What is Embedded Analytics?

A guide to the powerful ways embedded analytics can boost your external and internal apps.

Cover of the 'What is Embedded Analytics?' blog post

We’ve previously discussed the modern data stack for embedded analytics—but what is embedded analytics? Let’s take a step back and review this popular category of data application.

Embedded Analytics Definition

Embedded analytics is the integration of data analytics and visualization capabilities within a user’s natural workflow—within internal tooling, web portals, and products like apps.

Such an integration enables users to conduct analytics directly inside the applications they use rather than having to switch between various applications to get insights.

Let’s illustrate with an example: a seller on an e-commerce platform, “TrapezoidSpace,” would like insights about their revenue.

Without embedded analytics on the e-commerce platform, the seller would have to manually request a sales report from the e-commerce platform, import it into secondary analytics software, then work with the data to get the necessary insights.

However, with embedded analytics, they could get the insights they need with a real-time dashboard visualizing revenue presented to them directly within the e-commerce platform.

Embedded Analytics Use Cases

There are many use cases for embedded analytics. We can divide them by the end users and the types of information they present.

External vs. Internal Embedded Analytics

Embedded analytics can be internal-facing—meaning the end user is within an organization—or external-facing, meaning the end user is an organization’s customer.

Take our previous example of TrapezoidSpace: here, the seller is an external end-user of the platform’s embedded analytics features.

In contrast, let’s take the example of TrapezoidSpace’s customer success manager. They track the time-to-resolution of tickets (TTR) using a bespoke ticket management system that visualizes trends in TTR. That would be an example of internal-facing embedded analytics.

Types of Embedded Analytics

People use embedded analytics in many ways, but real-time reports and dashboards are the most common.

Real-time static and interactive reports are tabular views of information with the ability to refine data by setting parameters and scheduling. In contrast, dashboards and data visualizations are charts and graphs that present and visualize metrics to make finding insights and trends easier.

Embedded Analytics Benefits

Generally, embedded analytics contributes to a user-centric UX that adds value and drives adoption.

Whether users are external or internal, the point of an investment in any tooling is to bring value and thereby drive the tool’s adoption. And, providing a user-centric design and seamless UX tailored to the target user is one of the only ways to get people to (willingly) use products.

So, conveniently locating BI and analytics functionality within the user’s workflow significantly enhances a product’s UX. Improved UX promotes the more effective use of the tool, creates greater value, and increases the tool’s adoption.

Externally, embedded analytics increases revenue and competitive advantage.

In a survey of project managers, developers, and executives, 96% reported that embedded analytics contributes to revenue growth. They credited this to better UX and their ability to add revenue streams based on highly sought-after functionalities.

Adding high-value capabilities to products allowed businesses to introduce tiered or subscription-based analytics services—opening the door to acquiring new customers and upselling to recurring ones.

Internally, embedded analytics increases productivity and the accessibility of data-driven decision-making.

The efficiency with which users can conduct analysis with embedded analytics significantly boosts time effectiveness (and, therefore, cost-effectiveness). With auto-generated dashboards and filtering capabilities, users can draw insights faster and avoid the cumbersome processes of manually pulling data and conducting analysis in separate applications.

Easy access to analytics also allows those with less experience in data analysis to make data-driven decisions. In a 2022 survey, 92% of companies cited their biggest obstacle in becoming ‘data-driven’ as people, processes, and change management. However, a custom-built internal embedded analytics platform—one that is tailored to the team’s workflow—doesn’t require complex training in generic BI tools.

Rather than having to facilitate learning of generic BI tools which aren’t set in its work’s context, an organization can create tools that are already infused with the working context. And so, instrumenting intuitively tailored tools powered by embedded analytics removes training bottlenecks and dependencies—increasing productivity and value-add.

Embedded Analytics vs. Business Intelligence

The difference between embedded analytics and traditional business intelligence is simple. While traditional BI means you conduct your analytics in a separate set of tools, embedded analytics allows users to access insights in-app. Their data visualizations, dashboards, and reports live in the application in which they conduct operations, allowing for real-time refreshes and faster learning.

This contrast may seem somewhat minute, but it actually saves employees a lot of time—up to 5 hours a week. These savings increase productivity, allow for constantly updated information, and streamline the entire analytics workflow.

embedded analytics MDS

How Cube can help you power your Embedded Analytics

Cube is a headless business intelligence tool that accesses data from raw data sources, models the data, caches query results, and makes the data available to downstream data applications via REST, GraphQL, and SQL APIs.

“Headlessness” refers to tooling that separates its functionalities from the end user’s interface—making it the perfect basis for building branded and customizable embedded analytics for any application.

Cube is unique in that it provides a centralized, upstream semantic layer to define metrics, making all of the analytics it powers consistent and accurate. This semantic layer and its caching capabilities make sure that an application is performant and cost-effectively provides correct, up-to-date information to all of the users who access it.

Similarly, Cube’s centralized data access control supports multi-tenancy and allows role-based and column-based governance, which ensures users are only accessing data appropriate to them. This type of security is especially crucial in external-facing embedded analytics, where many separate entities—for example, each seller on TrapezoidSpace—need to access their data and their data only.

Cube enables developers to build consistent, secure, and performant embedded analytics efficiently; dashboards in hours, not weeks.

It can be the foundation for building embedded analytics applications with its compatibility with many popular front-end tools and business applications. So many, in fact, that we built a wiki about our favorite tools in the modern data stack—including tools for building embedded analytics (aptly named “awesome.cube.dev”).

Learn more

Want to learn more about how to build embedded analytics applications with Cube or headless BI?

Get in touch with us and the rest of the Cube community on Slack or Github.

In the mood for some hands-on help with building a modern embedded analytics data stack? Schedule a time to chat with us 1:1.

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