5 TECHNIQUES FOR CROSS-REFERENCING DATA IN EXCEL USING NATURAL LANGUAGE PROMPTS

Cross-referencing (or looking up) data is the backbone of most Excel models, moving data from one table to enrich another. AI dramatically simplifies this by eliminating the need to write complex functions like VLOOKUP, XLOOKUP, or the INDEX/MATCH combination.

5 Techniques for Cross-Referencing Data in Excel Using Natural Language Prompts

Here are 5 techniques for cross-referencing data in Excel using natural language prompts:

• BASIC VLOOKUP/XLOOKUP FORMULA GENERATION

  Technique: Ask the AI to write the lookup formula, specifying the tables and columns to be matched.

  Prompt Example: "In the 'Sales Data' table, create a new column called 'Customer Name' that looks up the 'CustomerID' from the 'Customer Info' sheet."

  How it Works: Copilot (or similar tools) analyze the data structure of both sheets and automatically generate the necessary XLOOKUP or VLOOKUP formula, complete with correct table and column references, ensuring the syntax is perfect.

  
  

• CROSS-REFERENCING WITH ERROR HANDLING

  Technique: Instruct the AI to build a robust lookup that handles cases where the required match is not found, eliminating the manual addition of IFERROR or IFNA.

  Prompt Example: "Add a formula to the 'Orders' table that pulls the 'Price' from the 'Products' table using the 'SKU' column as the key. If the SKU is not found, return the text 'Price Missing'."

  How it Works: The AI integrates the error-handling logic directly into the formula, typically using the built-in error handling argument of XLOOKUP (the modern function) or wrapping an older VLOOKUP in an IFERROR function.

  
  

• FUZZY MATCHING (AI Add-ins)

  Technique: Use specialized AI add-ins to match data points that are similar but not exact (e.g., "Jonathon Smith" vs. "Jonathan Smith"), a task impossible with standard Excel formulas.

  Prompt Example: "Merge the 'CRM Leads' table with the 'Webinar Attendees' table. Match on the 'Name' column with an 85% similarity threshold."

  How it Works: This relies on external AI algorithms that perform fuzzy matching and assign a similarity score, far surpassing the exact match limitation of native lookups. The AI automates the data processing needed to achieve this complex match.

  
  

• MERGING DATA VIA POWER QUERY

  Technique: Instruct the AI to merge two complete tables based on a common key, using the Power Query engine for a scalable and repeatable process.

  Prompt Example: "Combine the 'Expense Data' table and the 'Cost Center List' table. Match them on the 'CC ID' column, and include the 'Department Name' column in the Expense Data."

  How it Works: Copilot can translate this into the necessary Power Query steps (specifically the Merge Queries operation). The AI automates the ETL process, which is ideal for large datasets or recurring imports.

  
  

• CROSS-REFERENCE BASED ON MULTIPLE CRITERIA

  Technique: Ask the AI to generate a formula that performs a lookup based on meeting several conditions simultaneously.

  Prompt Example: "Create a column that returns the 'Commission Rate' from the 'Rate Card' table, matching both the 'Employee ID' AND the current 'Sales Quarter'."

  How it Works: The AI generates an advanced formula (such as nested FILTER or a combination of XLOOKUP with the ampersand & operator to create a unique lookup key), solving a multi-criteria lookup problem that traditionally required the complex INDEX/MATCH and CHOOSE functions.

AI is transforming cross-referencing in Excel by making the powerful capabilities of XLOOKUP and Power Query accessible via natural language. By simply describing the data match you need, you eliminate manual syntax errors and gain immediate access to advanced techniques like error handling and multi-criteria lookups, significantly accelerating data analysis.