My Secret Weapon for Taming Messy Data

A while back, I wrote about a system I designed for cleaning up messy financial data. It was mostly theory and specifications. Today, I want to share a story about how I just used that same system for something completely different: teaching a machine to read and rate website documentation.

This process taught me a valuable lesson: the real secret isn't a single clever solution, but a repeatable process for making sense of chaos.

The Problem: All Data is Messy Data

Whether it's financial data from different stock exchanges or documentation from different websites, all raw data is a mess. It arrives in different formats, with different labels, and is full of inconsistencies.

You can't build anything reliable on a messy foundation. You first have to clean it up.

My Solution: The Data Assembly Line

Instead of writing a new, one-off script for every new type of messy data, I built a mental model for a data "assembly line." It's a series of steps that I can apply to almost any data-cleaning problem.

It works like this:

  1. The Loading Dock (Ingest): First, just get the raw materials in the door. Don't worry about how messy they are. For my docs-score project, this meant grabbing the raw HTML from a list of websites.
  2. The Cleaning & Sorting Station (Normalize): This is the heart of the assembly line. Here, every piece of raw material is cleaned, measured, and put into a standard-sized "box." For the docs-score project, every documentation page—no matter its design or layout—was processed and transformed into a standard "box" of features, with clear labels like code_ratio and readability_score.
  3. The Quality Check (Score): Once everything is in a standard box, it's easy to inspect and evaluate. For my project, I added a simple "quality" score (gold or average) to each box, so I could teach a machine to spot the difference.
  4. The Shipping Department (Emit): Finally, send the standardized boxes out in whatever format you need. In this case, I exported everything to a clean, simple CSV file—the perfect input for a machine learning model.

The Payoff: A System That Scales

By using this "assembly line," I didn't just clean up some website data. I validated that the system I designed for finance could work for something totally different.

The docs-score project now has a reliable pipeline for turning messy websites into clean training data. But more importantly, I have a process I can trust the next time I encounter a new data mess.

That's the real power of a good system. You're not just solving one problem—you're building a machine that can solve a whole class of problems.