Embedded Analytics: What Is It And How Does It Work?

Andrew Chen
March 16, 2022

Incorporate Your Data Into Your Software, Application, and Website Using Embedded Analytics

Embedded analytics brings the full benefits of business intelligence to your customers or employees without interrupting their workflow, which allows them to make fast, data-informed decisions.

Want your data to be accessible to anyone who needs it without leaving the app or system they're using? Embedded analytics is the answer. By integrating critical data directly into your company's databases, friction is reduced, and processes are streamlined.

According to Allied Market Research, the embedded analytics market will be over $60 billion by 2023.

So let's find out more about what embedded analytics is—and how it works.

Embedded Analytics Explained

Embedded analytics integrates your data into apps, websites, and software, usually in the form of reports accessible through a business intelligence (BI) dashboard. In other words, it puts the information where you need it most, adding usability. Embedded analytics aims to bridge the gap between business processes and business intelligence. Instead of constantly switching from BI to your system, embedded analytics will bring BI data wherever it's needed.

For example, embedded analytics can interface with your business information dashboard to offer up-to-date information for your sales team. They can then run accurate demos, plugging directly into the CRM system, so employees don't have to switch between the BI platform and the CRM to check the data before closing deals.

Embedded analytics can also be used to improve the UX of your product. For example, if your SaaS system helps other companies manage their documents, embedded analytics can let them check the latest status updates in real-time.

A blog post can illustrate complex ideas through data visualization. With embedded analytics, you can insert the data right into the article. Or, if you need to check the status of your documents, the dashboard can produce a single report drawn from several data sources, providing relevant information at a glance.

Incorporating BI data into your workflow allows you to monitor processes better and add more value to your products or services.

The Relationship Between Embedded Analytics and Business Intelligence

Business intelligence is the comprehensive analysis of all the data within your company. It is a tool used to improve decision-making within a specific data platform.

Embedded analytics leverages business intelligence. It's a method of bringing the dashboard out—instead of siloing it within the BI platform—placing it where needed, such as within an article, spreadsheet, or CRM.

Within the business information system, embedded analytics provides a more precise visualization of business data, which leads to a clearer understanding. It then moves the data outside the confinement of BI systems, where stakeholders can make better, data-informed decisions.

How Is Embedded Analytics Used?

Embedded analytics makes data readily available and usable in three places: internal tools, the web, and your products. The reasons behind the decision to use embedded analytics may differ, but they all focus on using data to improve business outcomes.

1. Your Products

Embedded analytics can be used as a premium feature to upgrade customer experience.

For example, a user may wish to check their data status. If that user notices an inconsistency or an error, it can be fixed right away without switching to another system to report the issue.

2. Internal Tools

Using embedded analytics within your internal tools makes workflow more efficient. Other tools like CRMs also provide visualizations, but embedded analytics takes it farther.

For example, what if a company can visualize its sales data yet still can't incorporate that data into its larger business initiatives?

With embedded analytics, a company can gather all its business data together, placing it in one sales dashboard that makes the most sense to the sales team. Thus, embedded analytics ensures that the sales team doesn't have to leave their CRM to analyze their data and make robust data-driven decisions.

3. Websites

Embedded analytics is also used to create engaging content for blog posts and other web pages. Plugging data visualizations into blog posts with complex concepts helps communicate the data better.

For example, interactive graphs and charts can be incorporated to illustrate key points more effectively.

Three Ways to Make Use of Embedded Analytics

To understand how you use embedded analytics to best effect, it is essential to know what's going on under the hood. Otherwise, embedded analytics could be a waste of time and money.

Embedded analytics functions in 3 steps:

  1. Data is gathered from different sources through an API and stored in a centralized database.
  2. The collected data is queried and used to make a report or dashboard, which can be embedded into a piece of software, an application, or a website.
  3. The dashboard or report refreshes the data by rerunning queries, the period of which may vary.

Understanding these steps can help you decide how to implement embedded analytics, integrating it into your system. If this seems complex, you can always invest in a platform to simplify implementation rather than coming up with an in-house solution

How Do You Integrate Embedded Analytics?

Use it as a Feature for a Larger BI Platform

For companies already invested in a BI platform, this option is the easiest. The BI platform already eliminates most of the steps mentioned above, simplifying the process.

  • Pro: Avoid buying additional software, setup can be simple if you've already implemented the BI platform.
  • Con: These platforms are not designed for embedded analytics, so design, security, responsiveness, and ease of implementation are all areas they struggle in.

Buy an Embedded Analytics Platform

There are tools customized explicitly for embedded analytics. These are not BI platforms but features to help developers implement the steps above.

  • Pro: Specifically designed for embedded analytics, customizable, product-ready, provides a great experience for your end-user.
  • Con: Only solves embedded analytics, not BI. These tools also use proprietary languages and new interfaces, which might take some getting used to.

Build and Maintain an Embedded Analytics Solution Using Developer Resources

You can build your own system tailored to your specific needs. But this takes up a lot of precious developer time and can be expensive in the long run, particularly in terms of opportunity cost. Companies who choose to build analytics in-house, typically find themselves committing more time than imagined to iterate on dashboards, and can't provide all the features their customers are looking for.

  • Pro: Embedded analytics customized to your specific needs.
  • Con: The need to provide significant development resources to create and maintain embedded analytics.

Evaluating an Embedded Analytics Solution

There are a few elements to consider when considering embedded analytics for your system. Here's a list of what to think about:

Developer Resources

Consider the time and effort your developers need to get your system up and running.


Find a way to secure your data and ensure that the solution can securely share data given your data model.

Load Time

Queries can slow the load time for the dashboard. If you want a faster loading time, consider looking for options that use progressive loading so users can see the dashboard as it loads individual charts.


Embedded analytics can be a powerful tool to improve stakeholder experience and give you insights into your data in an integrated, streamlined manner, leveraging your business information backend.

But it's essential to understand the implementation process and evaluate your needs before adopting embedded analytics to your business to avoid wasting time, money, and opportunity.

Finally, if you want to build your own analytics system, ensure you have the necessary developer resources, other embedded analytics may be the best solution for you.

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