What is Embedded Analytics? Benefits, Tools/Software & Examples

Embedded analytics bridges the gap between two distinct disciplines, data analytics and software engineering, bringing the value of data analytics into software products.

Everyone is trying to make better use of their data. Staying ahead of the curve means not just having data, but making it work smarter for you. That's where embedded analytics comes into play, seamlessly integrating powerful insights right where your customers need them—in your applications and software. 

Rather than have to go to a different place to see reports, data visualizations, and insights, people can see all this information “embedded” in their normal workflows and software that they use day to day by using an embedded analytics platform.

Embedded analytics are within the reach of most organizations, even those that do not have a data team or do not have a software product. In this article, we will explore the benefits and various approaches to getting data insights into the hands of your customers. 

Understanding Embedded Analytics

What Is Embedded Analytics?

Embedded analytics is the integration of data analysis and visualization capabilities directly into business applications, websites, or portals. This means users don't have to switch between applications to analyze data; instead, they get actionable insights right where they are. 

It changes the way businesses interact with data, making it more accessible, understandable, and actionable for all users, not just data analysts behind the scenes. By embedding analytics, organizations empower their users and customers with the ability to make data-driven decisions in real time, enhancing efficiency, productivity, and user experience.

What Are the Benefits of Embedded Analytics?

Although simple in concept and straightforward to implement, embedded analytics can bring great benefits to any business. Some benefits come from embedding analytics into customer-facing software products, others come from embedding analytics into internal systems for internal audiences, and some apply to both situations. 

• Better User Experience: By integrating insights directly into applications, users enjoy a seamless experience, avoiding the need to juggle between different platforms for data analysis. This integration enriches the software's capabilities, making it more intuitive and insightful.
• Competitive Advantage: Embedded analytics can add a strategic capability to your transactional software product. Offering data-driven guidance directly within your product not only differentiates it in the market but also adds value for the users, setting you apart from competitors.
• Data Monetization: This involves leveraging the data your applications generate or process to create new revenue streams. By providing customers with premium analytics services or insights, you can unlock new monetization opportunities.
• Increased Adoption: When users find real value in the insights your application provides, they're more likely to use it consistently. Embedded analytics can drive higher engagement and adoption rates by making data analysis a core part of the user experience.
• Real-Time Access to Data: In today's fast-paced world, having access to real-time data can be a game-changer. Embedded analytics ensures that decision-makers and users have immediate access to the latest data, enabling quicker, more informed decisions. Compare this with organizations that send out monthly or quarterly PDF reports.

The Evolution of Embedded Analytics

Understanding the historical trends that led to embedded analytics (part of what is often referred to as the Modern Data Stack) gives context to why things work the way they work today. 

1. Basic Reporting from Databases: Initially, data analytics was about basic reporting directly from databases, focusing on historical data to inform business decisions or manage processes. Products from the 1990’s and 2000’s connected to an on-premises database so that anyone in the office could access a report. Often, these reports were scheduled and published, rather than real-time on demand.
2. The Rise of SaaS: With the commercialization of the internet, Software as a Service (SaaS) became a preferred way to deliver software, and this is a key time in the history of embedded data analytics. Software products in all categories became more flexible and capable, and data analytics capabilities are no exception. As SaaS products mature, customers begin demanding more visibility and reporting capabilities to prove ROI and make the business relationship more valuable.
3. Cloud Infrastructure: As the number of SaaS companies and products grew, so too did their demand for scalable infrastructure that would allow them to deploy their software to exponentially more customers and users. Companies such as AWS, Google, and Microsoft began providing cloud infrastructure capabilities that satisfy this demand for scale. 
4. Cloud Data Warehouse: As cloud infrastructure became more ubiquitous, the scalable nature of the cloud began to to be used in databases designed and built for analytics in the cloud. These are known as cloud data warehouses. Although under the hood these technologies are complex, on the surface they are simply scalable databases.
5. Cloud-Native Visualization Tools and BI: As cloud data warehouses became popular, a new breed of business intelligence and data visualization tools arose. These were SaaS products, built on cloud infrastructure, designed to work with cloud data warehouses. 
6. Embedded Analytics: In-Application Analytics, as a Product Feature: Somewhere along the way, everyone realized there would be a ton of value in combining the data visualization and reporting capabilities of cloud-native BI tools with the demand for in-application analytics. These BI product vendors began adding capabilities to embed their data visualizations into other SaaS applications. 
7. Future of Embedded Analytics: BI product vendors will continue to innovate, adding AI capabilities like natural language and automated insights. This symbiotic relationship between SaaS products and the BI products they embed will allow each to specialize while bringing a unified experience to customers. 

With this rich history guiding our understanding of embedded analytics, we can explore more about how to bring this capability to your company.

Data Monetization with Embedded Analytics

Embedded analytics unlocks many options for data monetization, which is a large topic on its own. Many companies have proprietary data or insights that are valuable and can be monetized. By embedding these analytics, you add value to your products and services. You have the option to charge more for a premium service that includes these analytics. Or you can increase the value of your core services. A differentiated product increases customer loyalty and retention and increases win rates in sales. 

By providing actionable insights, you give customers a reason to return and a means to optimize the benefits they get from your core services. This increased engagement leads to higher customer loyalty and renewals. 

How is Embedded Analytics different from Traditional BI, White-Labeled Analytics, Customer-Facing Analytics, and Self-Service Analytics?

Embedded Analytics is the most common phrase, and several other phrases are commonly used either in place of or complementary to embedded analytics. Each has significant overlap with others, and knowing the difference will help you communicate more clearly about your product strategy.

• Embedded Analytics integrates directly with your business application or product, providing data insights and visualizations within the context of the user’s existing day-to-day workflow. Users can use this data in their decision-making without leaving the application.
• Customer-Facing Analytics provides analytics and insights directly to end customers, enhancing the customer experience with the product or service. Often, these analytics are delivered by using embedding. But this is not always the case. Sometimes, these are standalone data products or reports. 
• White-Labeled Analytics. White labeling is the concept of using a vendor product as a component or all of your own product but with your branding. The end customer is not aware that there is a vendor product underneath your solution. This is especially important in embedded analytics because you are using a vendor BI or visualization tool embedded in your product. Most vendors support white labeling, but a few try to sneak their logo into the embedded dashboard. The capabilities to theme and style data visualizations to match your application and branding can vary.
• Traditional Business Intelligence. Traditional BI is focused on creating reports or dashboards and sharing them internally with colleagues. Some vendors support embedding as an add-on feature, allowing them to handle traditional BI needs as well. Other vendors focus only on embedding as their primary use case. 
• Self-Service Analytics is the idea that customers or users can get the answers they need when they need them when they want them, and without waiting. Often this phrase is used to highlight the differences of delivering static reports that can quickly go stale.

Embedded Analytics in Action

Embedded Analytics Examples at Fortune 500 Companies

The New York Times is famous for embedding live, interactive data analytics content in their online articles. This adds richness and additional context to the stories they are sharing. 

LinkedIn has always been one of my favorite B2B sites that seamlessly integrates useful insights into my normal usage of the platform. As I use their product, I am informed about the impact of my use, causing me to take steps to improve the outcomes of using their product. It’s a great cycle because it causes me to engage not just more but also more deeply with the service.

Zillow’s entire company is based on data in the real estate industry. Simple, interactive data visualizations create an engaging, informative experience for users. It also helps communicate the value of using Zillow.

Embedded Analytics Examples at Small and Mid-Sized Companies

Embedded analytics applications can be found at companies of all sizes, not just the largest companies. Some of my favorite embedding examples include companies that are not traditionally technology companies. 

SevenStep found that by offering insights about various geographies, they added strategic planning capabilities to HR, in addition to their core transactional service (recruiting). Their Chief Strategy Officer said, “We created a multi-dimensional dashboard that presents insights along 100 different metrics that are highly consumable and very relevant to our customers. As a result, we have seen significant growth in our key accounts, while also gaining significant productivity improvements with our own staff.”

Portland Cement Association increased revenue by offering premium, real-time, self-service dashboards to members who need fresh information to make fast decisions based on industry data. Before this, the best Portland Cement Association could offer was static PDF reports sent out every quarter.

Their Director of Analytics said, “We are now able to deliver self-service analytics to our members with quick access to data needs and reporting. This further increases the value to a Portland Cement Association membership, a priority of our company.”

In the screenshot below, you can see the top-level header and navigation menu that are part of the Portland Cement Association’s normal website. And below that, on the same web page, you can see the embedded dashboards that offer an interactive experience. 

Herrmann transformed their 30-year-old whole-brain thinking science from static charts to an automated analytics experience. This made strategic impacts on their clients’ businesses and increased revenue opportunities. Their CEO reported a 10% increase in revenue opportunities, unlocked by providing a differentiated product.

The same data and analytics they use for getting a 360-degree view of their customer helps drive customer-facing analytics within their SaaS application.

How Do Embedded Analytics Work?

Embedded analytics work by integrating data analysis and visualization capabilities directly into business applications or platforms using embedded analytics software. This process involves leveraging APIs or SDKs from analytics or BI tools to embed analytics components—such as dashboards, reports, or individual visualizations—into the application's user interface. 

The goal is to provide users with insights and data-driven decision-making tools within the context of their existing workflows, without needing to switch to separate analytics applications. This seamless integration ensures that data insights are accessible and actionable where they are most relevant in real-time, enhancing the user experience and operational efficiency.

How to Get Real-Time Analytics Embedded in Your Application?

The most common approaches to embedding real-time analytics include using iframes or javascript components. 

___callout: Are iframes secure for embedding? Iframes have a bad reputation for being insecure. However, the vulnerabilities that make them insecure have been identified and known for some time now. The W3C and browser vendors have added features to overcome iframe vulnerabilities. Modern web development best practices make use of these features. The iframe has made a comeback in recent years, and is the preferred method for most embedded analytics products. 

An example of an iframe-based embedding starts by building a visualization or dashboard in an embedded analytics tool, matching the style of your application. Then, you designate the dashboard as “embeddable.” You write some code in your application to call the embedded analytics tool and request a secure URL. That way, the user can only see the data they are supposed to see, and the URL is not sharable publicly. That URL becomes the source for the iframe in your application. The result is a seamless integration between the dashboard or visualization and the rest of the application.

An example of an iframe-based embedding starts by building a visualization or dashboard in an embedded analytics tool, matching the style of your application. Then, you designate the dashboard as “embeddable.” You write some code in your application to call the embedded analytics tool and request a secure URL. That way, the user can only see the data they are supposed to see, and the URL is not sharable publicly. That URL becomes the source for the iframe in your application. The result is a seamless integration between the dashboard or visualization and the rest of the application.

An approach using javascript components is often called “native visualizations” as compared to “embedded analytics” or “embedded visualizations.” In this approach, the product engineers design and build visualizations using low-level programming libraries or charting libraries. They also build the communication infrastructure to securely connect to the data warehouse and execute queries that return the data to be visualized.

Choosing the Right Tools and Solutions

Ultimately, there are hundreds of options you can choose from to create visualizations and display them in your application. How do you even start to narrow this down to select the best embedded analytics tools? 

First Question for Analytics in Your Application: Build vs Buy

At Datateer, we have customers who use embedded analytics tools to create and manage their data visualizations. We have a smaller set of customers who choose to write their own visualizations using javascript libraries. Both approaches are technically feasible, i.e. there are no technical constraints that would make one better than the other.

The decision here is more around flexibility vs maintenance costs. If you need extreme flexibility in the types of visualizations you want and the user experience you need, you may find that BI tools are not up to the task. Addressing these more complex visualization scenarios using proprietary code built by your application team is the way to accomplish this.

However, all this code must be maintained. Often any compromises you need to make to accept a solution using a BI tool’s visualizations are justified when compared to the maintenance cost and risks of running complex custom code. 

In addition to the code for the visualization itself, the application backend/server/API will need to be updated to accommodate query requests from the visualization on the front end, translate those into SQL to execute in the data warehouse, and manage the connection to the data warehouse. 

Some of our clients are perfectly happy with the benefits they get to flexibility and user experience in their application. This option fits perfectly with their capabilities and their needs. But a common mistake is to have a negative reaction to the price tag of a capable BI product and jump to the conclusion that doing proprietary, custom visualizations is a better option. Often, the costs of this approach are actually more than embedding analytics, when considered all together.

Related Article: Selecting the Right Visualization Tool with Confidence

Checklist for Evaluating Embedded Analytics Solutions

Here is a list I’ve compiled from observing and advising hundreds of people who have worked out an embedded analytics solution for their companies.

• Design Customization: can it be completely seamless with your application’s layout, colors, fonts, language, and branding?
• Security: is the embedding process secure, or is there any way for data to be exposed?
• Data Protection: does the tool implement row-level security or other means to only show a user the data they are allowed to access?
• Cost: Embedding analytics is a premium feature in the marketplace, and most vendors are aware of this. They often charge more for embedded analytics features.
• Support/Community: Some vendors have excellent, real-time support. Others do email only, with 48-hour turnarounds. This is important, especially when just getting started
• User Experience: Some vendors have a product that is naturally intuitive and quick to produce new visualizations. Other tools are “clunky” or hard to use and maintain. 
• Development Workflow: At some point, you will need to make updates to a live dashboard that users already depend on. Does the vendor require you to make live updates and hope they don’t break anything? Or do they have a way to make updates in a sandbox environment, to deploy to the live environment only when they are ready?
• Monitoring: How do you know if something is broken and your customers cannot see their data, or see a broken dashboard? 

Design Decision: Reporting from Your Application Database vs a Cloud Data Warehouse

Another decision most product leaders run into is whether to generate analytics and reporting directly from the application database. This can work, but you will likely outgrow this design decision pretty quickly. 

When it works: Use it if the budget is too tight for a data warehouse if you don’t already have a data warehouse, if the application database can handle the additional load, and if you don’t need any additional data sources involved.

When it fails: Data models for applications are more complex, so reporting queries will take longer to write and maintain, any changes to the application database will break reports, and the additional load can cause unnecessary scaling and costs.

Pitfall to avoid: don’t let your application database become your reporting database. If you have your embedded analytics draw data from your application database, you are on a “slippery slope” when it comes time to start adding additional data your customers will want–support ticket information, billing and invoicing data, etc. Now the application database is trying to do too many responsibilities. Even if technically it can handle the load, managing everything there is a nightmare. 

If You Have an Application Engineering Team but No Data Team

Sometimes an application engineering team can cover everything that is needed to produce solid data analytics. Other times, with its specialized tools and practices, data analytics is a distraction for an application engineering team, and they are better focusing on the core application. Embedded analytics requirements for external audiences often have a high bar.

If the right organizational structure for your organization is to separate data analytics from application engineering, you can have two different teams, or you can outsource managed data analytics. 

If You Want the Benefits of Embedded Analytics but Don’t Actually Have an Application!

For all the attention that SaaS and tech companies get in today’s business world, many companies don’t have an application that their customers use to interact with their business. 

What are the options in this case, to get the benefits of customer-facing analytics that embedding analytics offers?

• Option 1: Direct exposure. Some BI products make it possible to add your external customers as users in the BI tool. Usually, they will also provide some way to organize these users to manage dashboard security. We have seen this option used successfully. The downsides are that the management of all the users can be burdensome, keeping one customer out of another customer’s data is a risk, and there is no “main menu” navigation so users can get lost.
• Option 2: Build an application. For organizations considering digital transformation or offering self-service features to their customers, embedded analytics could simply be the first feature in a new application.
• Option 3: No-code solution. Datateer offers a no-code embedding solution. It handles what you need an application to do–manage users, secure data, control navigation, and user experience. This can be an appealing solution for companies that don’t want to manage their own application but want a white-label experience for their customers.

If Your Company Already Has Traditional BI in Place

If your organization already has a data warehouse, traditional BI reporting, and a BI tool, you may be in luck. Chances are good that you can leverage the existing infrastructure to deliver customer-facing data analytics in addition to internal analytics. 

Even if your existing BI tool does not support embedded analytics, you can leverage the data warehouse and data processes to add new data sources or create data models appropriate for your customers and other external audiences

Embedded Analytics Wrap Up

Embedded analytics is a game changer for how businesses use data by integrating insights directly into apps, making information accessible without switching platforms. This approach benefits all businesses, regardless of size or data expertise, by enhancing decision-making with immediate data access. The rise of internet and cloud services has accelerated its adoption, improving user experiences and opening new revenue streams. 

Choosing the right tools involves deciding between custom development or using existing solutions, ensuring seamless integration, and maintaining security. Success stories from both large corporations and smaller firms highlight its universal appeal, demonstrating that embedded analytics can significantly boost how companies interact with data, directly within their user workflows.