As the digital economy evolves into the new economy, everyone who engages with your business—whether external customers, vendors, partners, or internal stakeholders—benefits from having access to data and insights. However, requiring them to step out of their existing workflows and log into a specific business intelligence (BI) tool can lead to abandoning analytics altogether.
Embedded analytics are a great way to integrate real-time insights into the tools your users already leverage, whether it’s an existing customer-facing application or an internal tool used by colleagues. With numerous embedded platforms on the market, it can be daunting to figure out which one is best for your particular needs. By asking yourself a few simple questions about user experience, scale, flexibility, and cost, you’ll be able to narrow down the choices and find the perfect platform for embedding analytics into your application. Continue reading to make an informed choice for your business's embedded analytics tool.
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An embedded analytics platform refers to a software solution that allows businesses to integrate analytics and reporting functionalities directly into their applications, products, or services. Instead of leveraging or building separate analytics tools, embedded analytics software enables organizations to add capabilities such as data visualizations, reporting, and even generative AI to their existing software interfaces, providing users with rich, real-time insights in context, without the need to switch between different applications. As a result, companies can foster data-driven cultures, monetize their data, and even create entirely new revenue streams through data products.
Embedded analytics have revolutionized the way organizations leverage data, build products, generate revenue, and retain customers. Let's explore the five main benefits in detail:
1. Empowering data-driven decision making
One of the most significant advantages of using an embedded analytics platform is its ability to empower data-driven decision-making, both across an organization and beyond to customers, partners, and vendors.
By making data insights readily available at the point of decisions, organizations can foster a culture that values data-driven decision-making. Embedding a self-service analytics experience within a product or service helps every user access and analyze data relevant to their roles without having to change processes. This enables them to make informed decisions based on objective insights rather than relying on intuition or guesswork. The ability to leverage data effectively empowers organizations to identify trends, spot opportunities, mitigate risks, and gain a competitive advantage in the market.
For instance, embedded analytics solutions like ThoughtSpot offer powerful conversational AI capabilities that allow users to ask questions about their business data and instantly receive real-time visualizations and insights—all without the assistance of your data teams or IT specialists.
2. Enhanced reporting and data analytics
One of the primary benefits of using an embedded analytics platform is the ability to enhance data visualization and reporting capabilities. These platforms offer a wide array of visualization tools such as charts, graphs, maps, and dashboards. By embedding these visualizations directly into applications, users can easily interpret and understand complex data sets. Interactive data visualizations allow users to endlessly explore these visualizations, drill down into details, and use AI to uncover related, meaningful insights.
By leveraging AI-powered dashboards like ThoughtSpot’s Liveboards, users can easily monitor KPIs in real-time, drill into specific data points, and automatically receive insights via AI capabilities. These interactive and automated features allow users to pinpoint pain points and discover patterns that otherwise may go unnoticed.
3. Seamless integration and customization
Modern embedded analytics platforms, especially those that are part of the modern data stack, offer seamless integration with existing applications or products. This enables organizations to bring the best, more innovative capabilities from across the data ecosystem, and expose them to users in the workflows they already utilize. Furthermore, these platforms often offer customization options, allowing organizations to tailor the analytics experience to their specific needs and branding.
4. Increased user engagement and adoption
Embedding analytics can be an incredibly powerful part of a user engagement strategy. By embedding analytics directly into applications, organizations can give users more value, significantly increasing user engagement and adoption of analytics capabilities. Users no longer need to switch between multiple tools or platforms to access data insights.
Instead, analytics functionalities are readily available within their familiar application environment. Ideally, the embedded analytics experience is so easy, that every kind of user can intuitively leverage it without additional training. This convenience encourages users to explore and interact with a product more, leading to increased engagement, a higher adoption rate, and more robust data-driven decision-making practices. With easy access to analytics in the tools they already know and love, users become more empowered to make informed decisions based on real-time insights.
Customer Spotlight: MDaudit, a leading healthcare billing compliance and revenue integrity company, experienced 25% business growth and 40%+ increase in user growth with ThoughtSpot Embedded.
5. Time and cost efficiency
Embedded analytics platforms provide substantial time and cost efficiency advantages when compared to developing analytics capabilities from the ground up. These platforms come with pre-built components, such as data connectors, visualization tools, reporting features, and AI functionalities, which significantly reduce the time and effort needed to create and maintain analytics capabilities.
Further, by using the right embedded analytics tools, organizations can get access to cutting-edge technology from a vendor purely focused on analytics, instead of having to continually build these capabilities internally, leaving them behind the innovation curve. By leveraging an embedded analytics platform, they can save development resources, reduce time-to-market, and allocate resources to critical business areas such as their own core products. Additionally, competitive vendors often offer flexible pricing models, enabling organizations to scale their analytics capabilities while maintaining cost efficiency.
Customer Spotlight: Act-On, a leading marketing automation company, deployed an internal beta within 30 days, and went live in production with ThoughtSpot Embedded in under 90 days.
6. Monetize data
The most innovative organizations, such as Guidewire, Harri, Verisk and Act-On, have taken embedding analytics further. Instead of simply adding analytics to their products, they are turning data into dollars. With the right embedded analytics platform, you can charge a premium for access to this analytics experience in your product or service, or build entirely new data apps that offer new paths to profits. The end result is greater data monetization, turning data from a cost center into a revenue-generating engine. In fact, research from Product-led Alliance and ThoughtSpot found that 44% of product leaders expect revenue gains from embedding analytics.
1. ThoughtSpot Embedded
We might be a bit partial, but when it comes to embedded analytics tools, ThoughtSpot Embedded truly shines. Recognized as a Leader by the recent Gartner Magic Quadrant for Analytics & BI Platforms, ThoughtSpot solidifies its position as the go-to choice for businesses seeking to supercharge their existing applications with AI-powered embedded analytics.
Offering a comprehensive suite of features, ThoughtSpot Embedded allows you to seamlessly embed analytics capabilities into other applications, portals, or websites, extending the reach of data insights beyond standalone tools and integrating them into your existing digital ecosystem. And with our powerful ThoughtSpot Embedded Developer SDKs, you can ship scalable, tested, and fully customized analytics experiences—without compromising speed or security.
Core features
Low-code embed: Infuse ThoughtSpot’s AI-powered analytics into your web and mobile applications with just a few lines of code.
Range of embed options: Choose whether you want to embed certain components—like the conversational AI experience, entire dashboards, single visualizations—or the full capabilities of the platform.
Easy white labeling: Create on-brand, personalized data experiences for your customers and partners with multiple customization options.
Enterprise-ready SDKs: Access a full range of programmatic capabilities to manage and maintain your embedded deployment.
Integrated developer playground: Give your developers the ability to prototype and preview workflows directly within the platform.
AI-powered capabilities: Empower users to find insights from their data faster with AI-powered natural language search and conversations, interactive Liveboards, and AI Highlights.
Seamless integration: Connect your data from various sources, including third-party applications and other business tools. This powerful integration enables you to extend their platform’s value without requiring substantial resources or slowing time-to-market.
Customer Spotlight
By adopting ThoughtSpot Embedded, Harri, a leader in human capital management, significantly enhanced its platform and delivered personalized data experiences tailored to three main user personas. This powerful integration empowered managers to find insights on their own, resulting in improved business processes and major contract wins.
See how embedding ThoughtSpot is helping Harri provide a real-time, self-service user experience:
Pricing
ThoughtSpot Embedded offers three flexible pricing models tailored to your needs. These plans essentially include a ‘Developer’ option which is free—for one year— for product teams to prototype and embed analytics into their apps quickly. The others are ‘Pro’ and ‘Enterprise’, tailored for embedding analytics for multi-tenant, large-scale applications. We recommend consulting our team to determine the best plan for your specific goals.
Want to see for yourself how embedding ThoughtSpot results in highly engaging data experiences within your apps? Schedule your free ThoughtSpot Embedded demo today.
2. Looker Embedded
Looker Embedded Analytics is a robust solution offering seamless integration of data visualizations into applications and workflows. However, organizations should consider some challenges. The platform can be cost prohibitive for smaller businesses, with licensing and implementation expenses potentially outweighing the benefits for limited use cases. Its reliance on LookML for data modeling introduces a learning curve, especially for teams accustomed to SQL-only environments. Performance issues, particularly with complex queries, and relatively limited customization options for visualizations can restrict flexibility. Additionally, as part of Google Cloud, Looker may create integration hurdles for companies using other cloud providers.
Core features
Connectivity with Google Cloud ecosystem: Integrates with Google Cloud products and the ecosystem, including Google BigQuery.
Dashboards and reporting: Embed dashboards and reports into your applications.
Data modeling: Model your data models with LookML, a proprietary modeling language.
Version control: Git integration and version control capabilities for managing embedded deployments.
3. Tableau Embedded Analytics
Tableau Embedded Analytics supports integrating advanced visualizations and dashboards into applications. While it excels in dashboard aesthetics, organizations may encounter performance challenges with large datasets, leading to slow load times due Tableau’s reliance on data extracts with their in-memory engine. The tool's steep learning curve around visualizing data and creating dashboards can pose difficulties for teams without prior Tableau expertise, often necessitating specialized training or resources. Additionally, the pricing model is opaque and can become prohibitively expensive as usage scales, making cost predictability a challenge.
Core features
Embed visualizations and dashboards: Customize advanced visualizations and dashboards to reflect your brand’s identity.
JavaScript and REST APIs: Utilize APIs to extend Tableau’s Embedded Analytics within your application.
Governance and security: Implement role-based controls and configure permissions to safeguard sensitive information.
Cached, in-memory data: Import your data into Tableau data extracts to enhance load times for your embedded dashboards and visualizations.
4. Sisense
Sisense is a capable platform for integrating dashboards into applications, but potential adopters should weigh a few considerations. While it offers some low-code and pro-code flexibility, embedding dashboards often require significant developer expertise, particularly in managing APIs and data modeling, which can complicate deployment. Challenges with cube management have also been noted, impacting ease of use for large-scale data handling. Pricing is another popular concern, with a lack of transparency requiring negotiations that often reveal high costs relative to alternatives. Additionally, the mobile experience is less intuitive than expected, with navigation and filtering features lagging behind modern standards.
Core features:
UI customization: Refine your dashboard’s interface with branded elements to deliver a consistent user experience.
Code-first analytics: Create dynamic queries, charts, and filters directly from your application code.
Deployment options: Embed analytics into your applications through different methods, such as iFrames, Compose SDK, or Sisense JS.
5. Qlik Embedded Analytics
Qlik Sense Embedded Analytics offers self-service dashboard capabilities and advanced data exploration, but potential adopters should consider some key limitations. While it handles moderate workloads well, it can struggle with performance when processing large datasets, due to Qlik’s reliance on its in-memory engine to power their apps. The platform’s responsive design, although intended for versatility, can lead to inconsistent visual layouts, requiring complex workarounds that may not align with branding goals. Finally, while the base pricing is reasonable, the need for extra payments for add-ons and advanced product features may inflate costs, particularly for larger or more customized implementations.
Core features
Multiple data integration connectors: Combine data from multiple sources, including cloud databases and spreadsheets.
Low-code workflows: Embed dashboards and visualizations using a low-code interface.
Developer libraries and command-line tools: Build and embed custom visualizations using JavaScript, custom libraries, and command-line tools.
Cloud support: Manage and scale your data and infrastructure with cloud compatibility.
6. Microsoft Power BI Embedded
Microsoft’s embedded analytics tool allows organizations to integrate Power BI’s analytics and reporting capabilities into their applications, products, and services. It provides customer-facing dashboards, an open-source integration system, and drag-and-drop functionality. Businesses can use the tool to design dashboards, reports, and visualizations that have the same look and feel as the rest of the application. Although Power BI Embedded is popular among technical data teams, for beginners, Power BI can be quite challenging to understand. The steep learning curve around its user interface also makes it less accessible to business users and front-line decision-makers.
Core features
Custom-built visuals: Allows brands to embed custom visualizations and dashboards.
APIs and SDK support: Enables integration with other applications and allows users to design tailored dashboards.
Data modeling: Shape, clean, and define data relationships, and create measures with Power Query, Power Pivot, and DAX.
Connectivity with Microsoft ecosystem: Integrates with other Microsoft tools such as Azure, Excel, and SharePoint.
7. Domo Everywhere
Domo Everywhere provides a straightforward way to embed dashboards and unify data from multiple sources. While its performance is adequate for smaller workloads, handling large datasets or complex queries is reported to be sluggish, making it less ideal for real-time or high-demand environments. Customization of dashboards and visualizations is limited compared to alternatives, which can hinder users needing tailored solutions. Additionally, the consumption-based pricing model is opaque and can result in unexpectedly high costs as usage scales.
Core features
White-label dashboards: Customize the look and feel of dashboards and reports to match the style of your existing application.
Library of B2B app connectors: Load data from various B2B applications into Domo for analysis.
Embed dashboards and reports: Create custom dashboards for different user groups and allow them to explore data.
Build custom interactions: Use Domo Bricks to set up custom interactions and trigger actions.
8. Amazon QuickSight Embedded
Amazon QuickSight provides an embedded analytics solution for integrating data visualizations within applications, especially for businesses already using AWS. However, several factors warrant careful consideration. The platform's visualization options are limited, lacking advanced chart types which can constrain advanced analytics needs. While the SPICE engine delivers fast query performance, its manual or event-driven refresh processes add complexity to real-time analytics setups. Mobile responsiveness is another drawback, as dashboards often lack full functionality and interactivity on smaller screens. Customization capabilities, though present, may feel restrictive for organizations requiring highly tailored embedded solutions. Additionally, reliance on AWS for seamless integration might create hurdles for businesses operating in mixed or non-AWS environments.
Core features
Cached, In-memory data: Load your data into the SPICE in-memory engine for rapid queries.
Dashbboards and reports: Add QuickSight’s visualizations and dashboards into your existing applications, portals, and services.
QuickSight Q embedding: Embed QuickSight’s AI features to allow users to explore and interact with data.
Authoring experience: Build custom dashboards in alignment with your brand’s identity.
Serverless architecture: Scale your analytics workflow with QuickSight's serverless architecture.
Whether you’re a SaaS company looking to reduce customer churn or an e-commerce company building an application to avoid stockout costs, embedded analytics platforms offer organizations the opportunity to leverage data within their applications to delight customers and users while creating new paths to money. With a variety of options available in the market, choosing the right embedded analytics platform requires careful consideration of several factors. Here are five key steps to follow to help you choose the most suitable platform for your business:
1. Assess your business needs
Begin by assessing your organization's specific needs and requirements. Consider the type of data you work with, the complexity of analytics required, and the intended audience for the embedded analytics, including whether this is for internal users, or something that will be used by external users like customers and partners. Determine the specific goals you want to achieve with the platform, such as improving operational efficiency, enhancing customer insights, or optimizing decision-making processes. This assessment will help you align the features and capabilities of the platform with your unique business objectives.
2. Evaluate integration capabilities
Ensure that the embedded analytics platform seamlessly integrates with your existing applications and technology stack. Assess whether the platform supports integration with your data sources, databases, APIs, and programming languages. Consider the ease of integration and the availability of resources and documentation provided by the platform vendor. A robust integration capability will enable smooth data flow and ensure compatibility with your existing infrastructure.
3. Consider scalability and performance
Scalability and performance are crucial factors to consider when choosing an embedded analytics platform, especially for new products where you don’t know how many users will eventually be leveraging your service. Evaluate the platform's ability to handle large volumes of data and support a growing number of users without requiring a similarly large investment of resources. Consider factors such as response time, query performance, and data processing capabilities. It is essential to choose a platform that can scale with your business needs and deliver fast and reliable analytics performance, even as your data and user base grow.
4. Analyze data governance and security features
Data governance and security are paramount in today's data-driven landscape. Analyze the security features offered by the embedded analytics platform. Look for features such as data encryption, user authentication, and role-based access controls to protect sensitive data. Assess whether the platform complies with relevant industry regulations and standards, such as GDPR or HIPAA, depending on your industry requirements. Additionally, consider features that support data governance, audit trails, and data privacy to ensure compliance and maintain data integrity.
5. Review support and training options
Evaluate the support, community, and training options provided by the embedded analytics platform vendor. Determine whether they offer reliable technical support, documentation, and resources to assist with implementation and troubleshooting. Consider the availability of training programs, tutorials, and user communities that can help your team effectively utilize the platform's features. A vendor that offers comprehensive support and training will facilitate a smoother implementation process and enable your team to make the most of the embedded analytics platform.
6. Ease of use
The end goal of embedding analytics is to make your products more efficient and valuable to users. However, most embedded analytics tools require technical skills for data exploration and visualization that inherently limit the potential pool of users. Instead, look for embedded analytics platforms that deliver a modern, self-serve analytics experience that’s as easy to use as your favorite consumer app.
For instance, ThoughtSpot’s AI Analyst, Spotter, allows users to simply ask their data questions in natural language, receive personalized visualizations and insights, and engage in multi-turn conversations. This effortless experience enhances understanding and facilitates more informed decision-making.
While there are so many modern-day embedded analytics platforms, selecting the right fit has become harder than ever before. With the acceleration of AI developments, it is clear that business users will not be satisfied with basic dashboards or static reports. They need self-serve, personalized analytics that break down silos and deliver tangible value. That’s why the world’s most innovative companies trust ThoughtSpot to fuel innovation and data-driven decision-making.
Experience the power of AI-Powered Analytics and empower your customers to tap into the value of their data. Take a product tour today and see for yourself how easy it is to get started with embedded analytics.
What are the top embedded analytics tools?
Recognized as a leader in BI and analytics, ThoughtSpot Embedded empowers organizations to embed AI-powered analytics directly into applications, making data accessible and actionable. Even world-class customers like MDaudit and Accern trust ThoughtSpot Embedded to deliver powerful analytics to their customers.
How do you choose an embedded analytics solution?
To select the right embedded analytics platform, identify the specific challenges your customers encounter within your existing system. Then, start accessing different platforms based on their integration capabilities, scalability, pricing models, and customization options. Finally, request demos or trials to conduct a hands-on evaluation and ensure the solution meets your needs effectively.
How does embedded analytics software differ from traditional analytics tools?
Embedded analytics software stands out from conventional analytics tools by seamlessly integrating into existing applications, offering users comprehensive analytics capabilities directly within their workflow. Unlike standalone tools, embedded analytics enhances the user experience by providing contextual insights and data visualizations without requiring users to switch contexts.
