Advances in technology, evolution of the distribution channels, demographic shift, economic conditions and regulations changes. How does an insurer prioritize all these seemingly competing goals and create sustainable competitive advantage. One answer is analytics.

Many insurance companies are just beginning to take steps toward becoming an “analytic insurer” – one that embeds analytics into daily operations to make better decisions that reduce costs, improve pricing, and more. And those organizations with more advanced analytic capabilities are actively seeking to build on previous successes and grow their analytic capabilities. And no wonder, given the increasing volumes of data being produced through enterprise business systems, online interactions, social media, and other channels.

Implementing analytics is not as straight forward as it sounds. There are many steps in the analytical life cycle to consider, but essentially it can be broken down into four main sections:

1. Data preparation

2. Analysis and predictive modeling

3. Deployment

4. Model management

 

In a series of four blog articles over the coming weeks each of these areas will be discussed in more detail.

Turning the increasing volumes of data into useful information is a challenge for most organizations, but following these simple steps insurance companies will be able to implement analytics…successfully.

I’m Stuart Rose, Global Insurance Marketing Director at SAS. For further discussions, connect with me on LinkedIn and Twitter.