Description: This session will discuss strategy and tactics for becoming good stewards of consumer/customer data. Topics will include various techniques, such as anonymization, pseudonymization, tokenization, and the merging of disparate, de-identified data sources.

Description: While it is now almost universally accepted that data can bring value to the insurance industry, there is little guidance on exactly how to make that value tangible. This session goes beyond advances in computing and storage technologies and is aimed directly at business leaders. This session will define what a data strategy is, discuss the main components, describe how to get started creating one, and will outline how to translate a strategy into execution.

Description: Simple mathematical equations often outperform very complex ones in predictive analytics (PA) situations. This session will cover "why" and "how" to guard against overfitting and other potential pitfalls that might not be intuitive.