Embedded analytics transforms where and how data is used for decision-making, allowing you to integrate data visualization, reporting, and analytical capabilities directly into applications. By combining operational and analytical systems, internal and external users alike can go from insight to action quickly.

Embedded analytics solutions offer several benefits:

  • Enhanced User Experience: Access insights without context switching.
  • Real-Time Insights: Provide current information, enabling faster, more informed decisions.
  • Increased Engagement: Boost user engagement and satisfaction.
  • Competitive Advantage: Differentiate product offerings with built-in analytics tools.

In this blog, we’ll examine the different ways to create seamless embedded experiences that cater to different business and technical requirements.

Hosted Reports

Even a small business using a simple accounting package expects to have reports of some kind available in the application. As consumers, we have similar reports available in credit card statements or utility bills: the principal is the same. These reports may be hosted on a separate page or tab of the application. Today, they are increasingly graphical and have simple capabilities such as sorting and filtering: not quite analytic, but not merely static printouts either.

In the enterprise, a key benefit of embedding a reporting platform is simply consistency across the organization. For users who work with business reports in other contexts, such as managers, the embedded versions will appear familiar and easy to navigate. Nevertheless, canned reports offer very limited insight: convenient and easy for sure, but that’s about it.

Business Intelligence Applications

There are now many BI applications on the market ranging from simple visualization tools to complete analytics packages with statistics, predictive analytics, and AI augmentation. Many of these tools have some facility for embedded, with some important differences depending on their architecture. In general, the key components of an embedded solution are the embedded presentation layer, the analytics engine, and the data sources.

Each of these can be developed and deployed independently, but in some approaches your architectural choices will appear limited. Especially in the most sophisticated packages, the analytics engine may be tightly coupled with the presentation layer as a legacy of an older architecture.

There are some advantages to embedding BI applications, especially for organizations with IT-provisioned BI already widely deployed: familiarity and ease of use due to existing user proficiency with the BI system, dynamic and interactive analytics capabilities, and consistent data reporting across the organization. Centralized management might also simplify governance and compliance.

This approach may also look cost-effective in theory, leveraging existing investments rather than requiring new development, but in practice the cost advantage is limited because the embedded solution will involve both new work and new deployment. Licensing is rarely the most significant cost in any system.

Within this approach, there are numerous options for embedding, including the following:

  • IFrame Embedding: This is one of the simplest ways to integrate analytics into an application. It involves embedding an analytics dashboard or report within an inline frame (iframe) on a webpage or application. Developers find this method relatively easy to implement as it doesn't require extensive coding or deep integration with the application's infrastructure. But the main limitation is that iframes remain somewhat inflexible. They often only provide a "window" into a separate system, which does not provide the best user experience or good interactivity with the host application.
  • API-Based Integration: Another approach is to use APIs to integrate analytics directly into the application. The analytics platform exposes APIs to allow the operational application to request and receive data and visualizations. In particular you may develop a look-and-feel which is more integrated with the workflow of the host application. But of course, this requires technical expertise and development effort. The integration needs to be maintained as both the analytics tool and the operational application evolve. It’s also worth noting that not all BI platforms have good APIs. Some of your favorite features in the platform may not be well supported, if at all.
  • White-Labeling: This approach involves rebranding an existing analytics solution while shipping it with the host application. The embedding may use one of the techniques described above, or the applications may just sit side-by-side. White labeling offers a way to provide analytics services under the host application's branding. It's also useful for software vendors wanting to offer analytics as part of their own product suite.
  • Embedded BI Platforms: Some BI platforms have been designed from the ground up to be integrated into other applications. These platforms provide comprehensive analytics capabilities, including data visualization, reporting, and dashboards which can be embedded with sophisticated tools for integrating both workflow and look-and-feel.

Embeddable Analytic Libraries

Pre-built, reusable components can provide developers with an efficient way to build analytic features into operational applications without the need to code common features from scratch. Analytics libraries may include data visualizations, predefined metrics, predictive models, interactive tables, report formats and many other useful objects. These libraries facilitate rapid development and deployment, thanks to their pre-built, ready-to-integrate components.

Most importantly component libraries enable the most complete integration of operational and analytic workflows. However, unlike a complete BI platform, component libraries may not have their own analytic or semantic engine. This may sound like a significant disadvantage, but in practice it affords some new opportunities.

Remember first that the tight coupling between the analytic engine and the presentation layer is exactly what makes embedding BI platforms so frustrating for developers. Also, the analytics engine of a classic BI platform may exceed the simpler requirements of an embedded scenario. Fortunately there is another approach. A headless analytic engine, such as a universal semantic layer, decouples the presentation layer from the analytic logic and data engineering.

AI-Powered Chatbots

Perhaps the most modern and user-friendly way to embed insights in an application is through a chatbot. These friendly interfaces used to be limited in functionality because of the difficulty in translating from natural language to data, finding the relevant answers in a knowledge base, and then delivering the details back in natural language again.

However, with the availability of ChatGPT in 2023, the experience of chatbots has changed forever. Now we can reasonably expect to interact with natural language and get back genuinely relevant insights which may be in natural language, or with multi-modal AI, may be new datasets or visualizations.

The challenge for developers embedding an AI chatbot is not the user experience, but rather ensuring that the Large Language Model (LLM) driving the bot has access to the relevant data and context for the business. But developers also need to constrain the LLM to give only the relevant answers and detail. Prompt-engineering can help, but it’s not enough. The universal semantic layer provides the LLM with a model of a world, composed of entities and their measures and dimensions. It’s a powerful way of enabling a chatbot experience that is usable, relevant, and accurate.

Best Practices for Successful Integration

The universal semantic layer provides a flexible foundation for embedded analytics, supporting an open, composable, scalable, and secure architecture for your application, built to your specific requirements. To successfully integrate embedded analytics using the universal semantic layer, consider the following best practices:

  • Define Clear Objectives: Choose your approach. Identify the specific goals you aim to achieve with embedded analytics, such as improving user engagement, providing personalized insights, or enhancing decision-making capabilities.
  • Focus on User Experience: Design the embedded analytics features with the end-user in mind. Ensure that the insights are easy to access, understand, and act upon. Use interactive visualizations to enhance engagement and usability.
  • Unify Data Models: Combine data from various sources into a single, cohesive data model. Provide access to consistent, high-quality data, regardless of its origin so that users can trust the insights they receive, knowing they are based on comprehensive and accurate information.
  • Enforce Data Access Controls: Implement robust data governance practices to maintain high data security, privacy, and quality. Enforce data access controls in the context of the current user or role, delivering analytics based on secure data.
  • Optimize for Performance: Leverage query optimization and caching features of the universal semantic layer to ensure fast and responsive analytics. Regularly monitor performance and make adjustments as needed to maintain a seamless user experience.
  • Plan for Scalability: Plan for future growth by leveraging the scalable architecture of the universal semantic layer. Ensure that your solution can handle increased data volumes and user demands without degradation in performance.

Deliver Consistent Data Anywhere

Embedded analytics has the potential to transform customer experiences, providing real-time, personalized insights directly within applications. Cube’s universal semantic layer is a powerful enabler of this transformation, offering unified and governed data access, optimized performance, scalability, and seamless integration.

By adopting best practices and leveraging the capabilities of the universal semantic layer, organizations can deliver embedded analytics that enhance user satisfaction, engagement, and overall value. Contact Sales to learn more about how Cube can accelerate your solution development timeline.