Natural Language Processing for BI

Augmented Analytics with ThoughtSpot

In 2020–2021, I served as Lead Solution Architect for an innovative project at large pharma company in  Switzerland that piloted the use of ThoughtSpot, a new-generation analytics platform. The initiative aimed to transform how business users interact with data: moving away from slow, IT-driven dashboard development toward self-service, search-driven analytics powered by natural language and machine learning.

Why It Mattered

At that time, BI users often faced 1–2 week waiting times for dashboard delivery, with limited flexibility for ad-hoc exploration. The ThoughtSpot solution dramatically reduced that gap—allowing business partners, analysts, and product managers to query data on their own and gain insights in seconds.

This experimental deployment was one first steps into augmented analytics, demonstrating how conversational search and AI-driven discovery could empower business decision-making at scale. It showed that even before today’s LLM revolution, enterprises could already leverage natural language and machine learning to unlock the value of their data.

The Value of Natural-Language BI, Then and Now

Empowering Business Users Through Natural Language (Circa 2021)

Back in 2021, ThoughtSpot’s natural-language interface offered a game-changing way for business users to interact with data. Instead of facing weeks-long waits for BI dashboards or writing complex SQL, users could ask questions in plain language—“What were Q2 sales trends in Europe?”—and immediately receive insights. This capability dramatically shortened decision cycles, reduced dependency on central BI teams, and enabled true self-service analytics.

At the time, ThoughtSpot stood at the forefront of augmented analytics, delivering not only natural-language search but also AI-powered insight generation via its SpotIQ engine.

Why This Was So Innovative

Implementing natural language interfaces in BI back then was still relatively novel. Although some vendors—like Tableau with Ask Data—were exploring this space, most BI tools required technical skills or heavy IT involvement. The ability for non-technical users to ask conversational questions and instantly visualize the results was a leap toward democratizing data access.

Fast Forward: How LLMs Are Reshaping BI Today

Fast forward to today—and everything in the augmented analytics world has evolved. Driven by the rise of large language models (LLMs) and generative AI (GenAI), modern BI platforms now offer even richer, more natural, and interactive experiences:

Looking Ahead: What the Future of BI Might Look Like

• Conversational assistants as analysts: Tomorrow’s BI tools may converse—guiding users with clarifying questions, recommending KPIs, or even surfacing insights before a query is asked.

• Domain-specific precision: SLMs tailored to specific verticals—say, pharma or supply chain—can drive more accurate, relevant BI experiences than one-size-fits-all models.

• Seamless multimodal analysis: With models that understand text, visuals, and more—BI could one day allow users to ask questions about a chart and get explanations, edits, or even new visualizations generated on the fly.

In summary, this ThoughtSpot implementation was an early instance of augmented analytics in action—well before the GenAI boom—and set the stage for today’s conversational, AI-first BI experiences. It showcased how natural-language interfaces could empower users, reduce friction, and accelerate insights in a large enterprise setting. Now, with LLMs, RAG, and hybrid architectures, BI platforms are poised to become even more intuitive, proactive, and embedded in everyday decision-making