Forget Power Pivot: How to Analyze Multiple Excel Files with AI

Problem background & pain points

Imagine you're a sales analyst, and at the end of each quarter, you receive three separate CSV files: sales_transactions.csv, customers.csv, and products.csv.

• The sales file has thousands of rows, each with a TransactionID, Date, CustomerID, ProductID, and SaleAmount.
• The customers file links CustomerID to CustomerName and Country.
• The products file links ProductID to ProductName and Category.

Your manager asks two seemingly simple questions: "What were our top 5 selling products by revenue this quarter?" and "Which countries generated over $10 million in sales?"

Immediately, you hit a wall. The sales data only has IDs. To get product names and customer countries, you need to combine information from all three files. The classic approach? A nightmare of VLOOKUP or INDEX/MATCH formulas, trying to pull everything into one massive, unmanageable master sheet. This method is not only slow and incredibly error-prone but also pushes Excel to its limits, often leading to crashes with large datasets.

The Traditional Excel Solution: The Power Pivot Method

For experienced Excel users, the "correct" way to handle this is not VLOOKUP, but Power Pivot. Power Pivot is a powerful add-in that allows you to load multiple tables into a "Data Model" and create relationships between them, effectively building a mini-database inside your Excel workbook.

This approach is undoubtedly more robust than VLOOKUPs. Here’s a high-level overview of the manual steps involved:

1. Enable the Add-in: First, you have to find and enable the Power Pivot COM add-in from Excel's options menu.
2. Import Data into the Model: You use Power Query (Data > From Text/CSV) to import each of your three CSV files. Critically, you must choose to "Only Create Connection" and "Add this data to the Data Model" instead of loading them as worksheets.
3. Create Relationships: You then open the Power Pivot window, go to the "Diagram View," and manually create relationships by dragging and dropping the key fields. You'd connect CustomerID from the Sales table to the corresponding ID in the Customers table, and do the same for ProductID.
4. Build a PivotTable: With the model built, you can finally insert a PivotTable. The PivotTable field list will now show all three tables, and you can pull fields from any of them. For example, you can drag ProductName from the Products table to Rows and SaleAmount from the Sales table to Values.
5. Write DAX Measures (Optional but Recommended): For more sophisticated analysis, you're encouraged to write formulas using DAX (Data Analysis Expressions). For instance, instead of just dragging SaleAmount into the values field (an "implicit measure"), you'd create an "explicit measure" like Total Revenue := SUM(Sales[SaleAmount]).

Limitations of the Power Pivot Approach

While powerful, this workflow turns a business analyst into a part-time data engineer. It's fraught with its own challenges:

• Steep Learning Curve: You're no longer just using Excel. You're learning data modeling principles (fact vs. dimension tables, cardinality) and a new formula language (DAX). This is a significant time investment.
• Time-Consuming Setup: The process of importing, modeling, and creating measures is tedious. Before you can even begin to answer your manager's question, you might spend 30 minutes to an hour just setting up the model.
• Rigidity: The model is built for a specific purpose. If your manager comes back with an ad-hoc question like, "What's the average transaction size for our top 3 products in Germany?", you might need to go back, adjust the model, or write a new, complex DAX measure. It's not built for conversational, on-the-fly analysis.
• Complexity: DAX is powerful but can be cryptic and difficult to debug. A misplaced comma or incorrect function can break your entire report, and finding the error isn't always straightforward.

You're spending more time building the engine than driving the car.

The New Solution: Using Excel AI with Excelmatic

What if you could skip the entire data modeling and DAX-writing process? What if you could just give Excel your files and ask your questions directly? That’s exactly what an Excel AI Agent like Excelmatic is designed for.

Excelmatic understands your data and the relationships within it, allowing you to perform complex, multi-table analysis through simple conversation.

Step-by-Step: From Three Files to Instant Answers

Let's solve the same problem using Excelmatic.

1. Upload Your Data Files

Go to Excelmatic and upload all three files—sales_transactions.csv, customers.csv, and products.csv—in one go. The AI will ingest them and prepare them for analysis.

2. Describe Your Goal in Plain Language

Instead of building a data model, you simply talk to the AI. To find the top 5 selling products, you would ask:

Using the three files, join them to find the total sales for each product name. Then show me the top 5 products with the highest total sales.

To find the top countries, you could ask:

Show me a list of countries where the total sales amount is greater than 10,000,000. Sort the list from highest to lowest sales.

3. View and Iterate on the Results

Excelmatic will process your request, automatically figure out that CustomerID in the sales file links to the customer file, and do the same for products. It will then generate a pivot table or a data summary that directly answers your question.

The best part is that this is just the start of a conversation. You can easily refine the results:

• "Great. Now, can you turn that top 5 product list into a bar chart?"
• "For the list of countries, add a column showing the total number of transactions for each."
• "Filter this entire analysis to only include sales from Q1."

This conversational approach makes ad-hoc analysis incredibly fast and intuitive.

4. Export Your Finished Report

Once you're satisfied, you can download the results—including the generated tables and charts—as a new, clean Excel file. You get the final answer without ever having to write a formula or manually create a relationship.

Example Conversation with Excelmatic

Here's how that entire interaction might look:

Power Pivot vs. Excelmatic: A Quick Comparison

FAQ

1. How does Excelmatic know how to connect my files? Excelmatic's AI analyzes the column headers and data patterns across your uploaded files. It intelligently infers that a column named CustomerID in one file likely relates to a column named CustID or Customer ID in another, and automatically proposes or creates these relationships.

2. Do I need to know any DAX or Power Pivot concepts to use Excelmatic? Not at all. The entire purpose of Excelmatic is to abstract away that technical complexity. You only need to know what business question you want to answer.

3. Is my data secure when I upload it to Excelmatic? Yes, data security is a top priority. Excelmatic uses secure protocols for data transfer and storage. For specific details on data handling and privacy, please refer to the official privacy policy on the website. Your original files are never modified.

4. Can Excelmatic handle large datasets that normally require Power Pivot? Yes, Excelmatic is built on a cloud infrastructure designed to process datasets far larger than what a standard Excel worksheet can comfortably handle, making it a viable alternative to Power Pivot for many big data scenarios.

5. What if the AI misinterprets a relationship between my tables? While the AI is highly accurate, you can always guide it. If it makes an incorrect assumption, you can simply clarify in your next prompt, for example: "Join the sales file with the customers file using the Transaction_Cust_ID and Customer_Ref columns."

6. Can I export the pivot tables and formulas generated by Excelmatic? Absolutely. You can download the resulting analysis as a new .xlsx file, which will contain the generated pivot tables, data tables, and charts. You can then use and modify them in your desktop version of Excel.

Take Action: Upgrade Your Excel Workflow Today

Every hour you spend wrestling with data models, debugging DAX, or teaching colleagues how to refresh a Power Pivot report is an hour you're not spending on actual analysis. The world of data is moving towards more intuitive, conversational interfaces, and your Excel workflow should too.

Instead of becoming a data modeling expert, you can remain a business expert and get answers faster. Stop building the reporting engine and start asking the questions that matter.

Try Excelmatic for free today. Upload the same set of files you're struggling with right now and ask it the question you need answered. You might be surprised to get your result in minutes, not hours.