Data I Commercial lines underwriters today are exploring the application of real-time data along with proprietary and third-party data to augment risk assessment and pricing. But the data underlying the risk selection is still mostly unstructured. Instead of spending their time analyzing complex risks, underwriters are having to expend copious amounts of time in sifting through unclear and unstructured data, handling multiple workflows, and standardizing the data for inputting into pricing tools to derive meaningful insights. Moreover, the data needs to be error-free and accurate for pricing to be accurate and for quotes to be quick. It cannot be outdated, incorrect, or incomplete. Innovative insurtech initiatives are enabling real-time, proprietary, and third-party data to pave its way to commercial lines. Underwriting needs such data to be of high quality for it to deliver predictive power. The data should also be easily ingestible into the system to offer any real value. Third-party data, such as address records in property insurance, can now be populated accurately and in a standardized manner using a modern geo-location solution. Such solutions employ geo-coding of parcels and buildings (going way beyond street addresses) and have very high fill rates and accuracy. Driving losses down with data-driven underwriting will require insurers to seek buy-in from policyholders, and more data and time to account for the entire policy lifecycle.