The Marriage of LLMs and BI to Empower Business Users

April 16, 2024 | Data Management | Slavko Kastelic

In today’s data-driven world, businesses striving to stay at the forefront of their industries recognise the critical need to analyse and interpret massive volumes of data effectively. Integrating large language models (LLMs) with business intelligence (BI) systems is revolutionising this arena, proving to be a formidable force in enhancing business operations and decision-making.

The Synergy Between AI and BI

On their own, LLMs and BI are potent tools. BI systems enable strategic decision-making through data analysis, while LLMs, such as OpenAI’s GPT models, bring capabilities to process and understand unstructured data in a human-like manner. When AI meets BI, the fusion accelerates complex processes, deepens insights, and allows businesses to adapt quickly to market dynamics.

This synergy is particularly transformative in handling both unstructured and structured data. By integrating GPT-like models with BI systems, businesses can leverage LLMs to generate complex queries for BI systems and interpret their results. This enhances traditional BI functionalities, making data interactions more dynamic, conversational, and intuitive, which in turn significantly improves accessibility and decision-making processes.

AI and BI

Enhanced Data Querying

LLMs excel in understanding and generating human-like text responses. In a BI context, this translates to creating SQL queries from natural language inputs. For example, a business analyst might inquire about sales figures for a specific product, and the LLM will process this request, craft the necessary query, and retrieve the desired data from the BI system, all with minimal user input required.

Dynamic Interaction with Data

The integration allows users to interact with data systems through natural language, eliminating the need for intricate query writing or deep familiarity with data schemas. This makes data analysis more accessible to business users across the organisation, from top executives to non-technical staff, democratising data access.

Streamlined Data Access

Automating query generation with LLMs reduces both the time and effort needed to access information, a boon for fast-paced business environments where quick data retrieval is pivotal to timely decision-making and strategic planning.

Improved Decision Making

Post-query, LLMs can analyse the results, summarise findings, or even create visual data representations. This step is crucial as it transforms raw data into actionable insights, simplifying interpretation and aiding direct application in decision-making processes.

Visualisation and Reporting

Beyond data retrieval and interpretation, LLMs assist in visualising data. LLMs can suggest or even generate graphical representations of data—like charts and graphs—based on the analysis needs, which can be directly incorporated into reports or dashboards. This capability can significantly enhance the presentation and comprehension of complex datasets, making it easier for stakeholders to understand trends, patterns, and anomalies.

Challenges and Considerations

Despite the benefits, integrating LLMs with BI poses some challenges:

Accuracy of Generated Queries:

The LLM must be finely tuned to understand the context accurately and generate error-free queries.

Security and Compliance:

Using AI to generate queries involving sensitive data introduces risks related to data security and regulatory compliance.

Integration Complexity:

Integrating LLMs with existing BI and data warehouse systems can be complex and may require significant customisation and configuration.

Future Prospects

The future of AI and BI integration is promising. As AI technologies evolve, we anticipate advancements in predictive analytics and conversational AI interfaces, potentially leading to more interactive, voice-driven analytics platforms.


Integrating LLMs into BI systems marks a significant advancement in rendering data-driven insights more accessible and actionable. Businesses enhance their data querying, interpretation, and visualisation capabilities by marrying natural language processing with data warehouses. This streamlines decision-making and democratises data access across the board, setting the stage for the next generation of intelligent, AI-driven analytics tools.

One of the leading solutions in this field is MicroStrategy AI, known for its deep and comprehensive integration of BI and LLM technologies, exemplifying the powerful combination of these two domains. You can find more about it in this blog.

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