The Best Embedded Analytics Strategy Isn’t Just Build or Buy

Description

Should you build your analytics from scratch or buy an embedded solution? This question has defined countless roadmap debates inside SaaS product and engineering teams. But the most effective teams aren’t choosing just one—they’re combining both.

In this session, Ryan Dolley breaks down the real-world trade-offs between building and buying embedded analytics, and shows how modern teams are blending both approaches to deliver value quickly and stay flexible long-term. Plus, we’ll look at how AI is changing the equation—introducing new possibilities for automation, contextual insights, and personalized user experiences when layered on top of a composable analytics foundation.

You’ll walk away with a clear understanding of when to build, what to buy, how to future-proof your stack—and how to make space for AI without starting from scratch.

What You’ll Learn:

The true pros and cons of building analytics in-house, from customization and control to hidden costs and maintenance debt.

What embedded analytics platforms do well, and where they tend to fall short (and how to work around it).

How to build on top of embedded solutions to get a native UX without starting from zero.

Why hybrid, composable architectures are becoming the default choice for high-performing SaaS teams.

How AI is enhancing analytics delivery through conversational interfaces, smart search, and automated insight generation.

A decision framework for evaluating your product’s current and future analytics needs, including readiness for AI-driven features.

Real-life examples of how companies have successfully combined embedded tools with custom development to speed up delivery and reduce complexity

If you’re responsible for delivering analytics in a SaaS product—whether as a feature, a platform capability, or a competitive differentiator—this session will give you a clear, practical perspective on your options. Join us to better understand the trade-offs, see what’s possible with AI, and chart a path that fits your team, your architecture, and your users.