Using Generative AI to Analyze Datasets | Faster, Smarter Data Insights
How Data Analysts Can Use Generative AI and Microsoft Copilot to Transform Excel and Database Workflows
Data analysts today have two powerful options when using Generative AI tools like Microsoft Copilot to work with their datasets—whether the data lives in a Microsoft Excel workbook or a database system:
Analyze data without modifying the source, producing insights, summaries, and visualizations
Analyze and then operationalize findings, applying changes directly to the underlying dataset
Both approaches unlock new possibilities for scalable, automated analytics.
Why Generative AI Is a Game‑Changer for Data Analysis
One of the biggest advantages of Generative AI is how easily it can produce ready‑to‑run source code—including:
This means analysts can instantly convert insights into repeatable, automated workflows. Even better, organizations operating across multiple IT platforms can generate consistent, cross‑platform code that applies the same logic everywhere.
From Sample Data to Enterprise‑Scale Automation
As described in Gen AI TIPs – Using Microsoft Copilot With Microsoft Excel, the examples you’ve explored in Excel are only the beginning. With Generative AI, any insight discovered in a sample dataset can be:
Translated into source code
Scaled to a larger population
Applied across different systems and environments
This makes Generative AI especially valuable for complex analytical tasks, such as segmentation, anomaly detection, and time‑series pattern discovery.
Example: Database Segmentation With Generative AI
For more advanced workflows, consider the approach outlined in your article Database Segmentation – IQR vs. K‑Means Clustering Techniques. That method shows how to segment data on a time‑series basis using statistical outlier detection and clustering.
Generative AI enhances this process by:
Allowing you to prototype segmentation logic in Excel
Automatically generating the code needed to scale it
Applying the segmentation across your entire database population
This bridges the gap between exploratory analysis and enterprise‑level implementation.
When You Only Want Analysis (Not Data Modification)
Not every workflow requires altering the source data. If your goal is to:
Explore patterns
Visualize trends
Build dashboards
Tell a data‑driven story
…then you simply ensure that Copilot or other GenAI tools do not have permission to modify your dataset.
In this mode, Generative AI becomes a safe, flexible analysis environment, ideal for:
Rapid prototyping
Statistical exploration
Scenario modeling
Insight generation
You get all the benefits of AI‑powered analytics without risking unintended changes to your data.
Using Microsoft Copilot to analyze large civic datasets
Key Takeaways
Generative AI gives analysts two modes: analysis‑only or analysis + operationalization.
Copilot can generate VBA, Python, SQL, and other code to automate insights at scale.
Insights developed in Excel can be translated into enterprise‑ready scripts for broader application.
Complex tasks like database segmentation become easier, faster, and more consistent.
If you only want insights, simply restrict write permissions and use Copilot as an analytical companion.
