Data wrangling for insurance

Drawing on use cases from Trifacta customers, the speaker explains how to leverage data wrangling solutions in the insurance industry to streamline, strengthen, and improve data analytics initiatives on Hadoop.

The insurance industry has always been heavily reliant on data, but its traditional IT systems were never designed to handle the recent explosion in data volume, variety, and velocity. As Hadoop became mainstream, the need to simplify and speed up analytics processes grew rapidly in all business lines, from underwriting to claims handling. Whether done by data analysts or actuaries, data wrangling emerged as a necessary step in any analytical pipeline and is often considered to be its crux, taking as much as 80% of a data scientist’s time. Excel used to be the default standard, but with increasing scrutiny from regulatory bodies and reporting standards for Solvency II, a further level of transparency and ease of auditing is required. Drawing on use cases from Trifacta customers, the speaker explains how to leverage data wrangling solutions in the insurance industry to streamline, strengthen, and improve data analytics initiatives on Hadoop.