BI Trends

Maturity Model: 4 Stages of Embedded Analytics

By Alvin Wong
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In a recent survey of software and SaaS providers, they most strongly agreed that embedded analytics was a way for them to create a competitive differentiation in their product. At 65% of strong agreement, this was greater than the other strategic outcomes we asked about.

Differentiation can be looked at in a simple manner, particularly if you only look at it from a capabilities standpoint. If users require a specific capability (such as interactive dashboards or perhaps self-service data analysis), and you are the first to offer it in your industry, you will have a leg up.

In this post, we take a different perspective. We will explore how the embedding, or integration, of analytic capabilities into a business process application (e.g. CRM, ERP, EMR, finance) creates new ways of working, enhances the user experience, and ultimately leads to competitive differentiation.

Innovation Model of Embedded Technologies

First, let’s consider what it means to take two disparate, but complementary, technologies and put them together. The end result can be greater than the sum of their parts.

As an example, we can examine how the automobile industry is being impacted by the integration of GPS technology. The use of GPS has gone through 4 stages. (1) When standalone GPS devices first became commercially available, a driver could suction the device to their windshield, plug it in into the circular outlet, and benefit from the GPS navigation. (2) Not to be left behind, automobile manufacturers embed GPS navigation into the dashboard solving the non-driving user experience issues and charging more for the integrated interface. (3) To continue charging for premium products and services, we see incremental enhancements, such as incorporating traffic and weather data, as well as integrating the GPS with automotive functions, such as highlighting nearby gas stations when gas is low. (4) Taking a leap forward, by integrating GPS and a host of other technologies, manufacturers are testing self-driving cars today, whereby completely transforming the user experience and creating an entirely new category of products. Not only does this open up a transportation options to an entirely new group of users (the visually or physically impaired can now drive!), but this will disrupt existing means of travel such as taxis and buses.

Product implementations across the innovation model continue to be relevant to customers and to the industry, but it is clear that the self-driving car presents the most opportunity for differentiation, market disruption, and growth.

Embedded Analytics Maturity Model

The integration of automobiles and GPS is an analogy for software applications and analytics. The Embedded Analytics Maturity Model represents the four stages of increasing integration between a business process application and analytic capabilities. The first stage is the non-embedded analytics application, followed by three forms of embedding.


Standalone Analytics Application

In the beginning, we have a standalone analytic application. Where there is a separate product or service which generates data, the standalone analytic application allows the user to analyze and understand that data. In commercial applications, this model is often employed when the standalone analytics application is the only user interface offered. For example, consider a service such as Google Analytics where a central repository of data is fed by website interactions, and the site administrator logs into a web application to view the reports on web visitors.

Gateway to Analytics

In the first stage of embedding, the business process application integrates the security model with the analytics application. From the user perspective, they log into the business process application they use every day, and click on a link to go the analytics application, where single sign-on has been enabled. In this model, the access to analytics in embedded. In addition, the rights and roles configured in the process application are passed to the analytics application and controls the data the user has access to. This model is often employed with hybrid architectures, where the analytics application is hosted in the cloud and reports against data coming from one or more on-premise applications.

Inline Analytics

Inline analytics builds upon the first two models and adds integration at the UI or presentation tier. This places analytic content and capabilities inside the business application. Often analytics is available to the user in the reports or module of the application, or as a dashboard on the homepage. From an interaction standpoint, a user can usually click on a link from a visualization to an existing page in the application from a specific record; while there is a path from insight to action, this does still mean switching the user context to perform that action.

Infused Analytics

With infused analytics, the application is developed with analytics as a core capability and woven into the user workflows. Analytics is integrated at the application or business logic tier. Analytics is embedded within workflows to guide user action – knowing and doing is available in the same context instead of separate contexts. A user can start from a visualization and update information on the same screen through a backend process, calling an API, or performing a database write-back. We have often seen software applications who infuse analytics receive industry recognition for their use of analytics.


While we have laid out four different ways applications embed analytics, software providers should consider which is best for them, their customers, and for their market. In our survey, we saw that inline analytics being the most popular and infused analytics second.


We also found by combining the two survey results mentioned, we found that deeper integration is correlated with competitive differentiation. Of those who infuse analytics, 91% strongly agreed that embedded analytics is a competitive differentiator, compared to 65% for inline.


Embedding analytics deeper into software applications and integrating into the core workflows improves the user experience, reduces the friction between understanding the business and working in the application, and ultimately presents the best chance to differentiate the application in the marketplace.

Download the State of Embedded Analytics Report here.


Originally published November 6, 2013; updated on May 24th, 2021

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.