Large language models (LLMs) are advanced AI systems designed to process and generate human-like text by training on extensive datasets. They excel in tasks ranging from translation and summarization to answering questions and writing content, effectively simplifying what used to be labor-intensive, complex interactions between humans and machines.

LLMs represent a transformative leap in artificial intelligence, combining vast datasets and sophisticated algorithms to process and understand human language.

Almost 67% of organizations now use generative AI products that rely on LLMs. This wide adoption is driven by the LLMs’ ability to handle complex natural language tasks, enhancing productivity and providing new capabilities across various departments, including business intelligence (BI).

In this post, let’s take a look at three examples of LLM use in BI. But first, let’s understand why LLMs are particularly valuable in the context of BI and data analytics.

Why Use LLMs for Business Intelligence

Put simply, BI is designed to enable data-driven decision-making, and LLMs, by making data handling and interpretation more accessible, have become a powerful tool in this field.

Here are some key reasons why LLMs are becoming indispensable for BI:

• More Efficient Data Handling: LLMs can automate and enhance various stages of data processing, from collection to analysis. This automation speeds up the BI cycle, allowing businesses to respond to market changes more quickly and with more informed decisions.
• Enhanced Analytical Capabilities: By leveraging LLMs, businesses can dive deeper into their data analysis, uncovering patterns and insights that were previously obscure. LLMs can handle complex queries and generate comprehensive reports, providing businesses with detailed and actionable insights.
• Scalability: As a business grows, the volume of data often increases exponentially. LLMs are well-suited to scale with this growth, handling larger datasets efficiently and without the need for proportional increases in human resources.
• Cost-Effectiveness: While the initial setup for LLM integration may require investment, over time, the cost-saving benefits become clear. By automating routine tasks and reducing the need for additional staffing as data volumes grow, LLMs can significantly decrease overall costs.

Speaking about the promise of LLMs in the context of BI, Pyramid Analytics CTO Avi Perez says, “The greatest value is simplifying the interaction between a non-technical user and their data, so that they can ask complicated business questions and get very sophisticated, clean, intelligent answers in response and not being forced to have to ask that question in a particular way, or get a response that is unintelligible to them.”

Examples of LLM Use in BI

Here are three ways LLMs are revolutionizing BI for good.

1. Data Enrichment

Data enrichment is a critical aspect of BI that involves enhancing existing data by appending additional information from external sources to provide a more detailed, accurate view of customers or business operations.

“Using publicly available third-party data (e.g., US Census Bureau or Department of Labor) to enrich enterprise data has traditionally been a manual search and copy-and-paste exercise. LLM-based search can now return such data in a tabular format for cataloging in a BI system,” says Boris Evelson, Vice President and Principal Analyst at Forrester.

That is, LLMs can automatically identify relevant external datasets and integrate them with internal data. For example, market research teams can use LLMs to enhance a customer database by adding social media activity data, demographic information, or economic indicators that provide a fuller picture of the customer base.

Through its deep learning capabilities, an LLM understands the context and semantics of the data. This allows it to tag data with relevant metadata automatically, making it more accessible and useful for analysis. For instance, an LLM can classify customer feedback into categories such as positive, negative, or neutral, and further identify specific themes like service speed or product quality.

Furthermore, by using LLMs for data enrichment, businesses can reduce human errors that typically occur during manual data handling. LLMs ensure that the data remains consistent and accurate across different databases and applications, which is vital for maintaining the integrity of BI insights. 

As the scale of data grows, LLMs efficiently manage the enrichment process without a proportional increase in effort or resources. They adapt to new data types and sources, ensuring that the BI system evolves in line with business needs.

2. Data Cleaning and Preparation

Data cleaning and preparation is a foundational step in BI that involves correcting inaccuracies, filling missing values, removing outliers, and standardizing data formats across datasets. LLMs can significantly streamline and enhance this process through their advanced natural language understanding capabilities.

Here’s how:

• Automated Error Detection and Correction: LLMs can automatically detect and correct common data entry errors, such as typos, inconsistent formatting, and illogical data entries. For example, an LLM might automatically correct date formats that are inconsistent across a dataset or identify and rectify misspelled names in a customer database.
• Handling Missing Data: One of the most challenging aspects of data preparation is dealing with missing values. LLMs can intelligently infer missing information based on the context provided by other data points. For instance, if a dataset of customer information is missing zip codes, an LLM can predict the likely zip code based on city and state information.
• Normalization and Standardization: Ensuring data from different sources adheres to the same format is crucial for accurate analysis. LLMs can normalize data by converting it to a standard format, such as changing all dates to a YYYY-MM-DD format or standardizing address information to facilitate easier comparisons and analyses.
• Data Integration: LLMs are adept at integrating diverse data sources into a cohesive dataset. They can recognize and reconcile discrepancies between similar data from different sources, ensuring a unified view that is critical for effective BI.
• Semantic Enrichment: Beyond simple cleaning, LLMs can enrich data by adding semantic tags or metadata that enhance the data’s utility for analysis. For example, an LLM might categorize company names and associate them with industry tags based on descriptions in their dataset, facilitating sector-specific analyses.
• Efficient Handling of Large Datasets: The scalability of LLMs means they can handle large volumes of data much more efficiently than manual processes. This capability is especially valuable in enterprises where data accumulates rapidly, requiring constant cleaning and updating.

Put simply, by employing LLMs in data cleaning and preparation, businesses can ensure that their BI systems are working with the highest quality data. This not only reduces the time and labor typically involved in preparing data but also improves the overall reliability of business insights derived from BI processes.

3. Data Exploration with a Conversational UI

Data exploration with a conversational user interface (UI) represents one of the most innovative applications of LLMs in BI.

With natural language query processing, LLMs can understand and process queries made in natural language, allowing users to ask questions about their data as they would in a conversation. For example, a user might ask, “What were the total sales last quarter by region?” and the LLM can interpret this query, fetch the relevant data, and present it in an understandable format.

“The power of the large language model engine allows people to talk in very plain, vernacular type language and get a response in the same tone and feeling. And that’s what makes the LLM chatbot so interesting,” explains Avi Perez.

“The integration into business intelligence, or BI, is then very appropriate because, typically, people have a lot of questions around the data that they’re looking at and would like to get answers about it,” he continues. “Just a simple, ‘Show me my numbers,’ all the way through to the more interesting aspect which is the analysis. ‘Why is this number what it is? What will it be tomorrow? What can I do about it?’ So on and so forth. So it’s a very natural fit between the two different sets of technologies.”

Moreover, conversational UIs powered by LLMs can offer dynamic interactions, where the system asks follow-up questions to clarify the user’s intent or to drill down into more specific details. LLMs are capable of maintaining context over the course of a conversation. This interaction mimics a dialogue with a human analyst, making the exploration process more engaging and thorough.

So, by allowing users to interact with data systems via conversational UI, LLMs democratize access to data analytics. This is particularly beneficial for non-technical users who may find traditional data exploration tools daunting. Everyone from executives to sales personnel can directly interact with data to derive insights relevant to their roles.

LLMs can also tailor the analytics experience based on the user’s role, preferences, and past interactions. This personalization makes the data exploration process more efficient and user-friendly, as the system can anticipate the type of analytics or data presentation that the user prefers.

“In the past, each BI vendor used proprietary NLP to convert a natural language question into an SQL query,” adds Boris Evelson. “We believe that most vendors will shift to LLMs for this conversion, creating differentiation by using prompt engineering to tune questions and enrich the question with data and semantic context.”

Wrapping Up

By automating data preparation, enriching data, and facilitating sophisticated data interactions through conversational UIs, LLMs not only streamline analytical processes but also enhance the depth and accessibility of insights. These capabilities allow businesses to operate more efficiently, respond more swiftly to market dynamics, and make more informed decisions.

As LLM technology continues to evolve, expect its integration into business intelligence tools to deepen, further unlocking potential across various job functions and industries, revolutionizing how businesses leverage data for strategic advantage.