5 WAYS TO USE GENERATIVE AI FOR DATA MOCK-UPS IN EXCEL TESTING

Generative AI is transforming software testing and financial modeling by providing realistic, high-volume, and safe synthetic data directly within Excel. This eliminates the risks and constraints associated with using production data or manually mocking large datasets.

5 Ways to Use Generative AI for Data Mock-ups in Excel Testing

Test Without the Mess: 5 Uses for Generative AI in Creating Realistic Data Mock-ups for Excel Projects:

• GENERATING LARGE-SCALE SYNTHETIC DATASETS

  Mock-up Use: Creating thousands of rows of mock data for stress-testing the performance of large financial models, complex PivotTables, or custom VBA macros.

  How it Works: Use AI add-ins or external tools (like Mockaroo, Gretel, or Copilot in advanced modes) with a descriptive prompt: "Generate 5,000 rows of transactional sales data with columns for 'Customer ID,' 'Product Category,' 'Transaction Date,' and 'Sale Amount' between $10 and $5,000." The AI instantly produces the high-volume spreadsheet, saving days of manual entry.

  
  

• CREATING PII-COMPLIANT MOCK DATA

  Mock-up Use: Generating realistic customer or employee data that preserves the statistical properties (like name structure or date formats) of real data without risking sensitive Personally Identifiable Information (PII).

  How it Works: AI models are excellent at generating contextually appropriate but fictional data. Prompts like, "Generate 50 unique employee records with realistic US 'First Names,' 'Last Names,' 'Email Addresses' (using generic domains), and 'Start Dates' spanning the last 5 years." This ensures security and compliance during model development and testing.

  
  

• SIMULATING EDGE CASES AND ANOMALIES

  Mock-up Use: Stress-testing a model's error-handling capabilities by deliberately inserting realistic data inconsistencies that commonly occur during real-world data import.

  How it Works: You can prompt the AI to generate a specific, flawed dataset: "Create a 100-row list of 'Product Prices,' ensuring 5% of the values are blank and 3% of the 'Currency' column contains misspelled entries like 'USSD' or 'Dollar.'" This ensures your reconciliation formulas or Power Query cleaning steps are robust enough to handle messy inputs.

  
  

• MODELING TIME-SERIES AND SEASONALITY

  Mock-up Use: Generating multi-year financial data that exhibits specific, complex trends (like seasonality and growth rates) to test the accuracy of forecasting models.

  How it Works: Prompt the AI to: "Generate 36 months of 'Revenue Data' with a 10% year-over-year growth rate and a 40% seasonal spike in Q4." This mock data allows you to train and test forecasting functions (like $FORECAST.ETS$) under known, controlled conditions to validate that the model is predicting the correct trend.

  
  

• GENERATING CLASSIFICATION/CATEGORIZATION DATA

  Mock-up Use: Creating text-based data (e.g., customer reviews) to test the effectiveness of AI classification models or conditional aggregation formulas ($SUMIFS$).

  How it Works: You can ask: "Generate 20 customer comments for a software product; ensure 15 comments express 'Positive Sentiment' about 'Performance' and 5 comments express 'Negative Sentiment' about 'Customer Support.'" This mock-up allows you to test formulas that categorize text based on sentiment or topic keywords.

Generative AI is transforming software testing and financial modeling by providing realistic, high-volume, and safe synthetic data directly within Excel. This eliminates the risks and constraints associated with using production data or manually mocking large datasets. By using tools like Copilot, AI add-ins, or external generators, analysts can quickly create mock-ups that are statistically representative of real-world data, drastically improving model robustness and reducing security risks during the testing and development phase.