Embedded Analytics

Also known as: Embedded BI, embedded dashboards, embedded reporting

What is Embedded Analytics?

Embedded analytics is the integration of analytical capabilities such as dashboards and data visualizations into software applications or transactional process systems. It provides business data and self-service analysis process capabilities within a user’s standard workflow natively, without dependence on external or third-party analytical solutions. Its purpose is to increase accessibility of relevant information by enabling end-users to analyze the data held within the app they use for work, and help vendors in managing and optimizing software performance.

Embedded analytics is often available as a white-label analytics option for software OEMs to rebrand the analytical component to provide a seamless blend with the design of the application into which it is embedded. This allows enterprise applications to market the analytics as their own, or grey-label their analytics so that the software platform provider’s brand name remains.

What is embedded analytics used for?

Embedding analytics into an enterprise software application introduces significant benefits for both the business and its customers, and can be used for a variety of processes.

Embedded analytics for software vendors and enterprises

Independent Software Vendors (ISVs) and enterprise organizations can increase user engagement, decrease customer churn and deliver an improved analytical user experience by offering customers out of the box dashboards and reports and the ability to perform self-service analysis and create their own content using defined and secure data sets. Embedded analytics also opens up new revenue channels, such as upselling a cutting edge analytics offering to attract more customers. Internally, embedded analytics is typically deployed for specific digital workplace processes, like financial budgeting and inventory management optimization.

Embedded analytics for end-users

For end-users, embedded analytics makes self-service analytics more seamless with the user experience of the software they need for their work. It provides the ability for users to glean insights from the data held by an application faster and use that data to increase their efficiencies, engagement and value derived from both their software and its integrated BI tools.