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Top 4 Uses of Embedded Analytics

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Embedded analytics provide users and businesses with access to invaluable insight while eliminating the heavy costs of proprietary, insourced analytic software development.

Analytics are vital to the modern business, but the development of a truly comprehensive and interactive analytics interface from scratch can be very time-consuming and resource-exhaustive. That’s why many businesses are choosing embedded analytics (EA) for their workflows, operations and market-facing products. Analytics allow businesses and users to view and assess large and complex sets of data in order to process them in a useful manner. However, accurate, relevant and comprehensive data often require advanced data mining and processing that could be overwhelmingly cost/time-inefficient if they are totally insourced. Embedded analytics allow businesses to leverage the power of data while bypassing the heavy investment of time and resources it takes to develop proprietary analytics tools.


The richness and complexity of modern analytics necessitate special tools for seamless integration with other programs and platforms. Typically, an application programming interface (API) serves as the conduit through which analytics are embedded into third-party systems. This protocol essentially acts as a software-to-software interface (in contrast with a human user interface or a hardware device driver), which allows distinct software programs to interact with one another in a functional and productive manner.

When embedded into market-facing products (in any context, whether it is business to business, business to consumer or another model) analytics can serve as white label assets for proprietary platforms and original equipment manufacturers (OEMs). In other words, embedded analytics are often implemented into software programs that, in spite of third-party integration, maintain their exclusive branding. This empowers companies with enhanced business intelligence while also augmenting their brand with highly attractive integrated features.

The advent of embedded analytics illuminates the rise in demand for meaningful and dynamic business intelligence across a diverse array of industries and platforms, which is being fulfilled by a convergence of various data processing and representation tools. This convergence is in turn enabled by APIs, around which the integration of embedded analytics is centered. Through these systems, data is given a new clarity and context that leads to more informed decisions and more predictable outcomes.

Uses of Embedded Analytics

1. Data Visualization

Many people are visual learners, and patterns that occur within data are very often best conveyed through some form of graphic representation. Data visualization automatically creates graphic depictions of processed information, which can be presented in ways that clarify context, relationships and evolution. Data visualization makes large, complex and multi-nodal clusters of information tangible to the average user.

Data visualization relies on accurate and timely representations of relevant information sourced from large and complex data sets. Graphs, charts, symbols and other visual assets can appear simplified thanks to turnkey embedded analytics tools. The development of those sorts of systems in-house is more often than not a significant burden (in terms of cost, time, energy and resources) to business operations. With embedded analytics, businesses and users can generate meaningful and dynamic data visualization with significant ease.


2. Interactive Reports

Reports are often (if not always) most useful when they are customizable. The ability to refine different variables (demographics, sources, ranges, etc.) helps users easily compare data and ensure that they are getting the information that they need, in as specified and specialized format as possible. We live in an age of digital interactivity, and static, read-only content will no longer suffice when it comes to analytics.

Smart visualization systems in embedded analytics automatically adapt to the parameters that are set by users. Features such as zooming and filtering enable optimum relevance and specificity, and interactive functions allow for dynamic presentations and user experiences. Interactivity is an essential component of modern user experience (UX) design. But the development of interactive environments that are up to modern specifications involves a great deal of effort and resources, which is why white label solutions like embedded analytics are often preferred.

3. Mobile Business Intelligence

Business intelligence is comprised of data that helps companies understand and contextualize information in order to make informed business decisions, as well as execute them efficiently and beneficially. The concept of mobile business intelligence dates back as far as the early 1990s, when the mobile phone began to quickly evolve as a consumer item and spread throughout the marketplace. But it wasn’t until the advent of the smartphone that mobile business intelligence really began to gain traction, as the larger screens and improved processing finally met the complex needs of mobile reporting and presentation.

Embedded analytics simplify the distribution of business intelligence over mobile platforms. Interactive analytic reports and visualizations are particularly useful when they are adaptable to mobile devices and their environments. This requires a level of programming and design that exceeds simple business intelligence. Embedded analytics offer scalable and adaptable reporting and data visualization that are user friendly on any number of mobile devices.

4. User Engagement

User engagement is a key indicator of virtually any digital product’s value. User behavior is measured by the user’s interaction with a given application, which collects data in a manner that enables it to better serve them in future upgrades. Features are added (and subtracted) on an ongoing basis in order to increase engagement by adding value to user experience.

The potential functions of embedded analytics within software environments are multi-faceted. They help users make data-driven decisions, enabling them to spend time and effort more efficiently when interacting with software. Customer sentiment is also easily measured with embedded analytics, improving customer experience and optimizing business intelligence.


The phrase “data is the new oil” (which is often attributed to marketing analyst, Clive Humby) has been popularized as big data continues to shape global markets. That phrase has recently been modified to “data is the new soil” (by journalist and information designer, David McCandless), shifting emphasis on the idea that data should not be regarded as a finite resource, but instead as a foundation for growth.

Embedded analytics give users and businesses the tools to gain invaluable insight over time. EA providers empower OEMs through a variety of partnership programs, which often include useful onboarding services, such as live support, training and certification. In an age of perpetually increasing data commodification, embedded analytics provide streamlined and simplified methods of interpreting and presenting vital information to customers and businesses alike.


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Colyn Emery
Colyn Emery

Colyn is a writer and digital artist from Southern California. He writes about topics like AI, UX/UI, big data and blockchain technology. He has written articles, blogs, web copy and whitepapers for many different tech companies and organizations, and has worked in digital media professionally since 2007. He is a graduate of Chapman University and Art Center College of Design.