Talk to Your Data
Introduction
It's easy to think of Large Language Models (LLMs) as merely stand-alone chatbots like ChatGPT, which excel as consumer-facing products. However, LLMs offer far more value to your business than just being standalone consumer applications. LLMs can integrate various tools making them a powerful resource for increasing operational efficiency and providing novel services to clients. One such use case is automating data analytics and reporting quickly and easily for the user.
Automating Data Analytics with LLMs
Many developers already leverage LLMs to write code, and similarly, LLMs can also generate database queries. By using LLMs to convert natural language questions into SQL queries, businesses can retrieve relevant data and produce insightful visualizations effortlessly. For instance, imagine you are a sales executive who wants to quickly view the Q4 sales in a particular region for a specific product. Traditionally, this would require writing a complex database query, exporting that data, and finally creating a plot to represent the information. With the power of LLMs, we can automate this process, enabling users to simply ask a question like, “Show me the fourth quarter sales in the NorthEast region for Product Zeta.” A chatbot can understand that question and present a relevant data table and visualization based on the company’s actual data.
How a Proprietary Data Analytics Chatbot Works
Database Context Loading: The LLM is loaded with the context of the database structure, including table names, column names, and descriptions of what the columns represent.
Natural Language to SQL Conversion: Once the user inputs their question, the LLM converts it into an SQL query.
Query Execution: The generated query is run against the database in the backend, returning the relevant table of results.
Visualization Code Generation: The result table is presented to an LLM with the context for taking the table's contents and writing code to produce a data visualization.
Visualization Execution: The visualization code is executed on the backend, returning the visualization.
Result Presentation: Both the table and visualization are presented back to the user, along with any insights the LLM found.
Iterative Analysis: The user can ask follow-up questions about the data or request the chatbot to conduct another analysis.
Benefits and Applications
This natural language data analytics tool can save time for executives who need to access critical data insights quickly. It also benefits businesses with data-heavy products, enabling them to create more user-friendly interfaces for interacting with their data, such as cybersecurity scanning software.
Conclusion
By integrating LLMs with data analytics capabilities, businesses can unlock new levels of efficiency and provide innovative services to their clients. At Delphi Intelligence, we're excited to explore how these cutting-edge LLM applications can be tailored to meet the unique needs of various industries and use cases. If you have any questions or would like to discuss how LLMs can be implemented to optimize your business processes, we invite you to contact us and schedule a consultation. We're here to help you harness the power of LLMs and propel your organization into the future. Don't forget to sign up for our blog to stay informed about the latest AI advancements!
