Embedded analytics has finally turned the corner
What did my chat with Ridge AI's CEO and co-founder, Ellie Fields, tell me?
“Embedded Analytics” has been a clarion call for as long as I’ve been in this field. It always seemed to me to be a technology “just around the corner”. Have we turned that corner now, in 2026?
I was joined by Ellie Fields on The AI Analyst (LinkedIn, YouTube) to hear her thoughts on the latest in embedded analytics and why she’s building Ridge AI.
Before we get to Ellie and Ridge AI, here are my up and coming livestreams. Do join me!
Chart Chat, Monday Aug 3, 11am EDT: we’ll look at how data teams have mapped air quality and wildfires around the world in this period of exceptional heat. Sign up here.
“The AI Analyst?”, Thursday, August 6, 4pm BST: Enrico Bertini and I will be discussing how we’ve used LLMs and GenAI to create data viz teaching assets, and lessons we’ve learnt.
Here are three big takeaways from our chat. I’ll share my own thoughts at the end.
Embedding is still a holy grail
“If you give people data where they’re working, that’s helpful,” said Ellie. Embedded has been a goal as long as IFRAMEs have existed but it’s still not reached its potential.
Here’s how Ellie explains the benefit of embedded analytics:
Embedding technology has matured. It’s ready!
One of the problems of embedding has been the technology. Users of BI tools are simply not patient enough to wait 10, 15, 20 seconds for a display to load. Below, Ellie explains how the technology is finally ready to provide millisecond responses.
Pricing AI analytics is evolving
Pricing a BI platform is harder than ever. Do you price by user? Compute? Something else? It’s a big topic in the industry.
Here, Ellie explains why they’ve chosen to price per “Ridge” (embedded asset) you create:
My reflections
At Tableau, it seemed that every year, in our company kick offs, we’d have leaders from the product and the sales teams say, “This is the year of embedded analytics.” And it never seemed to take off.
The key problem, as I saw it, was the dreaded spinning wheel you’d see while waiting for a view to render. If a user actively logged on to Tableau server, then it was just about accepted that you might wait for a view to load. Not ideal, but a price of using a BI platform.
But if you’re coming to my platform, and an embedded element takes an age to load, you’d judge me, not the underlying embedded tech.
For that reason, it’s the matured technology aspect that wins the day for me. Reducing friction has to be a prime objective for any vendor.
What do you think? Are you ready for embedded analytics yet? What are your experiences with embedded?
Chart Chat, Monday Aug 3, 11am EDT: we’ll look at how data teams have mapped air quality and wildfires around the world in this period of exceptional heat. Sign up here.
“The AI Analyst?”, Thursday, August 6, 4pm BST: Enrico Bertini and I will be discussing how we’ve used LLMs and GenAI to create data viz teaching assets, and lessons we’ve learnt.
Take care and stay safe everyone.
Andy