As companies become increasingly data-driven, many are turning to embedded analytics to bring insights directly into the tools their users rely on every day. Rather than forcing people to switch between standalone BI platforms and work operations systems, embedded analytics allows them to access and act on data seamlessly, within the core applications where they’re actually focused on getting things done.
However, choosing the right embedded analytics option goes beyond picking the solution with the flashiest dashboards. To truly assess whether a platform can drive real business impact, frictionless integrations with the company’s existing data stack, and sustained user engagement, organizations must analyze their options based on critical capabilities.
In this article, we’ll explore three essential features you must consider when selecting an embedded analytics platform.
1. Interfaces That Extend Beyond Premade Dashboards
Modern embedded analytics has evolved beyond simply dropping static dashboards into your application. The best solutions now allow teams to embed individual components, whether it’s charts, KPIs, filters, and even conversational interfaces.
By embedding more targeted and versatile components rather than full dashboards, businesses can surface the exact insights users need in the moment. They can engage with specific metrics or drill into individual charts, without being overwhelmed or confined by what comes with navigating a full BI dashboard.
When evaluating platforms, business leaders should look for solutions that enable highly interactive embedding with minimal custom code, while still allowing for some personalization. Close collaboration with data teams is also necessary so that data models, metrics and governance workflows stay consistent across the embedded components.
Pyramid Analytics stands out in this regard by offering lightweight, no-iframe embedding with full interactivity. With the Pyramid Hub tool, data and product teams can embed collages, a combination of charts, KPIs, filters, and fully interactive dashboards, directly into their applications.
The platform even supports voice chat interfaces, so users can ask questions in natural language and receive answers as custom visualizations.
2. Deep Integrations Using Component-Based Embeds
Another key capability that many of the leading embedded analytics platforms offer is the ability to integrate BI functionality directly into the product using component-based embeds. Rather than embedding static iframes, this approach allows teams to embed individual analytics components as reusable UI elements that fully align with the product’s design system and user experience.
With this approach, analytics becomes a true extension of the product, matching the look and feel of the existing interface. Users will find it easier to engage with the data, since it feels like a natural part of their workflow rather than a separate, unfamiliar tool that’s shoehorned in.
Developers will also benefit from accelerated development cycles, as the prebuilt, reusable components reduce the amount of custom code required.
Component-based embeds work well for customer or partner portals that need to display personalized metrics, or even internal applications for employees to get context-aware insights without leaving their workflows.
From a technical perspective, this requires embedded analytics platforms to provide JavaScript or React/Vue component libraries that integrate with modern frontend frameworks. Additionally, APIs for passing user context, such as user IDs or tenant IDs, are needed to ensure that embedded components render user data securely and accurately.
GoodData excels in this area by allowing developers to embed individual analytics components like charts, KPIs, and filters as native UI widgets that integrate directly into React, Angular, and Vue applications. This allows for highly customized, scalable embedded analytics experiences that feel fully native to the application.
3. Native Data Governance and Access Management
Last but not least, strong data governance is a critical requirement for embedded analytics solutions. These platforms handle highly sensitive business and user data, which is highly attractive to cybercriminals. Any gap in security can lead to data breaches, regulatory fines, or customer trust issues.
The key to strong security with embedded is the ability to synchronize the application’s permissions with the embedded analytics access controls by passing user identities or roles (RBAC) via tokens or claims. This allows administrators to centrally manage access while providing a seamless user experience.
Support for row-level security and audit logging is also essential, so users can only access the data they’re authorized to see, while admins have full visibility into who accessed what data and when. Data teams and developers must collaborate to ensure governance and access policies are drafted and enforced across all data sources.
Looker by Google is a great choice for security-conscious organizations with its enterprise-grade governance capabilities, especially if your data resources already live in the Google Cloud ecosystem. It supports dynamic row-level security to enforce granular data access, along with seamless integration with all major identity providers for centralized authentication and authorization.
Final Thoughts
Focusing on delivering the maximum value to end users helps organizations steer their embedded analytics strategy in the right direction. The features we covered all bring true business value by making data more accessible and secure.
To make an informed choice, teams should consider piloting two or three platforms to evaluate how well each solution fits their technical infrastructure, security requirements, development workloads and user experience goals.
Throughout the selection and implementation process, close collaboration between developers and data teams is essential to ensure long-term success and scalability.
