Building effective data products is a cornerstone of delivering value in the digital age. 

I’m Uri Bahar, Director of Solution Engineering at Sisense, and throughout my career, I've encountered the build-versus-buy dilemma time and time again—especially when it comes to embedded analytics. 

In this article, I’ll share insights into the critical considerations for building or buying an embedded analytics solution, the expectations users have today, and how to approach the decision based on your business maturity, data strategy, and goals. 

Understanding the current tech landscape

To set the stage, let’s take a look at the current tech landscape from two perspectives:

  1. User expectations: What do users expect when it comes to digital experiences? We’ll explore a list of these expectations, many of which won’t be surprising, but it’s important to align them with trends in embedded analytics.

  1. The importance of embedded analytics: Why is it crucial to integrate analytics into your product offering? We’ll examine how this capability influences user satisfaction and product success.

By marrying these perspectives, we can better understand why embedded analytics is a critical component of modern products. This understanding also helps frame the internal discussions around whether to build or buy—and if building, to what extent.

In the sections that follow, I’ll delve deeper into these considerations and offer practical insights to guide your decision-making process.

Defining embedded analytics

To start, let’s clarify what embedded analytics means—and, importantly, what it doesn’t. When we talk about embedded analytics, we’re not referring to the analytics you use internally to track product usage or performance. 

Instead, we’re talking about the analytics you offer to your customers, typically within the context of a B2B product.

Even in B2C contexts, embedded analytics exists—for instance, when Spotify sends you an annual recap of your listening habits. While monetization may not always be as direct in B2C, in B2B, analytics often comes with clear monetization opportunities.

Take the example of an application designed for sellers on Amazon or other online marketplaces. Sellers manage product catalogues across platforms, dealing with complex data: product placement algorithms, image requirements, shipping logistics, and more. 

Such applications often provide analytics to help sellers make data-driven decisions, like adjusting prices to improve product rankings or deciding which products to promote.

Embedded analytics, at its core, is about enabling your users to make these kinds of decisions based on the data your application gathers. It’s not just about presenting data—it’s about helping users act on it seamlessly within their workflow.