Embedded Analytics: All You Need To Know

Summary

This article defines embedded analytics and explains why it has become a critical capability for modern software products and internal business tools. It describes how embedding analytics changes the way users interact with data, outlines the value it delivers, and examines the different ways it can be implemented. The article is designed to help business leaders, product teams, and developers understand when and how to incorporate embedded analytics to improve decision-making and enhance user experiences.

What Is Embedded analytics?

Embedded analytics is the integration of data analysis and visualization directly into business applications or workflows, allowing users to access insights without leaving the tools they already use.

In many ways, embedded analytics builds on traditional business intelligence (BI), except it is integrated into the environment where the user actually works. This integration might be purely aesthetic or fused more deeply into the underlying foundations of the two tools. Often, end users do not even realize they are working with embedded analytics and instead experience it as a single, unified tool.

As Gartner’s definition highlights, embedding analytics into a business process gives the end user quick and easy access to data visualizations within their daily workflow. It is a step beyond classic business intelligence dashboards, where users often need to leave their primary application and log into a BI platform. The key takeaway here is that with embedded BI, the end user doesn’t need to switch between multiple applications (i.e., from their current workflow to a separate analytics tool).

Embedded analytics also allows software companies to obtain and fully integrate an analytics platform within their own SaaS software product (without the need for heavy investment in developing an in-house solution).

How Does Embedded Analytics Work?

Embedded analytics works by pulling data from sources, processing it through analytics engines, and displaying interactive dashboards, charts, or reports directly within the host application’s interface.

Embedded analytics can seamlessly match the look, feel, and branding of a company’s application (the one it is being integrated into). It then becomes an integral part of the application without anybody noticing that two pieces of software have been turned into one.

You can either embed out-of-the-box data visualizations available in the analytics platform or create a fully custom visualization if you need to meet specific requirements. Additionally, you can also use any third-party library and embed your desired solution.

Methods of Embedded Analytics

There are three main ways to embed data analytics:

• Iframe and HTML
• React SDK and API calls
• Web Components

These methods differ in how deeply they integrate data with the host application or web portal and in the level of flexibility they provide. Depending on the solution chosen, different skill levels are required. So consider the following information when deciding which type of embedded analytics is right for you:

The fastest and simplest method is basic embedding via iframe using an HTML snippet.

More advanced embedding techniques use React SDK and APIs. When embedding via React SDK, there are two options: use pre-built dashboards and visualizations, or create an entirely custom solution (programmatic embedding). Embedding pre-built dashboards and visualizations is a more advanced option than iframe. However, it remains less flexible than programmatic embedding, with the latter offering the developer the most flexibility.

Web Components is an alternative embedding method that falls between iframe and React SDK.  It is fast to implement and offers deeper integration than iframe (but less so than SDK).

Below is an example of an embedded dashboard and how it can adapt to the given context.

If embedding is not the right option, analytics can still align with your branding and design by being delivered as a standalone white-labeled solution. With a white label approach, the analytics solution can be fully rebranded to appear as a natural part of the host application. White-labeled analytics ensures that dashboards and reports carry the company’s branding, colors, and design standards. This approach is especially useful when you want to deliver analytics as a seamless part of your product while still relying on the vendor’s platform in the background.

Understanding Advanced Embedded Analytics

In cases where more specialist customizations are required for the embedded solution, advanced embedded analytics — known as programmatic embedding via React SDK — is the right option.

Advanced embedding via React SDK enables the creation of a completely custom visualization. The developer can code both the look and the behavior of the visualization. Advanced embedded analytics ensures deeper custom-made integration of the application and embedded visualization, excellent performance, and seamless interactivity.

Note: This advanced method of embedded analytics should not be confused with advanced analytics. The embedded element ensures that insights are delivered directly within the workflow, while advanced analytics adds further depth through techniques such as forecasting, statistical modeling, and machine learning. Modern platforms should therefore support programmatic embedding as well as advanced analytics, enabling organizations to deliver not only interactive dashboards and reports but also forward-looking insights that guide smarter and more strategic decisions.

Why Use Embedded Analytics?

A company should use embedded analytics because it seamlessly integrates data insights into everyday applications, enabling faster, smarter, and more scalable decision-making.

There are several key benefits that make embedded analytics so valuable:

• Data-driven decision-making: Bringing embedded analytics to a business process gives the end-user quick and easy access to data visualizations within their daily workflow (without switching between multiple applications). End users can take immediate action based on their insights and get more value from company data.
• Flexible theming: Embedded analytics can be customized and branded so that the analytics adapt seamlessly to the context of the application.
• Reduced time: Companies can fully integrate data analytics within their application or web portal; there is no need to invest time and money into developing an in-house analytics solution.
• Flexible company growth: Embedded analytics allows the scalable reporting of data to thousands of separate user groups.

Key Features of an Embedded Analytics Platform

A good embedded analytics tool should provide the following key features:

Engaging, self-service visualizations

End users should be able to create compelling, interactive visualizations via a drag-and-drop interface without the need for a data specialist.

Real-time analytics

An effective embedded analytics tool should provide access to insights based on live data. With real-time analytics, dashboards and reports update automatically as new information becomes available, reducing delays between data capture and decision-making.

UI customization toolkits

There ought to be options to customize the look and feel of the visualizations and dashboards, and adapt them to the whole user interface.

AI-accelerated dashboards and visuals

When AI is implemented within the embedded analytics platform, business users and analytics engineers are able to work in one place. They can, for example, explore and interact with the data via an AI chatbot, automate its behavior, and adjust the code without the need to switch to any other platform.

Automation of workflows and processes

A strong embedded analytics tool should include built-in automation to reduce repetitive tasks and speed up decision-making. Automation can handle user provisioning, data refreshes, alerts, and report distribution without requiring manual intervention. This not only improves efficiency for technical teams but also ensures that business users receive accurate, up-to-date insights.

Automated scaling and agile change management

You should be able to fully automate the separation and control of multiple user groups from different teams, locations, or companies. Platforms usually provide automated user provisioning tools, and the infrastructure secures the physical data isolation of one user/user group from another. It is impossible for users to get access to data to which they have no access rights.

SSO (single sign-on)

Enables users to access embedded visualizations located within another application using only one login. It's not necessary to log in separately to see the data, as the data becomes part of one application.

Life cycle management

Ensures the alignment of any changes or new features to be rolled out to the analytics solution integrated into the SaaS product or application. When you roll out a new version or update, life cycle management allows these changes to be propagated to all clients using your product (as well as the embedded analytics integrated within it).

Security and compliance

A reliable embedded analytics tool must provide strong security features to protect sensitive data and ensure compliance with industry standards. In this context, security means more than just access controls. It includes data encryption, role-based permissions, secure API connections, and compliance with regulations such as GDPR or HIPAA.

What Is the Difference Between Embedded Analytics, Business Intelligence, and Embedded BI?

Business intelligence is a process that involves people, systems, and tools collecting and preparing data for analysis to support data-driven decision-making. With many BI tools, the data is often missed or underused as end users need to log into a separate platform to access it. With embedded analytics, data is easily accessible within the company's software, and end users are more likely to use it on a daily basis. Embedded BI is business intelligence in the form of dashboards and reports, integrated directly into the user's application. The term is often used interchangeably with embedded analytics and, as such, can be considered a synonym.

Embedded Analytics Use Cases/Who Is It For?

Embedded analytics can be applied across a wide range of use cases because it adapts to the context of the host application. Its flexibility makes it valuable for both technical and non-technical users, with the exact scope depending on the capabilities of the chosen solution.

Embedded analytics is especially relevant for (but not limited to) the following industries:

Software companies: Embedded analytics helps SaaS providers deliver more value by providing customers with real-time insights directly within their applications. This strengthens product adoption, improves user experience, and creates opportunities for monetization. For a real-world case study, check out how BlackHyve simplified construction scheduling with advanced analytics. They used React SDK to embed analytics deeply into their platform, automating data flows, and delivering project-specific dashboards within just eight weeks.

E-commerce: Online retailers use embedded analytics to monitor customer behavior, improve product placement, and track performance within the platform.

Financial services: Banks and fintechs rely on embedded analytics to track performance, manage risk, and deliver client-facing dashboards that support compliance and real-time decision making. For a practical use case, check out how Mercatus embedded analytics to enable asset managers to access real-time insights in its software.

Insurance: Embedded analytics supports insurance providers in claims processing, fraud detection, and customer service by integrating insights directly into their core systems.

Healthcare and medtech: Embedded analytics supports clinicians, administrators, and medical technology providers by placing critical insights such as patient monitoring, data trends, and operational metrics directly into their workflows. For a useful healthcare case study, check out how Medicare provides embedded dashboards that allow healthcare and life sciences organizations to make better decisions.

Manufacturing: Manufacturers increasingly rely on embedded analytics to manage operations and optimize the supply chain. By embedding analytics into manufacturing systems, companies gain visibility into production performance, demand forecasting, inventory management, and logistics.

Energy and utilities: In the energy sector, embedded analytics plays a key role in grid management, demand forecasting, and predictive maintenance. For utilities, embedding analytics into operations platforms enables better monitoring of consumption patterns and improves efficiency in energy distribution.

Choosing the Right Embedded Analytics Solution

While different use cases will require different solutions, the right embedded analytics platform should provide:

1. Self-service capabilities to support engaging, interactive data visualizations.
2. An intuitive UI that caters to the needs of both technical and non-technical users with, for example, API support for the former, and drag-and-drop reporting for the latter.

An effective embedded analytics solution must integrate securely, efficiently, and without disruption, while maintaining cost control and reliability. Beyond integration, it should offer advanced analytical and forecasting capabilities, enabling organizations to extract deeper insights from their data. To ensure scalability, the platform should be able to support hundreds or thousands of distinct user groups, each with specialized requirements.

For a comprehensive overview of the market landscape, refer to our detailed comparison of leading embedding platforms.

How To Embed Analytics Into Your Application?

How do you actually embed analytics? That depends on the embedding method you choose and your technical skill level. The level of expertise you have will determine whether you work with more or less code, which in turn affects both the final outcome and how seamlessly the solution integrates.

As previously mentioned, there are several methods for how to embed analytics. Each has its own benefits and fits a different use case.

The easiest way to embed is via iframe. This method is suitable for non-technical users, but doesn't offer much flexibility. A more complex approach, though still without requiring much code, is Web Components. The most flexible option is React SDK — although it comes with the requirement of advanced technical skills. You can find out more about embedding methods, examples, and use cases in our webinar How to embed analytics: Methods and examples.

In most cases, you embed dashboards and visualizations pre-built in the analytics platform. Depending on the method, you use a code provided by the analytics vendor, adjust the code, and embed analytics into your application. React SDK is the only method offering an option to embed a completely custom solution (while both the look and the behavior are defined by the analytics engineer/developer). To embed analytics into your application software, you don't need to sign into the analytics platform as the entire process is done via code.

6 Requirements of Engineers for an Embedded Analytics Tool

There are a few questions every engineer will ask about a prospective embedded BI tool:

• What is the level of integration and flexibility of the embedding solution? (The level required will depend on your needs and expectations).
• What are the customization options? Embedded dashboards and visuals need to be integrated into a software product so seamlessly that the end user can't tell the difference between the two pieces of software.
• What is the deployment process? Only some analytics platforms ensure quick deployment.
• Is the solution self-service? The more flexible and self-service-aligned the solution, the more efficient the embedded analytics will be, enabling ease of access for non-technical users.
• What are the scaling options of the analytical platform? Choose a platform that enables easy scaling in terms of data volume, price, and the number of users. Multitenancy tends to be the best way to achieve this.
• Does the embedded solution come with any AI-fueled processes? Embedded analytics tools that provide AI features can save significant time for the entire team, both when developing the solution and analyzing and drawing answers from the data.

AI and Embedded Analytics

Artificial intelligence (AI) has become an inevitable part of data analytics. It enhances embedded BI solutions by enabling process automation and advanced analytics (such as forecasting and predictive analytics).

Instead of just asking “what happened”, you can now also ask “why it happened”. AI understands the context and provides you with the information you're looking for. Based on facts and a proper understanding of your data, you can easily predict “what will happen” in the future.  So, with AI-accelerated embedded analytics, you gain clear, advanced insights that are directly available within your application.

Forecasting in an embedded analytics solution

How Much Does Embedded Analytics Cost?

The cost of embedded analytics varies depending on the pricing model offered by the vendor and the needs of your business. There are several pricing strategies commonly used, and it’s important to consider which one best aligns with your company’s approach. These are the most common:

• Pricing per user: The platform charges per the number of active users.  It is a straightforward option as money is not wasted on inactive users. This solution is ideal for internal teams, where it’s clear how many users you need to provide access to. However, for B2B companies, it is hard to estimate how many users will be involved, and in turn, how expensive the embedded analytics is going to be.
• Pricing per query: The company is charged for every query used to display an analytical insight or explore information in a self-service tool. Although it may appear flexible, this is a highly unpredictable option.
• Pricing per workspace: The company is charged for every workspace and one workspace can be used by an unlimited number of users. This option is highly predictable; pricing can be simply calculated by counting the number of workspaces and additional needs.

Key Questions To Ask Embedded Analytics Providers

Be sure to compile a list of embedded analytics key questions before choosing your embedded analytics solution. Questions you should ask to better understand the embedding tool's key features include:

• What options do I have to embed graphs, charts, and dashboards with your analytics platform?
• Can non-technical users easily create and edit dashboards and visualizations?
• What deployment size can your analytics solution support? How does scaling to more users affect cost and performance?
• Which data sources are supported by your embedded analytics solution?
• What are the security standards supported by your embedded analytics platform?

How To Monetize Your Data With Embedded Analytics?

You can monetize your data with embedded analytics by packaging and selling insights directly to clients and partners. Embedded analytics offers companies the chance to do this in a number of different ways. For example, imagine that you collect data about consumer behavior related to your e-commerce website or marketplace (where they go, what they save as desired items, what they buy, and so on). All of this information can be shared with clients and retail brands via dashboards and visualizations embedded into your client portal. The data offered can be divided into tiers, with one tier provided for free (e.g., basic data insights) and another provided for an additional fee (e.g., advanced, more detailed data insights).

As clients realize how valuable the data is in helping them to boost their operations and strategy, they will likely start requesting more detailed reports — and thus be willing to pay an additional fee. By creating ‘free and paid tiers within your data offering as described, you can tap into a new revenue stream and gain measurable ROI on your embedded analytics investment.

Want To Get Started with Embedded Analytics?

Planning to embed analytics into an app or portal? See first-hand what GoodData's embedded analytics platform can do with a free trial or personalized demo.

Want To Know More About Embedded Analytics?

To learn more about embedding data analytics and BI, continue reading via the links below.

DashboardView: Advanced Embedded Analytics

Embedded Analytics: An Alternative to Power BI

Best Practices for Building a Data Product With Embedded Analytics

The Future of Embedded Analytics

Why Embeddable AI Is the Future of Modern Enterprise Analytics

Starter Guide: Embedded Analytics in Your Software Product

Tech Guide: Your Go-to Guide to Launching Branded & Embedded Analytics

Headless BI + Embedded Analytics: Differences and Purposes

Understanding the ROI of Embedded Analytics

Embedded Analytics FAQs