Why Historical Insurance Data Models Don't Work in our Current Environment

Classic data modeling and history-based actuarial models do not comprehensively work anymore. In order to get useful insights that can be implemented to support customers and the business, insurers must rapidly incorporate new data sources in their analysis. Current events that are different than anything we have seen in recent history force us to get used to a new world that cannot totally be evaluated and analyzed based on historical experience and knowledge.

Embracing Big Data And Multi-Function Analytics To Better Manage Complexity

Guest post by Mark Ferman, Sr. Oil & Gas Analytics Advisor Oil and Gas companies operate within one of the most demanding business environments on the planet with an array of complex challenges that regularly test their ability to innovate, plan, and execute strategic objectives.

Operational Database NoSQL and Related Capabilities

This blog post is part of a series on Cloudera’s Operational Database (OpDB) in CDP. Each post goes into more details about new features and capabilities. Start from the beginning of the series with, Operational Database in CDP. This blog post gives you an overview of the NoSQL, component integration, and object store support capabilities of OpDB.

Qlik Sense in 60 - Comparative Analysis (Alternate States)

All versions of Qlik Sense allow you to designate specific selections and visualizations to interact with one another - independently of the default selection state. Therefore you can make different value selections on the same dimension and compare those results in two or more visualizations side by side. How to: Learn more.

Splunk your Kafka with SQL

Here at Lenses.io, we’re focused on making data technologies such as Apache Kafka and Kubernetes as accessible to every organization as possible. It’s part of our DataOps vision and company DNA. Lenses is built by developers, for developers. We understand the headaches they live with and the challenges they face seemingly have to learn a new data technology every few months. We believe that’s just not the right model.