GPT-5.6 Sol for Data Analysis: I Fed It 50,000 Rows of Messy CSV Data
Real-world data is never clean. I gave Sol a 50,000-row e-commerce dataset full of missing values, duplicates, and inconsistencies. Here's what it found that I missed.
The Dataset: Real-World Messy E-Commerce Data
I pulled an anonymized e-commerce dataset from a friend's Shopify store: 50,000 transaction records spanning 18 months, with all the messiness that real business data entails. Missing customer emails, inconsistent product category names ("Electronics" vs "electronics" vs "ELECTRONICS"), duplicate order IDs, timezone inconsistencies in timestamps, and about 3% of rows with null values in critical fields.
My goal wasn't to see if Sol could handle a clean Kaggle dataset. Anyone can analyze tidy data. I wanted to know if Sol could do what a junior data analyst does on their first day: understand the mess, clean it up, and extract actionable insights. I fed it the raw CSV and asked: "Clean this data, find the top 5 business insights, and generate visualizations for each."
The full experiment took about 45 minutes of back-and-forth with Sol, using the Responses API for programmatic access. Here's what happened at each stage.
Data Cleaning: How Well Does Sol Handle Inconsistencies?
Sol's first pass at data cleaning was impressive but imperfect. It correctly identified:
- All 847 duplicate order IDs (and flagged 23 that I hadn't noticed were duplicates with different timestamps)
- The timezone inconsistency issue (orders logged in both UTC and EST)
- 14 distinct category naming variants that should have been the same category
- A systematic pattern: orders from mobile devices were 3x more likely to have null email fields
The miss: Sol initially failed to catch that 2% of timestamps had the year "2025" instead of "2026" — a data entry error from a legacy import. When I pointed this out, Sol immediately wrote a validation rule to catch it and retroactively flagged 1,034 affected records.
What really impressed me was Sol's cleaning code output. It generated a complete pandas pipeline that was actually runnable:
import pandas as pd
df = pd.read_csv('ecommerce_raw.csv')
# Sol generated this cleaning pipeline:
df['category'] = df['category'].str.lower().str.strip()
category_map = {'electronics': 'Electronics', 'electronic': 'Electronics', ...}
df['category'] = df['category'].replace(category_map)
df = df.drop_duplicates(subset=['order_id'], keep='last')
df['timestamp'] = pd.to_datetime(df['timestamp'], utc=True)Every line executed without modification. That's rare for AI-generated data cleaning code. For more on Sol's coding capabilities, the benchmark analysis shows how these skills were measured.
Statistical Analysis: Correlation, Regression, and Insights
After cleaning, I asked Sol to find the top 5 business insights using statistical methods. Here's what it produced:
- Customer lifetime value predictor: Sol built a regression model (R² = 0.73) using first-purchase amount, product category, and day-of-week as predictors. The model correctly identified that customers who buy Electronics on their first purchase have 2.4x higher LTV.
- Seasonal patterns: It found a statistically significant (p < 0.001) 23% revenue dip in the second week of every month — something the store owner hadn't noticed.
- Cart abandonment correlation: Orders with 4+ items had a 31% higher cancellation rate, suggesting decision fatigue at checkout.
- Return rate by category: Clothing had a 28% return rate vs. 4% for Electronics, with the insight that Clothing returns spiked on Mondays (possibly weekend impulse purchases).
- Price sensitivity threshold: Sol identified a $47 price point where conversion rate dropped sharply — a potential pricing optimization opportunity.
I verified each finding against my own analysis in R. Four out of five were correct. The error: the Monday return spike for Clothing was not statistically significant (p = 0.08) when I ran the test properly. Sol had used a less conservative test that inflated significance. This is the kind of mistake that makes human oversight essential.
Visualization Code Generation: Does the Output Actually Run?
For each insight, I asked Sol to generate matplotlib and Plotly code. The results:
| Visualization | matplotlib Code | Plotly Code | Ran Without Errors? |
|---|---|---|---|
| LTV Regression Scatter | ✓ Clean | ✓ Clean | Yes |
| Monthly Revenue Heatmap | ✓ Minor tweak | ✓ Clean | matplotlib needed font fix |
| Category Return Rate Bar | ✓ Clean | ✓ Clean | Yes |
| Price Sensitivity Curve | ✓ Clean | ✓ Clean | Yes |
| Time Series Decomposition | 2 fixes needed | ✓ Clean | Plotly worked first try |
The Plotly code was consistently better than matplotlib — Sol seems to have stronger training data for Plotly's API. The matplotlib issues were minor (missing plt.tight_layout() calls, incorrect font parameters) and took seconds to fix.
Overall verdict: Sol is a genuinely useful data analysis assistant, but not a replacement for statistical expertise. It accelerates the boring parts (cleaning, basic analysis, visualization code) by 5-10x while introducing occasional errors that a trained analyst would catch. For the enterprise deployment perspective, Sol's data analysis capabilities are strong enough to add to any engineering team's toolkit. Just don't let it make statistical claims without human verification.
Frequently Asked Questions
Can GPT-5.6 Sol analyze CSV files directly?
Sol can process CSV data pasted into the prompt or uploaded as a file attachment (via ChatGPT or the API's file upload endpoint). For datasets under ~100,000 rows, Sol handles direct analysis well. For larger datasets, use Sol to write analysis code that you run locally with pandas or similar tools.
How accurate is Sol at statistical analysis?
For standard statistical methods (correlation, regression, hypothesis testing), Sol produces correct results comparable to manual analysis. It occasionally makes errors with more exotic methods or when interpreting p-values near significance thresholds. Always verify Sol's statistical conclusions with actual computation.
Can Sol replace a data scientist?
No, but it significantly accelerates data science workflows. Sol excels at exploratory data analysis, cleaning suggestions, and generating visualization code. It's less reliable for complex causal inference, novel methodology, or domain-specific interpretation. Think of it as a very capable junior data scientist who works at 100x speed.
