Most modern analytics solutions—Power BI, Looker, Qlik, and Tableau—have embedded analytics capabilities. We explore what makes these platforms different and how to best work within each to build an embedded analytics solution.

Many of our clients ask about embedded analytics—often in the context of monetizing their data. It’s something many organizations are doing especially as they identify their own valuable data sets—something you likely also have. Or, they want to enable a better user experience for their business users—something that will help reduce user errors, lead to faster decision-making, and address software licensing constraints. In either case, embedded analytics is a solution for both internal and external use cases.

But which business intelligence (BI) platform should you use and how can you build the most effective and secure embedded analytics solution for your specific use case? Our experts compared four BI platforms—Qlik Sense, Power BI, Looker, and Tableau.

What Are Key Considerations for an Embedded Analytics Solution?

A real-world example of embedded analytics: Civic Consulting Alliance needed a modern analytics solution to provide actionable data on household income across different demographic groups and geographic areas in Cook County. Within each BI platform, the embedded analytics solution needed to include geospatial mapping visuals as well as specified KPIs pulled from various data sources. Other considerations included:

  • Data sources: How do you connect to each data source, where are they located, and how can the BI tool ingest and prepare the data from the different data sources?
  • Authentication and access: How is a user allowed to view the data, and do they need to sign in or not? Are there different access levels for different users, and what data can different users see?
  • Data visualization: What sort of charts are available—are there any special charts a BI platform has that others don’t? How easy is it to build a visual? How do the visuals look aesthetically? Do different charts talk to each other and respond to filtering? How customizable are the visuals?
  • Embedding the visual: What sort of API does the tool use? How easy does the tool make it? How does the embedding work with authentication? What will be required to get it functional?

Comparing Embedded Analytics Platforms—Strengths and Weaknesses with Workarounds

Building an Embedded Analytics Solution in Qlik Sense

Qlik Sense delivers a modern, self-service oriented experience through a responsive user interface. It supports seamless deployment across on-prem and cloud sites and it has an open platform with APIs that allow web developers to build custom applications and embed analytics in existing applications and portals. Qlik offers flexible development capabilities as well as a single governance framework to allow for common security, manageability, and reusability.

Qlik Sense dashboard with embedded analytics solution showing map of Chicago color-coded to represent racial disparity by census tract.

Using Qlik Sense embedded analytics, you can easily create and embed an area map into a web page—and users can easily filter through different KPIs on one object.

Some takeaways when creating the embedded analytics solution using Qlik Sense include:

  • Combining and integrating data sources: Qlik Sense allows you to easily combine any data source—large or small—for an embedded solution that seamlessly integrates into workflows.
  • Exploring relationships in data: The platform uses Qlik Associative Engine—an in-memory data engine that enables you to rapidly explore relationships in various sources of data without having to write queries. The associations exist across all objects— map, bar chart, or table—making it easier to focus on bringing in data as opposed to having to worry about indicating which filters apply to which objects.
  • Responsive design in data visualization: Qlik’s opinionated design language —which automatically optimizes the user’s experience for the device they’re using did not lend itself well to layering in different views and different objects necessary for this project. There were complications in having objects respond to one another, and the automatic adjustment made some objects either too small or too large. A workaround is possible by embedding the entire sheet of the HTML solution instead of the individual objects— however this is not recommended for a branded solution.
  • Granting anonymous access: Anonymous authentication—a process of giving access to anyone without requiring them to supply user identity or credentials—is easy to do if you’re using Qlik Sense as a client-managed solution, but there isn’t an out-of-the-box solution for their SaaS platform. The workaround is to develop a reusable process that would allow for anonymous authentication within the embedded analytics solution.

Building an Embedded Analytics Solution in Power BI

Microsoft’s Power BI is a SaaS product that is designed for unified self-serve business intelligence providing data visualizations, data transformation, automation, paginated reports, artificial intelligence and much more. Power BI allows users to consume content through the Power BI web portal, through a mobile app, or embedded into custom websites. Embedded analytics Power BI implementations generally fall into two categories: (1) embedding for your internal organization and (2) embedding for customers.

Power BI dashboard with embedded analytics solution showing map of Chicago color-coded to represent overview of median household income for each census tract.

Power BI allows users to consume content through the Power BI web portal, through a mobile app, or embedded into custom websites.

Some takeaways when creating the embedded analytics solution using Power BI include:

  • Embedding for your organization vs. embedding for your customers: When beginning to build Power BI embedded applications, it is important to clearly define your use case. Creating apps for your internal organization can enable your employees to generate data insights and act upon them in an ‘all-in-one’ self-service platform. Imagine a tool where sales executives can both identify high value opportunities and follow up with leads from the same portal. Embedding for external users enables data monetization use cases. For example, consider a market intelligence company that may want to share their proprietary data models and visualizations with paying customers. Understanding the embedded use case will, in turn, define the solution requirements.
  • Licensing and authentication: Often, the most challenging task in developing data-driven web applications is authentication. Power BI licenses are offered in several different SKUs (Pro license, Premium per User [PPU], Premium [P SKU], or Embedded/Azure [A SKU]). Each allows for their own security and authentication methods. In general, authentication is implemented via a service principal using Azure AD or by inheriting the license and credentials of a ‘master user’. Defining the use case (internal vs. external users) is critical to the design requirements. If developing for internal users, consider if they will have Pro licenses and how to configure the Azure AD properly. If developing for external users or a public website, you should take care to encrypt your master user credentials. Embedded application developers should work closely with data governance teams.
  • Visuals and iframes: These are two primary ways to embed Power BI features into a website or application. You can use an iframe to embed an entire report into your website. HTML embed codes are automatically generated from the Power BI web portal for you. Again, take care to understand the tenant level settings for your workspace embed codes to ensure that your users will be properly authenticated when accessing. For a more custom application, you can embed multiple standalone visuals to design your app. When designing this way, authentication will use the Power BI JavaScript REST API to authenticate and interact with the dataset.
  • JavaScript REST API: JavaScript is a programming language that is one of the core technologies of the web, alongside HTML and CSS. An API (application programming interface, i.e., the way programmers talk to each other) is used for communication from your client site to the Power BI backend. The Power BI JavaScript REST API provides service endpoints using JS so that your application can communicate with Power BI web services. The API’s uses range from embedding, to administration, to governance and user resources. Developers can use this API to drive complex interactions with the PowerBI apps and data. Learn more in the Power BI Playground.
  • Power platform: Modern data experiences will not use Power BI in isolation—it is important to consider the entire ecosystem of the Microsoft Power Platform (Power BI, PowerApps, PowerAutomate). Power BI is just one piece of the puzzle. Power BI and PowerApps are already able deeply integrate with one another today. Embedded analytics solutions, especially for internal organizations may want to pair it with PowerApps to provide a call to action in tandem with insights provided with Power BI.

For most of our customers in enterprise scenarios, using a Premium SKU and hosting reporting within the native Power BI web portal usually is most appropriate and provides faster time to value. Full embedding scenarios, though, allow developers to go above and beyond what is native in the web service (which is already a ton!). Pairing web developer skillsets (HTML, CSS, JavaScript) with that of data engineers and data analysts can help create seamlessly integrated and incredible custom built, data-driven website applications.

Building an Embedded Analytics Solution in Tableau

Tableau is a powerful BI platform that helps anyone see and understand their data. Relative to the other BI platforms, Tableau excels at creating sleek, aesthetically pleasing interactive visuals. In addition to industry-leading visual capability, Tableau also allows users to share their work with others by publishing to Tableau Online or through embedded analytics. Developing embedded analytics in Tableau happens with the JavaScript API, which allows the developer to integrate the dashboard into the website and replicate core Tableau functionality like filtering, parameter control, and navigation into the website itself rather than in the dashboard. This functionality allows developers to control both the look and feel of the dashboard as well as how deeply the dashboard is integrated into the website itself. Usual Tableau functionality such as row level security and user filtering is still applicable for embedded dashboards.

Tableau dashboard with embedded analytics solution showing map of Chicago color-coded to represent median household income by census tract.

Tableau’s embedded analytics solution lets users embed existing dashboards into websites with the Tableau JavaScript API; basic embed code is generated automatically every time a dashboard is published to Tableau Online, Tableau Server, or Tableau Public.

Some takeaways when creating the embedded analytics solution using Tableau include:

  • Dashboards can fit seamlessly into a website: Tableau’s embedded functionality allows the developer to tightly integrate their dashboard into a website. Embedding a dashboard in Tableau requires basic knowledge of web page programming in JavaScript and HTML, although unlocking core embedded functionality requires very little custom code. If you don’t have experience working with these coding languages, start by learning the basics. With the tools Tableau provides, however, the developer can not only put their dashboard alongside custom web content but also integrate dashboard controls to programmatically respond to user input in the website itself.
  • Low barriers to a basic embed: Building an embedded dashboard with basic functionality can be accomplished with only a few chunks of JavaScript API code. The barriers to embedding a dashboard into a website are surprisingly low if you don’t want to include any special website functionality. Once you have the code for one embedded dashboard, it is straightforward to replicate for future dashboards.
  • Auto-generated code: Tableau auto-generates basic embed code whenever you publish a dashboard to Tableau Server or Tableau Online. Web developers can then use Tableau’s JavaScript API to display visualizations, navigate between dashboard pages, and filter dashboard data from within the web page rather than within the confines of the dashboard.
  • Advanced functionality: Tableau’s JavaScript API allows you to do a whole host of things to increase functionality such as chaining functions together or interacting with one dashboard to alter the results of a second dashboard. This allows the developer to tell a more comprehensive story from within the website rather than within the dashboard, ultimately increasing the functionality of both the dashboard and the website. The API enables customization to allow the user to control dashboard size, positioning, default views, and displayed content, making sure that the dashboard can sit alongside websites of all types.

Building an Embedded Analytics Solution in Looker

Looker is a modern, cloud-based BI platform that connects directly to your database—enabling data-driven businesses to provide access to the most up-to-date data for end users. Data governance is made easy with Looker because the platform is purpose-built for integration with a central data warehouse—where business logic has already been defined and data has been cleaned. Looker helps define data relationships using its own modeling language—LookML—so that developers can work quickly without having to write complicated SQL queries. The platform allows for secure embedding through single sign on and can deploy into third-party solutions as an embedded iframe or through JavaScript. Its drag-and-drop functionality and collaboration features enhance user experiences, and pre-built visualization formats (like heatmaps) help accelerate the process and generate real-time snapshots from the data. It is a great platform for teams with mixed skill levels, but it comes at higher cost than other solutions.

Looker dashboards with embedded analytics solution showing maps of Chicago color-coded to represent racial disparity and median household income year over year.

Looker’s embedded analytics solution provides the visualization styles, functionality, and data security a user would expect in the Looker platform without having to log in to Looker at all.

Some takeaways when creating the embedded analytics solution using Looker include:

  • Data sources: Looker cannot import CSV or Excel files directly—any data Looker touches needs to be accessed via a database. You can work around this by setting up your own database using a tool like Supabase, an open-source database.
  • Data security: Looker provides excellent tools for handling data access. This is something to keep in mind when you need to find a way to give the public access to the data without exposing any internal data.
  • Anonymous user authentication: Looker’s API handles a lot of the heavy lifting when it comes to authenticating users, and is accessible using many different popular coding languages, so users can interact with the API in the language they’re most comfortable with.
  • Flexible heatmap visualization: Looker’s maps have an Automagic Heatmap ability that generates squares of a heatmap using raw latitude and longitude. The squares will change depending on how zoomed in you are. This is a great feature if you’re analyzing raw location data without having to define boundaries.
  • Version control and multi-developer support: Looker is easy to use in small or large teams—many developers can be working on the same project and the changes are all logged via GitHub.
  • Embedding tools for developers: Looker is constantly pushing to make embedding easier and faster for frontend development teams—this makes customizing your visualization that much easier. The platform offers premade visualizations that you can use as is, or you can use elements of it and customize the rest, or completely make your own to meet your business needs.

Time to Start Thinking About Embedded Analytics

With a low barrier to entry for embedded analytics, now is the time to start thinking about how you can benefit from an embedded solution in your organization. All four of the BI tools discussed in this blog—Qlik Sense, Power BI, Looker, and Tableau—have embedded capabilities baked into their platform or offer specific embedded-only tiers of licensing.

Whether you’re looking to increase user adoption of analytics, increase efficiency with your business users, create a consistent look and feel within existing applications, or monetize your data—embedded analytics can help you get there. As you consider which platform you want to use, remember there is no time like the present to first examine your data strategy and make changes where necessary, asses your data stack and it’s capabilities to make sure it is compatible with the BI tool you want to use, and finally plan out your uses cases for embedded analytics and the value you want to get out of the solution.

Sharon Rehana Sharon Rehana is the content manager at Analytics8 with experience in creating content across multiple industries. She found a home in data and analytics because that’s where storytelling always begins.
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