Will LLMs Revolutionize Business Intelligence?

June 17, 2023

Mieke Houbrechts

Are Large Language Models the future of business intelligence? In this article, you'll learn how LLMs can get you from raw data to valuable insights quicker.

Are Large Language Models the future of business intelligence? Although they won't replace data scientists anytime soon, LLMs can help you get insights from your data much quicker. And for a SaaS product, adding powerful AI could deliver a better user experience to your customers faster. In this article, you'll learn more about LLMs and how they can be useful for business intelligence.

What are LLMs?

A Large Language Model, or LLM, is an artificial intelligence model that has been trained on a huge amount of text data. LLMs will observe patterns in these millions of text data, and generate new text following those patterns. Because these models are trained on such a vast amount of data, the resulting text often looks like a human wrote it.

Think of a large language model as a parrot with unlimited brain capacity. Parrots listen to human speech, and repeat the words and patterns they picked up. Even though they don’t know what these words actually mean (although we can never be 100% sure of that), it learns the patterns and can reproduce them. Likewise, an LLM learns text patterns from reading a vast amount of text data, and mimics them without understanding what it means.

Some of the leading AI large language models are:

  • GPT-3(.5): OpenAI’s large language model that caused a surge in popularity of AI
  • GPT-4: OpenAI’s latest model that is even more powerful
  • BERT: Google’s pioneer Natural Language Processing model
  • PaLM: Google’s latest breakthrough in LLMs
  • LLaMA: a collection of foundational language models by Meta
Schema of how large language models work

Generative AI vs LLMs

You will often hear generative AI and LLM in the same context. But although there is some overlap, they are 2 different concepts.

Generative AI is a type of artificial intelligence model that can create new content. And that means any type of content you can imagine. Text, images, code snippets, videos, artwork, and even music. Generative AI isn’t limited to generating text only.

Large Language Models, however, are a specific type of generative AI that only generate text. No images, diagrams or sound. Still, generating 'text' goes beyond text snippets, articles, emails, and so on. Powerful models like GPT-3 and GPT-4 can understand other text formats, like code in web development or even protein sequences in biotechnology!

In short, LLMs are a type of generative AI, but not all generative AI models are LLMs.

Why use LLMs for Business Intelligence?

The biggest potential of LLMs? Its time savings capacity. After the initial hype of ChatGPT, a lot of people still haven’t realized how much time they can actually save by using LLMs for certain tasks.

“With every hype comes skepticism. But the big difference with LLMs is that there are already tons of use cases that prove the time and efficiency gains are very real. Just take the example of finding protein sequences with LLMs. This R&D process normally takes decades, while now it took mere weeks.”

- Karel Callens, CEO at Luzmo

Likewise, there are a lot of manual, tedious processes in business intelligence, where LLMs can offer a more efficient process and quicker outcome. Even if the outcome isn’t perfect, you will still have saved loads of time.

Our world changes quickly, and LLMs can help us to adapt quicker. But there’s one big catch. LLMs mimick language, so responses aren’t always 100% accurate or reliable.

The solution? Business intelligence.

The reason why BI and LLMs go so well together is because data can validate and back up the claims that large language models make so confidently. So in short:

  • LLMs provide you with a quick answer to your problem or challenge
  • Business intelligence provides you with the right insights to challenge and verify these answers
"Imagine ChatGPT would confidently tell you to cross a busy street without looking both ways, would you do it? Probably not. But if you have the data to back up this claim, showing you the speed and amount of cars and the perfect time interval to cross the street, you would feel much more confident about your decision. Business intelligence can take away the risks of LLMs, and that combination is golden."

- Karel Callens, CEO at Luzmo

3 ways LLMs are shaping the future of BI

The ways you can use LLMs to speed up certain tasks are virtually endless. So it can be overwhelming to come up with good uses for business intelligence. Let’s look at some tangible examples of LLMs at work in business intelligence.

Data enrichment for smarter insights

Imagine you are a brand manager who is monitoring brand mentions on social media. How can you really tell if your brand is growing, when you don’t know if these mentions are positive or negative?

LLMs can help you analyze the sentiment of social media mentions and add that information straight to your dataset. Give it a score from 1 to 5, and you’re well on your way to understanding how people really feel about your brand.

You can do the same with online reviews, or online articles about your industry to spot market trends. In short, LLMs can add richer information to your existing data, without having to spend hours analyzing text snippets one by one.

Saving time on data preparation

Some data scientists would rather watch paint dry than waste time doing data cleaning in Excel. But having correct data in a usable format is one of the essential steps to succeed with business intelligence.

LLMs recognize patterns in your data, so they are great for taking over some of the manual data preparation. Use LLMs to uncover missing data, or flag outliers in your data, write SQL queries, improve your data model,…

Some SaaS vendors are already incorporating the power of these LLMs into their own software products. Softbuilder, for example, launched an AI data modeling tool that creates or updates new data models automatically.

Conversational AI for data exploration

Conversational AI, like ChatGPT, are powerful tools built on top of large language models. And you can use them just as well to explore your data in an easy, conversational way.

Imagine typing “What did our sales figures look like for the past 3 months?” and getting a chart with your revenue evolution in return. In the future, you will see more and more BI tools incorporating these chatbot-like features for data exploration.

As a result, LLMs will make data analysis even more accessible to anyone. Also for those who aren’t skilled in data science or data analysis.

Example of using conversational AI to generate data visualizations

LLM and BI: the golden combo

If you ask us, large language models and generative AI are here to stay. LLMs can help us cope with changing circumstances faster. In the future, BI tools will add powerful AI features to their offering to make data analysis even easier — if they haven’t already done so.

If you’re worried about the accuracy and bias of large language models, you will find solace in data. The combination of automating repetitive work, and having factual data at hand to validate your claims will be gold for a data-driven future.

Curious to experience first-hand how LLMs and BI work together? Try out this AI dashboard builder, and create reporting dashboards on autopilot using ChatGPT (LLM) and Luzmo (BI).

Build your first embedded dashboard in less than 15 min

Experience the power of Luzmo. Talk to our product experts for a guided demo  or get your hands dirty with a free 10-day trial.