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Embedded Analytics: Easy As Pie (Or Two Lines of Code)

By Alvin Wong
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Just how easy is it to embed a visualization or analytic content into a Web application? As part of the Logi Info v11 launch, we are introducing the Embedded Reports API which gives BI developers another way to incorporate Logi-generated content inline within an application. This capability is particularly useful to software providers and SaaS companies who OEM the Logi Info product and embed analytics throughout their existing applications.

In addition, the API takes advantage of the security model integrated with that of the host application, not only authenticating the identity of the end user, but also validating the access rights of that user.

Virtually any content can be embedded via the API, whether it is a static chart, an interactive visualization, or a whole dashboard. The API is also based on open Web standards, utilizing a combination of an HTML DIV tag and Javascript that renders on the end user’s browser. In addition, the API is compatible with Logi’s existing mobile capabilities, so if an end user were to bring up your application on their phone, a dashboard could rearrange its panels automatically.

The process for utilizing the Embed API begins in Logi Info Studio with configuring a standalone dashboard or report like the one you see below.


After setting up the desired security interface, grab the code snippet and insert it into the source code of the host application.  The script tag references the Javascript file for the API.  The div tag references the specific content to be placed in the host application.


Finally, the embedded dashboard should render within the host application.


To answer the original question, it takes just two lines of code to embed content into your Web application.

For technical information on the Embedded Reports API and the multitude of implementation options available, visit Logi DevNet.


Originally published January 21, 2013; updated on June 15th, 2018

About the Author

Alvin Wong has an extensive background in solution architecture and implementation of SaaS and business intelligence applications. Alvin earned his MS in Engineering Management from Stanford University and BS in Electrical Engineering from Cornell University.