MJ Lindeman, PhD, Community Partner
Feb 6, 2025
You've been there: staring at a sprawling dataset, knowing critical insights lie within, but dreading the hours ahead of cleaning, organizing, and coding before you can start your analysis. This preparation consumes valuable time that could be spent on strategic thinking or other things.
Instead of struggling to update your pivot tables constantly, what if you could ask your software questions in natural language and receive intelligent, contextual responses? This isn't a future scenario. For example, doing financial data analysis by large language models (LLMs) can help analysts: “The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle.”
The integration of LLMs into data analysis for any type of data has already caused a fundamental shift. For example, an embedded AI such as Quadratic AI spreadsheet provides capabilities that are not available when using an external LLM for analytics.
The LLM revolution in data analysis
Traditional analysis demands precise syntax, structured queries, and deep technical expertise. Each question requires careful translation into code, and each new dataset presents a fresh challenge of understanding its structure and quirks.
Using LLMs for data analytics can eliminate these problems and pain points because LLMs understand context and natural language. This enables analysts to explore data through conversation rather than code. This transformation goes beyond simple query translation.
The results depend on the AI model chosen for use. Some LLMs are smarter than others. They can understand the intent behind questions, suggest relevant analyses, and even identify patterns that might be worth investigating.
How LLMs interpret datasets
LLMs bring unprecedented sophistication to dataset interpretation. When encountering a new dataset, they can automatically analyze its structure, identifying column types, relationships, and potential quality issues. This goes beyond simple schema detection.
For example, LLMs can understand the semantic meaning of fields, recognizing that "churn_date" relates to customer retention or "LTV" represents lifetime value. LLMs can also be fine-tuned to interpret unique company terminology, understand unusual ways of organizing data, or even reference documentation to determine the best method of analysis.
An LLM’s interpretation can extend to understanding data distributions, identifying primary and foreign key relationships, and detecting hierarchical structures within the data. For example, when analyzing a sales dataset, an LLM can recognize that "product_category" contains broader groupings than "product_name" and automatically suggest appropriate aggregation levels for analysis.
Data quality assessment becomes more intelligent with LLMs. They can identify inconsistencies in formatting, detect outliers that warrant investigation, and suggest appropriate cleaning steps based on the data's context and intended use. This automated interpretation significantly reduces the time needed to understand and prepare data for analysis.
LLM-powered data summarization
LLMs excel at providing meaningful summaries of complex datasets. Unlike traditional statistical summaries, LLM-generated summaries combine numerical insights with natural language explanations, making them accessible to both technical and non-technical users.
LLMs can automatically identify and explain key statistics, trends, and patterns. They might note that "revenue shows strong seasonality with peaks in Q3" or "customer acquisition cost has increased 18% year-over-year." These summaries go beyond raw numbers to provide business context and implications.
Time-series data benefits particularly from LLM summarization. Models can identify trends, seasonal patterns, and structural breaks, explaining them in business terms rather than statistical jargon. For instance, instead of just reporting a correlation coefficient, an LLM might explain that "marketing spend shows a strong positive relationship with sales, but with a two-month lag."
Generating actionable insights
The true power of LLMs in data analysis lies in their ability to generate actionable insights. By combining their understanding of business context with statistical analysis, LLMs can identify patterns and opportunities that might be missed in traditional analysis.
Pattern recognition goes beyond simple correlation analysis. LLMs can identify complex relationships across multiple variables, suggesting potential causal relationships and areas for further investigation. For example, when analyzing customer behavior data, an LLM might notice that "customers who engage with the help center within their first week of signup show 12% lower retention rates."
Anomaly detection and explanation become more sophisticated with LLMs. Rather than just flagging unusual data points, LLMs can explain why they are unusual and suggest possible causes. This might include identifying that "the spike in customer complaints coincided with a server outage" or "the drop in conversion rates appears related to a competitor's promotion."
Most importantly, data analytics LLMs can generate specific, actionable recommendations based on their analysis. These might include suggestions like "consider segmenting email campaigns by user activity level" or "investigate the impact of pricing changes on high-value customer segments."
Quadratic’s AI spreadsheet implementation
Quadratic leverages these LLM capabilities within its spreadsheet environment, combining familiar spreadsheet functionality with advanced AI-powered analysis. Users can type natural language queries and receive not just the results of the analysis, but also an explanation of the process used and suggestions for further investigation. Spreadsheet templates are provided to aid users in specific domains.
The platform's code generation capabilities mean that even complex analyses can be initiated with simple natural language requests. For instance, asking "Show me the correlation between customer satisfaction scores and renewal rates, segmented by account size" automatically generates the necessary Python code, creates appropriate visualizations, and provides interpretable results. In this case, the AI is giving the user a method of analysis that can be adapted within the code, as opposed to a black box answer.
The code can also be taken out of the Quadratic spreadsheet used in other environments. For example, your analysis might need to become part of a larger workflow. Perhaps you developed a market segmentation analysis in Quadratic, but now you want to integrate it with your marketing automation tools.
Business impact
The impact of using an LLM to analyze data across business functions can be transformative because it frees analysts to focus on interpretation rather than mechanics. Product managers can quickly iterate through hypotheses about user behavior, testing multiple scenarios in the time it previously took to analyze one.
Sales and marketing teams gain the ability to analyze campaign performance across channels without technical support, enabling rapid optimization of strategies. Financial analysts can model complex scenarios and identify trends more quickly, supporting faster and more informed decision-making.
The democratization of data analysis using LLMs might be the most significant impact. Team members across the organization can now engage with data meaningfully, regardless of their technical background. This accessibility accelerates decision-making and promotes data-driven transformation throughout the organization.
Future outlook
The evolution of LLM capabilities in data analysis is accelerating, and there is no single best LLM for data analysis. Enhanced multimodal capabilities will allow LLMs to work with diverse data types, including images and unstructured text. Improved causal analysis capabilities will help identify not just correlations but potential cause-and-effect relationships in data.
For businesses, the imperative is clear: early adopters of LLM-powered analysis tools are gaining significant competitive advantages. As these tools become more sophisticated, the gap between organizations that effectively leverage an LLM for data analysis and those that do not will widen.
The future of data analysis is conversational, intuitive, and powered by AI. The tools are available today, and the potential benefits are faster insights, deeper understanding, and more democratic access to data analysis. These are too significant to ignore. The question is not whether to adopt these tools, but how quickly you can integrate them into your analytical workflows to start realizing these benefits.
