What is Embedded Analytics?

The definition of embedded analytics

What is embedded analytics? What does embedded analytics mean?

Embedded analytics is the integration of analytical capabilities and data visualizations into another software application.

Also called embedded business intelligence (BI) or embedded BI, this is a modern type of data analytics solution that merges with another software application, rather than acting as its own standalone tool. Traditional analytics solutions, in comparison, require switching to another app.

The integration, or embedding, of embedded analytics tools into business software is typically seamless and highly customizable, allowing businesses to easily blend sophisticated analytics features with their brand’s aesthetics, as well as the existing user experience of their software, whether it is a customer relationship management (CRM), financial, HR or accounting software, e-Commerce platform, enterprise resource planning (ERP) solution, and so on. Learn the basics of embedded analytics in our video, below:

What is the purpose of embedded analytics?

The purpose of embedded analytics is best summarized in Gartner’s definition of embedded analytics. According to Gartner embedded analytics allows data analysis to be actioned within a user’s workflow as they engage with your core software.

With embedded analytics, they do not need to switch to a separate app to access their data or to share and consume insights; everything is in one place, delivering more value to them, while enhancing the capability and value your software provides significantly.

For end-users (which includes customers, internal team members, C-suite, etc), the integration of embedded analytics means they can use dashboards, data visualization, and AI-powered analysis within the same application, without necessarily knowing they are using two different solutions. Software owners can leverage this seamless model of introducing a new analytics solution to deliver a more cohesive user experience. 

When an analytics solution that is embedded into a software application is unrecognizable to the end user as a separate piece of software, this is called white-labelling or white label analytics, where the analytics platform is completely rebranded to blend in with the look and feel of the rest of the application into which it is embedded. Alternatively, the analytics platform may be grey-labelled - which means the platform provider’s brand name will remain. For example, it may say ‘powered by Yellowfin’ in the footer of the analytics module.

Who is embedded analytics for?

Remember the build vs buy analytics debate? 

Embedded analytics effectively enables enterprise organizations and independent software vendors (ISVs) to bring a best-in-class analytics platform into their software-as-a-service (SaaS) or specialized internal work software without having to invest in developing a solution in-house, which is both time-consuming and expensive without the proper technical expertise. It also means businesses can focus on core processes, rather than divert resources and attention away to building and maintaining an analytics module.

For product owners in particular, embedded analytics also offers added value to the user experience (UX) by integrating interactive features that make data less complex and more available, ultimately helping create a stickier end product. Enterprise organizations can also benefit from embedded analytics by providing users of their core work software the capability to access data to find actionable insights while they work in the app as normal.

Examples of Yellowfin embedded analytics software and data visualizations on dashboards, integrated within an existing e-Commerce platform.

For the end user, embedding real-time reports and dashboards allows them to analyze the data held within the software application into which the analytics platform is embedded. With this analysis, the end user can better track operational performance metrics and key performance indicators (KPI), identify and mitigate issues, and spot opportunities to maximize and optimize business outcomes by using the results (insights) for data-led decision-making.

For enterprises and software vendors, embeddable analytics effectively makes data analysis a deeper part of the user’s everyday workflow.

How embedded analytics works?

As previously mentioned, embedded analytics integrates several analytical features directly into an existing software application for the purpose of merging data analysis processes with the regular user experience and workflow. This approach to analytics adoption also helps foster a data-driven culture among a company’s end-users (customers, internal staff, etc). 

As an embedded analytics example, Yellowfin BI’s customer Kodak has embedded analytics into their printing press software as a white-labelled BI solution. Kodak’s customers purchase the Prinergy software to help them centrally manage their digital printing systems, and access self service analytics reporting and dashboard tools directly in their application, without their clients necessarily knowing these analytical tools are provided by Yellowfin, a separate solution.

The analytics embedded into Prinergy allows the customer to monitor, for example, their ink usage, the number of print passes, and their production trends and volumes using the one application. This will enable them to more accurately forecast resource needs and help them increase the efficiency of their ink usage without having to use a separate analytics platform.

Another Yellowfin BI customer, Case IQ, used Yellowfin embedded analytics software to move away from their investigative SaaS platform’s legacy analytics, which was becoming too complex to keep updated for their clients’ growing data needs, and instead upgrade to best-in-class analytical capability with Yellowfin dashboards and data visualization embedded directly into their existing application’s user experience via white labeling.

An example of Case IQ's embedded analytics module, provided by Yellowfin BI.

The embedded BI model and Yellowfin’s extensive customization allowed Case IQ to quickly make data analysis and actionable insights a natural part of both their internal staff and end-users’ everyday workflows. This seamless integration was ultimately the selling point for Case IQ to move ahead with Yellowfin, after finding Tableau embedded analytics not as flexible.

Embedded analytics is effectively a window for the end user into the data that is held within a particular software application.

What are the methods of embedded analytics?

It’s clear that embedded analytics provides a valuable way to integrate powerful and engaging analytics features within existing software products or enterprise applications. However, building the best analytics experience for your SaaS product or enterprise work application requires the right business intelligence solution, partner and expertise to ensure a smooth experience. 

Depending on the exact embedded analytics platform and BI vendor selected, there are a number of ways to embed analytics into your app to meet specific use cases and data requirements, each with their own level of customization, flexibility and extensibility in regards to the depth of integration. Before continuing, we recommend reading our essential embedded analytics checklist.

The most important part of integrating analytics into your application is ensuring the components you embed are consistent in the look and feel of the rest of your application. As we mentioned earlier, you can white label analytics into your software (making it indistinguishable from your existing experience), grey-label (brand it as your own without hiding that the analytics is from a third-party vendor) or you maintain the branding of the embedded analytics partner. Whatever your choice, you have a number of options to suit your preference (and current technical expertise or developer count), including:

1. Tight integration

Also called an application integration, this approach means you embed the entire analytical application into your software, providing your end-users with direct access to some or all of the suite of business intelligence tools provided by the embedded analytics platform you chose. 

For example, in the case of choosing Yellowfin as your embedded analytics platform, tight integration affords access to its complete suite, allows you to achieve Single Sign-On (SSO), and access to some interactivity that can only be accessed through tight integration, such as Yellowfin’s self service BI and ad-hoc report creation, and collaboration features. 

Tight integration is achieved either via a number of methods, each aligning with certain use cases: 

• HTML and iFrames, which embeds the solution within your own portal or native application inside an iFrame using HTML snippets - the fastest way to integrate 
• Redirection, which routes end-users directly into the embedded analytics solution you opted to embed
• Web components, which offer more control over the integration
• APIs and SDKs, which allow developers to build highly customized analytics experiences based on more advanced or specific requirements.
  
  

Consistency with the existing software application can be maintained by re-styling the user interface, colors, and headers using the embedded analytics software’s design options, though the extent of how much you can re-style varies between BI vendors. Yellowfin’s extensive design and customization options can help make the look and feel of the embedded BI suite exactly the way you want it. Below is an example of an embedded analytics dashboard in a software application using Yellowfin’s tools, to give you an idea of the level of customization you have to make the integration of analytics look the way you want it.

Data connectors add further ability to quickly connect your various data sources across multiple on-premise servers or SaaS applications as part of your embedded analytics project. The level of connectors vary between BI vendors. See our list of supported data connectors for Yellowfin.

2. Loose integration

Also called content integration, this method allows for embedding certain analytic content, such as dashboards and reports, from the embedded analytics platform to display in the software application. This is typically done to allow end-users to explore or consume analytic content, rather than create, and can range from interactive dashboards to standalone charts and graphs.

For example, using Yellowfin BI, loose integration can be achieved through JavaScript API, which allows reports and dashboards to be embedded into external web pages, which can be hosted on separate servers to Yellowfin. End-users can be authenticated and access the content by inserting a scriptlet into the HTML code, and JavaScript API allows the configuration of controlling what content is displayed (hiding or showing certain UI elements, metrics, etc) and other features like pre-filling filters and values, allowing drill downs, and more. 

3. No integration

There is ultimately the option of not integrating the analytics solution into the software application, meaning end-users will still access the embedded analytics suite in a separate instance or window. This decision could be made due to an integration not being possible, or if the value end-users derive will be higher if the analytics is accessed as a standalone tool. 

For a more detailed guide on the methods, please visit the Yellowfin Evaluation Guide on Embedding Analytics. For a visual-based guide, please watch our Yellowfin Embedded Analytics Walkthrough for Developers video, embedded below.

Why use embedded analytics?

There are many reasons why enterprise and smaller software vendor businesses should use embedded analytics instead of continuing to offer data analytics as a separately accessed tool.

Embedded analytics is the fastest way to integrate powerful data visualization, reporting, and business intelligence dashboards directly into existing software applications, and empower users to access real-time insights without switching platforms or application windows. This seamless integration enhances decision-making, productivity, and user experience by allowing more business people to analyze and act on data within their daily workflows, and make data analysis more of a natural everyday business process rather than something that is seen as complex or specialized, or that has to be switched to use properly (and potentially never actioned, leading to waste of your newly invested BI tool). 

An example of a Yellowfin BI embedded analytics dashboard used in the healthcare industry.

Industries such as finance, healthcare, and eCommerce benefit greatly from embedded analytics, as it streamlines analysis processes and provides quick access to data in real-time  that is traditionally locked away in other applications or contexts. Additionally, embedded analytics tools help in enhancing user engagement with interactive dashboards, AI-powered insights, and customizable reporting options. For software vendors (ISVs), embedding analytics into their applications can drive further product differentiation, customer retention, and revenue growth.

For a full and comprehensive step-by-step guide on what embedded analytics is, how to embed analytics into your enterprise application or software product, and why it is so important today, we recommend downloading our free whitepaper, The Ultimate Guide to Embedded Analytics.

Business intelligence vs embedded analytics vs embedded BI: What’s the difference?

Embedded analytics is a term often used in the context of business intelligence. While both are highly related, there are some fundamental differences to clarify.

The key difference between them is that business intelligence describes the standalone processes that a company uses to collect data and analyze it for the purposes of improving business-related decision making, which can include a number of different tools that are made available by a BI solution. Business intelligence and its effectiveness can wildly vary depending on the BI vendor you partner with, but oftentimes traditional BI tools need to be accessed separately from core work applications.

Embedded analytics is a model of data analytics that merges a specific BI solution and its tools directly into another application, with the aim to provide deeper context and better merging of BI processes into the core work application, leading to more informed and actionable decisions. Meanwhile, embedded BI is another term for embedded analytics and is not a different model.

Read more: Business Intelligence Platforms vs Embedded Analytics Software: What’s the Difference?

What are the key features of embedded analytics?

There are several key components that make embedded analytics unique and different from traditional, legacy BI tools, a few of which we will break down in the next section:

Streamlined user experience

The main feature and appeal of embedded analytics is the seamless integration of analytical features within a software application’s existing user experience. It effectively and efficiently brings powerful and engaging data analytics capability into the same user interface end-users are familiar with, and can be continuously customized, managed and refined to fit changing requirements. Best of all, it makes the process of data analysis directly into users’ workflows. 

Cloud-friendly and scalable

Embedded analytics integrates seamlessly with cloud-based applications, ensuring real-time data access, flexibility, and reduced infrastructure costs that scale with current data volumes and user requirements without compromising on performance. Certain vendors, like Yellowfin, offer cloud BI solutions that allow businesses to deploy embedded analytics using both the public cloud (AWS, Azure, Google) and private cloud, as well as physical (on-premises or virtual servers) or any OS that supports a full Java Virtual Machine (JVM), as well as supports multitenancy.

Developer-friendly and secure

With APIs, SDKs, and low-code tools, developers can easily integrate and customize analytics within applications. Built-in security features like role-based access, encryption, and compliance ensure data protection and regulatory adherence can easily be managed, ensuring complete control over how the integration is deployed, and what analytic content end-users can access.

Read our guide for more detailed information on the top embedded analytics features in 2025.

What are the use cases of embedded analytics?

Embedded analytics is widely used across several industries to enhance data accessibility, decision-making processes and the user experience of software applications. 

For general business, deploying embedded analytics in a SaaS application provides real-time data updates and insights, enabling more business people to track, explore, share and analyze important data and metrics without switching platforms (such as embedding a general KPI dashboard into the main user experience of the SaaS application).

A sales KPI dashboard example, embedded into an existing work application via Yellowfin.

In healthcare, embedded analytics helps staff better track patient outcomes and optimize resource allocation as dashboards and reports are directly integrated into the work software they use for ward rounds (see our customer case study with North Tees and Hartlepool NHS for more examples), while in financial services, embedded analytics can bring the processes of  fraud detection, risk assessment, and compliance monitoring directly into one platform rather than several. 

By integrating analytics directly into workflows, businesses across all sectors can ultimately drive greater efficiency, improve decision-making, and gain a competitive edge. For more information, refer to our free guide on use cases for embedded analytics SaaS.

5 benefits of embedded analytics in your software application

Embedding analytics into your enterprise software application can bring huge benefits to your product. From increasing user engagement to providing new revenue channels to decreasing client churn, embedding analytics provides you with business opportunities. Here are five benefits of embedded analytics that you’ll reap as a software OEM or enterprise company from embedding analytics into your app or work platform.

1. Sustainable competitive advantage over your peers

If you don’t have modern analytics embedded within your application that serves up actionable insights, you are falling behind your competition. Clients are coming to expect analytics on everything. More data is being produced than ever before and organizations want to capitalize on the wealth of insights locked in the software applications they purchase through analytics.

By embedding the right modern analytics platform, you enable your end users to leverage new technology to get to the heart of data - insights - fast. When you choose to embed an analytics platform that focuses on innovation, like Yellowfin, you will always have the latest technological advances, such as automated business monitoring (Yellowfin Signals) or automated analysis (Yellowfin Assisted Insights) available to you and your customer base. This gives you a sustainable advantage over your competitors.

2. The ability to create an amazing analytical experience for your customers

When you embed a modern business intelligence platform, you are able to deliver new capabilities that will transform how your customers use your application. They will be able to code their own capabilities into their dashboards, for example, or get instant, automated insights without the manual graft of data discovery. They can collaborate over data and share insights in a governed platform. Modern embedded analytics also offers an enhanced user experience with improved UI and flexible design options.

3. Additional revenue streams via upsell opportunities

Because of the huge value brought to your customers through modern embedded analytics, you will gain the opportunity to add revenue streams and upsell them. The analytics module itself could (and should) become an additional income stream. Or, you could, for instance, include dashboards and embedded reports as standard in your application and reserve automated insights as an extra that your sales team can upsell to clients. A similar thing could be done with data storytelling capabilities like Yellowfin Stories and Present.

4. The ability to get to market fast

If you build your own analytics capabilities, you will inevitably run into build and deployment issues, set-backs, and delays. The longer it is before you get to market, the longer it is before you generate revenue from analytics. But with the right embedded analytics partner (note: you’ll be in business partnership with your analytics provider as you’re embedding their software into yours, so choose wisely), you will be able to get to market fast. 

For example, at Yellowfin, we offer the Quick Start package that walks you through how to do all the integration and following best practices with dashboard design and reports in the Yellowfin platform. In addition, we will walk you through how to market your application and prepare your sales team for selling the new benefits that come from embedding analytics into your application.

Read more: 3 Ways Embedded Analytics Improves Speed to Value

5. Allows you to focus your resources on uplifting your core product

Embedding business intelligence and analytics rather than building it yourself will also free up your developer resources to focus on your core offering. Analytics isn’t your endgame.

Because analytics is our expertise, we are constantly improving Yellowfin, adding new features, and integrating the latest technologies so you and your clients get an ever-better product and experience. That’s just not something you could dedicate resources to without it being detrimental to your core product.

Read more: How Embedded Analytics Drives Product-Led Growth

What to prepare before embedding analytics

There's a considerable amount of benefits to embedding analytics into business applications, but ensuring it realizes value for your users comes with additional planning.

Determine your software's maturity for analytics

An exceptional embedded analytics offering is underpinned by the right framework, strategy and vision, which necessitates the need to recognize and address where your application may need to improve its BI maturity level. That's what the Embedded Analytics Maturity Curve is for.

Build or buy embedded BI

The first debate in analytics adoption journeys, but not the only one - here are 8 key considerations for choosing an embedded analytics solution.

Recognizing the signs when it's time to upgrade

If your product’s analytics can’t keep up with what your users want today and tomorrow, they will look outside of your product to fulfil their BI needs. This is why it's time to update your embedded analytics.

Common capabilities of embedded analytics platforms

There are some things that are standard in almost any platform, regardless of how good the platform is. But the leading platforms will deliver more, like automated business monitoring (ABM)  self service analytics, and AI-powered capabilities. You should check out the five key things you need to know before buying analytics  but for the basics, here’s a list of common features in embedded analytics platform:

Data preparation

For any analytic project to be successful, organizations need their data to be ready for analysis. Data preparation capabilities provide what you need to connect and extract data from multiple data sources while providing easy access to data outputs so end users consume accurate data. In embedded analytics scenarios, this capability is usually used by the software provider to ensure their customers have the most accurate, clean data to work with. 

Dashboarding

A business intelligence dashboard (or BI dashboard) is the best way to visualize multiple data reports in one place to help drive action. Dashboards allow you to group multiple reports on the same topic so your end clients can gain an overview of the business’ performance. Modern business intelligence dashboard software is even enabling developers to code actions, such as order buttons, into dashboards so users never have to leave the dashboard to complete their workflow. Embedded analytics platforms should also enable the end user to easily build dashboards themselves and customize the look and feel of the dashboard to match their brand.

Data discovery

To better understand their business, your customers need to explore their data, discover patterns and outliers, and share their insights with others. Data discovery capabilities within embedded analytics enable your clients to uncover insights in the data held within the application and let everyone share and disseminate the insights in a governed way. The rise of augmented analytics has brought automation into data discovery. Machine learning algorithms can continuously search for patterns in the data and automatically alert relevant users when there is a statistically significant change.

Interactive reports

Reports should be easy to build and interactive. Embedded analytics end users may want to build their own reports, but even if they don’t, they will want to drill into further detail, visualize the data in tables and charts, query the data, and compare the data with other data points. Machine learning has enabled more powerful interactive capabilities such as the ability to click on a data point and have it automatically explained through related data and natural language explanation points (in Yellowfin, this is an exclusive feature called Assisted Insights). This gives greater clarity on data faster, so action can be taken sooner.

Mobile reporting

Being able to provide your customers with their dashboards and reports on mobile devices is critical in some industries. For example, being able to see machine performance, downtime, and production rates from anywhere on the factory floor via a tablet can be invaluable. In addition, being able to receive automated data discovery alerts to a mobile can be the difference between success and failure in highly reactive, fast-paced industries.

Collaboration

Governed collaboration over data insights and report creation within the embedded analytics platform saves your clients enormous amounts of time and provides added stickiness in your application. If your clients can collaborate where the data exists, rather than copy and paste screenshots into emails and instant messaging platforms, they will remain in the platform longer, will have live visibility of their data, and can take action instantly.

How to implement embedded analytics?

Implementing embedded analytics involves seamlessly integrating data visualization and reporting capabilities into existing applications, portals, or websites, allowing users to access valuable insights without switching platforms. The process starts with identifying user needs and understanding the data insights that will most benefit the app’s workflow. 

Choosing the right analytics platform is crucial—solutions like Microsoft Power BI and Yellowfin BI provide robust tools for embedding interactive dashboards and reports. Once the platform is selected, designing user-friendly and intuitive visualizations ensures that the analytics blend seamlessly with the application's interface, improving the user experience. Data security and data compliance are also critical factors, requiring robust data protection measures to safeguard sensitive information while adhering to industry regulations. 

After embedding the analytics, thorough testing is essential to verify functionality, performance, and seamless user interaction. Additionally, ongoing monitoring and optimization help improve the analytics experience based on user feedback and evolving business needs. 

By following these steps, businesses can successfully implement embedded analytics, directly empowering users with real-time insights within their everyday applications, enhancing decision-making, and driving efficiency.

AI analytics and embedded analytics

With the rise of AI analytics (also called augmented analytics) - the use of natural language query (NLQ), machine learning (ML) algorithms and artificial intelligence (AI) in analytics - more and more of the manual labor in business intelligence and analytics is being automated and streamlined to become more accessible to more people business (non-technical and analysts). This trend is being called the rise of the augmented consumer by several data industry experts.

This introduction of AI and automation is a huge benefit to you and your clients if you have embedded analytics into your software application. Augmented analytics allows you to market an AI-enabled product and provide your end users with data insights faster than ever before. 

For example, through automated data discovery Yellowfin Signals will scan your client’s data for trends and patterns and instantly alert the relevant end user of any statistically significant changes. These could be dips, spikes, trend direction changes, or step changes. With instant alerts, the end user will be able to act immediately to fix issues or maximize successes. No more manual data discovery. That also means they don’t need as much internal analyst resource either - you’re saving them salaries as well as time.

Machine learning can also assist the end user in uncovering insights as they query data. Capabilities like Yellowfin’s Assisted Insights allows users to click a data point and choose ‘explain’ or ‘compare’. Algorithms then populate the explanation or comparison with related analysis and even natural language explanations in bullet points so your users can get the most out of all their data by seeing it in context and with descriptions they understand. Yellowfin’s Guided NLQ feature also utilizes natural language query (NLQ) to help guide users on how to ask questions of their data and find relevant answers and explanations.

The augmented analytics trend is only going to increase with automation expanding into more of the traditional analytics capabilities and assisting analysts and end users in their quest for insights and explanations.

What does embedded analytics cost?

Price is naturally an important consideration when assessing data solutions for your software application or work platform. Different vendors offer varying models of embedded analytics and associated analytical capability, as well as different approaches. Understanding which vendor aligns with your specific use cases and data requirements is essential to getting the best embedded analytics platform, as well as avoiding hidden costs.

In general, business intelligence solution vendors price their embedded analytics tools using various licensing or subscription models. For example, Tableau embedded analytics pricing is organized into three  tiers, Tableau Creator, Tableau Explorer, and Tableau Viewer, which roughly corresponds, in capability, to the level of integration of analytics desired. Meanwhile, Yellowfin BI’s pricing plans are organized by business type (enterprise or independent software vendors). For embedded analytics specifically, Yellowfin can be priced per unit (total analytics usage), revenue share model, or server core deployment. 

We recommend reading our embedded analytics pricing comparison blog for more information on the various pricing models that analytics vendors employ. You can also learn how to lower TCO and increase ROI with embedded analytics in our free guide.

What should I look for in an embedded analytics partner?

After you have considered pricing and where your software’s analytic maturity level lies, it is important to take the time to assess what to look for in an embedded analytics partner, and the questions you need to ask before selecting an embedded BI vendor:

• Does the vendor offer modern interactive dashboards, BI reporting, data visualization and AI-powered features as part of their broader analytics software suite?
• Does the vendor support the right data sources?
• Does the vendor support OEM embedded analytics (white labelling)?
• Does the vendor provide proactive technical support and guidance for implementing analytics into your platform?
• Does the vendor provide live demo datasets and/or an embedded analytics proof-of-concept (PoC)?
  
  

Be sure to read our guide on the top embedded analytics pitfalls and how to avoid them for more information about what criteria to look out for when evaluating different BI vendors’ offerings.

Embedded Analytics: Next steps to get started

Ready to embed analytics into your business application or portal? We recommend watching the Yellowfin Embedded Analytics Walkthrough video, trying a free demo with our experts, or downloading our free ultimate guide to Embedded Analytics, included below for your convenience.