The multi-tenancy services reduce the complexity and customizations that are often required when embedding BI into your OEM software, ERP application, or other SaaS applications. Its capabilities allow you to easily maintain data isolation while supporting multiple tasks within the same account or organization at the same time.

The best embedded analytics solutions offer a lot more than just data visualizations. We gathered a list of 7 embedded analytics features you need to keep in mind when choosing your embedded analytics vendor.

Stored procedures are one of the most used and most popular database subjects and as such, they offer a handful of benefits. Using stored procedures can simplify and accelerate the execution of SQL queries, and more.

It’s important that analytics and visualizations are embedded within the ERP software itselfitself, so users don’t have to export the data to a separate application. And if the analytics are easy for business users to use with self-service capabilities, then more people throughout a company can do deeper dives into a range of pertinent business data.

Having company insights at your fingertips is the most valuable advantage of mobile BI. You are not limited to one computer in one location, but instead, you can access important data information on your mobile device at any time and from any location.

Data-driven insights can help you identify business opportunities, detect customer journey leaks, and proactively identify weaknesses in your product before they grow into serious problems.  

The real value of embedded analytics is how it strengthens products and business outcomes. It shortens development cycles, cuts costs, and keeps analytics inside the workflow where users need it. The biggest payoffs are higher adoption, predictable ROI, and new revenue through monetization. With SDK-first integration, white-label control, and AI-powered insights, embedded analytics delivers scale, flexibility, and a lasting competitive advantage.

A Treemap Chart is designed for drill-down scenarios. It shows the relative weight of data points at more than one level allowing users to continuously drill down deeper into the data set that is represented by smaller rectangles for more efficient analysis.