Cleaning Data with AI: A Visual Workflow for Analysts

"Garbage in, garbage out." Every data analyst knows this mantra. But cleaning data with Python or SQL often involves a lot of trial and error.

You write a regex. It fails. You tweak it. It matches too much. You ask ChatGPT. It gives you a script. You run it. It breaks.

The Spaghetti Code Nightmare

When you use ChatGPT to write data cleaning scripts, you often end up with a "spaghetti thread"—a long, winding conversation full of broken code snippets and error messages.

The Pipeline Approach

Chat Navigator helps you visualize your data cleaning process as a pipeline.

• Step 1: Ingestion. Paste your CSV header.
• Step 2: Normalization. Ask for a script to fix date formats. Chat Navigator creates a "Date Fix" node.
• Step 3: Deduplication. Ask for logic to remove duplicates. Chat Navigator creates a "Dedupe" node.
• Step 4: Validation. Ask for a test script.

If the validation fails, you don't have to scroll back up 50 messages to find the normalization logic. You just click the "Date Fix" node and refine the prompt.

You're building a reproducible pipeline, not just hacking together a script.