Embedded analytics powers data experiences inside the tools we use every day. Dashboards and visualizations are placed directly within these systems, allowing users to access insights without jumping between platforms.
“Embedded analytics allows an analytics platform or product to be added inside a host application,” explains Avi Perez, CTO of Pyramid Analytics. “The platform can be quite sophisticated, involves a huge number of capabilities and functions, and ultimately allows the user to visualize or ask questions through a point-and-click interface while working within the host application.”
But even with this progress, one critical barrier remained: not everyone knows how to ask the right questions in the language of data.
The next big evolution is transforming how users interact with the data itself. And that evolution is already here, in the name of natural language interfaces. Instead of learning how to query or filter data, embedded AI analytics chatbots allow non-technical users to ask questions in plain English and get instant insights right where they work.
Let’s find out how we got here, and why natural language interfaces are among the biggest developments in modern data analytics.
The Evolution from Dashboard to Dialogue
Analytics has come a long way from the days of static reports, offering only a snapshot of performance for a given time period. On top of being restricted by time, they also required technical know-how to generate and interpret the data.
Next came interactive dashboards, revolutionizing the experience by letting users explore data visually and in real time. Embedded analytics took this to a new level by integrating dashboards directly where users work.
But the technical barrier was still there. Users had to know how to slice the data, apply filters, and then interpret the output. That is why natural language interfaces are so exciting. They allow every user, regardless of technical fluency to meaningfully interact with data.
Why Natural Language Changes the Game
Instead of complex SQL queries, AI chat allows users to simply type, or even speak, requests like, “Show me last quarter’s revenue by region,” and get instant, insightful answers. The user can then follow up with further questions, turning the embedded analytics solution into a conversational partner.
“When you’re inside an application and you’re using it, it’d be very, very useful if you have the matching analytical components appearing side by side with whatever you’re looking at, at the same time,” explains Perez.
NLIs not only bring analytics directly into the flow of work, whether that’s within a CRM, ERP, or project management tool, but they also allow users to do so naturally by asking questions and engaging with context-driven AI in real time.
This is not only much more convenient, but also way faster. Without switching between platforms, or applying endless filters to get to what they’re actually looking for, users save valuable time and can focus their brainpower on decision making rather than migrating datasets, navigating dashboards or writing queries.
Shifting from Data Access to Decision Intelligence
One critical distinction between traditional embedded analytics and natural language interfaces is that the latter doesn’t just present data. The AI models interpret it, and provide additional explanations, and even recommendations to aid in decision making.
While traditional dashboards answer the “what,” generative business intelligence engines go a step beyond to answer the “how.” If a user asks a question like “Why did revenue drop last quarter?” the AI model will analyze the underlying trends, product performances, or external factors, and provide a clear narrative behind the numbers.
This transforms modern analytics from a tool for reporting into a proactive decision intelligence layer that everyone in the organization can access.
The Integration Reality (and How to Overcome It)
One hurdle that’s stopping a widespread adoption of natural language interfaces across organizations is the complexity behind making them work. They require tight integration across different technical layers, including front-end frameworks (React, Angular) and back end systems including APIs, permission systems, and semantic layers.
The integration complexity raises challenges around performance and scalability. Perez warns that “when it’s an in-house tool, you can control the client environment. When it’s customer facing, you don’t know what kind of a device they’re using, what kind of a browser they’re using, and suddenly scaling and performance is a lot more of an issue.”
To overcome these challenges, organizations must take a modular, API-first approach to building embedded analytics. This means separating the intelligence engine from the visualization and data layers so each can scale independently. With microservices and containerization, organizations can seamlessly deploy, update, and scale individual components without disrupting the entire system.
Final Thoughts: Embedded Analytics in the Age of AI Conversations
The world as a whole has entered the era of AI conversations. We are using LLMs to optimize our diet plans, brainstorm ideas, and even as a way to learn. It’s only natural that chatbots start integrating with business intelligence and analytics platforms.
The combination of AI and analytics transforms how humans engage with information. Now, every user can meaningfully explore data, with the same ease as chatting with a friend. With such accessibility, data-driven decision making can have a profound impact on how organizations operate and innovate.
