Systems | Development | Analytics | API | Testing

Improve Underwriting Using Data and Analytics

Insurance carriers are always looking to improve operational efficiency. We’ve previously highlighted opportunities to improve digital claims processing with data and AI. In this post, I’ll explore opportunities to enhance risk assessment and underwriting, especially in personal lines and small and medium-sized enterprises.

Where Is Your Customer Data Located?

Modern organizations have multiple touch points continuously collecting customer data. Data collection is essential for firms that use it for personalized marketing campaigns and improving customer experience. Analytics provided by this data help enterprises observe customer behavior and make critical business decisions. However, before firms can explore any use cases, it is crucial for them to recognize the data touch points where vital information is collected.

What's new in ThoughtSpot Analytics Cloud 8.7.0

Want to bring the data-driven insights created in ThoughtSpot to the apps your teams use most? With this month's release of ThoughtSpot Analytics Cloud 8.7.0.cl, we're launching ThoughtSpot Sync that lets you operationalize your insights by sending data directly to tools like Slack, Microsoft Teams, and Google Sheets. Watch this video to learn more about ThoughtSpot Sync, along with other new features like Liveboard tabs and threshold-based alerts in SpotIQ Monitor.

Activate your data: How to get started with ThoughtSpot Sync

Every data team wants to make insights more actionable for frontline business users. The only question is how. You know they spend the majority of their time in business-critical tools like HubSpot, Slack, and Microsoft Teams. So why not bring the data-driven insights created in ThoughtSpot to the apps they use most? With ThoughtSpot Sync, you can. Starting today, ThoughtSpot customers will be able to send insights directly from ThoughtSpot to Google Sheets, Slack, and Microsoft Teams.

Data Vault Techniques on Snowflake: Streams and Tasks on Views

Snowflake removes the need to perform maintenance tasks on your data platform and provides you with the freedom to choose your data model methodology for the cloud. When attempting to keep the cost of data processing low, both data volume and velocity can make things challenging.

Building an Automated ML Pipeline with a Feature Store Using Iguazio & Snowflake

When operationalizing machine and deep learning, a production-first approach is essential for moving from research and development to scalable production pipelines in a much faster and more effective manner. Without the need to refactor code, add glue logic and spend significant efforts on data and ML engineering, more models will make it to production and with less issues like drift.