The landscape of data analytics has evolved rapidly over the past few years. With the shift to SaaS and product‑led growth, customers expect insights to be part of the product rather than an add‑on. Traditional BI tools still play an important role in generating reports and dashboards, but modern buyers increasingly look for embedded analytics —analytics delivered directly inside their applications. Building analytics into the workflow not only delights users but also helps vendors differentiate in a crowded market. This cheat sheet helps you understand key differences and know when each approach fits best.
Defining the approaches
Traditional BI – centralizes data and delivers dashboards through standalone tools. These platforms excel at consolidating information from multiple sources and producing standardized reports for executives, but they require users to leave their workflow to explore insights. Because IT often owns data modeling and access, turnaround times can be slow. See our explainer on operational analytics, self‑service BI and embedded analytics for a deeper dive.
Self‑service BI – exposes dashboards and ad‑hoc analysis tools to business users without requiring SQL knowledge. Empowering non‑technical users can improve agility, but adoption is often limited because users still need to open a separate portal and sift through dozens of reports to find answers. The context switch interrupts their flow, making these tools less sticky.
Embedded analytics – surfaces only the metrics and visualizations relevant to the current task, directly within the application. Instead of presenting a sea of dashboards, embedded analytics offers in‑context KPIs, charts and even conversational insights right where decisions are made. Because it reduces context switching and matches the user’s workflow, customers engage with it more often. Learn more about how this approach increases adoption in our business benefits overview.
Why embedded analytics shines in 2025
The context in which analytics is consumed matters. In 2025, users expect answers at their fingertips rather than hidden behind a login. Embedded analytics provides several advantages over traditional BI in this environment:
Higher user engagement – placing insights where users work increases consumption and eliminates “dashboard fatigue.”
Faster time to decision – no need to export data into separate tools. Relevant KPIs are displayed in context.
Better monetization – customers are willing to pay more for products that offer built‑in intelligence. See the ROI stats in our business benefits article.
When traditional BI still matters
Traditional BI isn’t obsolete. It shines in situations where analysts need to slice and dice large amounts of data, build custom reports and perform deep explorations across the entire organization. Enterprise data warehouses, regulatory reporting and quarterly board decks still benefit from centralized BI tooling. For everyday operational decisions, though, embedded analytics offers a smoother experience because it removes friction and surfaces the right information at the right moment.
To explore the differences further and understand how these tools fit together, check out our article on BI vs embedded analytics.
Choosing the right approach
When evaluating analytics solutions in 2025, consider your audience and their workflows. Do they need a full BI suite for complex analyses, or are they frontline users who require quick answers inside the app? Often, the best solution combines both worlds: a robust BI platform for power users and embedded analytics for end users. As your product evolves, revisit these choices regularly to ensure the analytics experience stays aligned with your customers’ expectations.
