data resources and results from one to another data store as needed. This saved tremendous amount of time and cost.