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White label analytics allows SaaS products to embed dashboards and insights under their own brand, fully integrated into the application. Users interact with data inside the same interface, which improves adoption and keeps workflows uninterrupted. The key difference from basic embedding lies in integration depth. iFrame-based approaches limit customization and create disconnected experiences, while SDK and API-based platforms enable deeper control, better performance, and scalability across tenants. For SaaS teams, this affects retention, monetization, and development speed, turning analytics into a core product capability rather than an external add-on.

A high-impact dashboard UI can be a daunting thing to design. Especially if you don’t know where to start. But following the dashboard design best practices that we list in this article makes the goal achievable.

Even though there are many technologies making embedding analytics into your applications easy, you still need to take the time to ensure you’re picking the best technology for your own unique business use case.

Guided analytics allows a company to set up, with the help of a developer, business applications featuring dashboards and charts that are updated based on user explorations and feedback. The end-user has no ability to create their own data visualizations, bring in one of their own data sources, or build dashboards and reports. Guided analytics BI operations are entirely owned by the IT team or dedicated business intelligence experts.  

In the embedded analytics space, however, you’ll be hard-pressed to find clear, transparent, and publicly available pricing. Most embedded analytics vendors keep their pricing a secret and charge you unpredictable costs like usage and users.

Analytical apps are designed as standalone applications or embedded analytics to simplify access and understanding of data for all users regardless of their expertise. They provide a logical and contextual view of the data that business employees work with.

Predictive analytics in healthcare refers to the analysis of current and historical healthcare data that allows healthcare professionals to find opportunities to make more effective and more efficient operational and clinical decisions, predict trends, and even manage the spread of diseases.  

In this article, we are taking a closer look at two of the most popular embedded analytics solutions on the market – Reveal and Looker, to help you find the best tool for your needs.  

Choosing the wrong embedded analytics platform slows your roadmap, increases dev burden, and weakens the product experience. Many tools check feature boxes but fail on pricing models, embedding control, integration fit, and long-term scale.

Powered by two of the greatest analytics technologies, augmented analytics allows users to ask questions and automatically generate insights by exploring their data using conversational language.