What is Embedded Analytics?

Embedded Analytics can simply be described as the inclusion of analytical visualizations in an application. In this brief article, we explore embedded analytics along several dimensions and we will make use of a fictitious company called Plugs Electronics that sells consumer electronics.


The first dimension is the intended audience for the information included in embedded visualizations. The audience can be one of the following:

  • Business users can build reports and dashboards in a familiar spreadsheet interface.
  • Easily track data lineage and the transformations needed to productionalize data products built with input by the business.
  • Data teams can leverage the work done by the business in Sigma to productionalize data products faster.


The second dimension is the placement of the embedded visualizations in the application. Single visualizations or a group of visualizations can be embedded in a page that includes other UI components. Another common scenario is embedding entire dashboards on their own.


The third dimension relates to the important question of data exposed by the embeds. The embeds can either be secure such that users have to authenticate before they can see data and the data itself is filtered based on the user. Or, the embed may be public such that authentication to an application is not required and only anonymized or benchmarking data is shown.

Development Platform

The final dimension, and probably the most important, is whether the visualizations to be embedded are built in-house by your application’s developers or you use a third-party analytics platform for the purpose.

Sigma’ Embedded Analytics offers an easy to use, economical, secure, scalable, and flexible platform that is designed keeping all of the factors we have considered here in mind.

We are Sigma.

Sigma is a cloud-native analytics platform that uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse. It requires no code or special training to explore billions of rows, augment with new data, or perform “what if” analysis on all data in real⁠-⁠time.