Data

The Devil Is In The Details

Note: This article was originally published on LinkedIn on December 2, 2024. I’m republishing it here to share with a broader audience.

Lately, I’ve found myself with a bit more time on my hands than usual. So, I've been diving into a project that’s been rattling around in my head for a while. It’s one of those “I’ll get to it someday” ideas, and well—someday finally came.

The project involves pulling together user-created content from a variety of sources, which I’ve discovered is not as straightforward as I thought. When it comes to textual data, it’s never that simple. The biggest hurdle? Comparing data from different sources often requires comparing strings to ensure the data from both sources refers to the same thing. This is where normalization comes in.

Data Normalization is an Art

Normalization is the process of cleaning and standardizing messy data so that it all works together. Picture this: one source says “St. Mary’s Hospital,” another says “Saint Mary’s Hospital,” and yet another tacks on a user comment like “(Expanded Edition).” The quirks of human input can be particularly challenging.

Add in variations like numbers written out as words (“twentieth” vs. “20th”), creative abbreviations, and parentheses full of extra details, and suddenly you’re not just organizing data. You’re solving a puzzle where the rules keep changing.

The Tricks I’ve Picked Up Along the Way

As I’ve been working through this project, I’ve learned a lot about what works—and what doesn’t—when it comes to normalization. Here are a few things that have made my life easier:

• Know Your Domain: The rules depend on the type of data. In some cases, “vol.” means “volume,” but in others, it’s completely irrelevant. Context is everything.

• Make the Simple Fixes First: Start by removing punctuation, normalizing whitespace, and converting everything to lowercase. These small steps make a big difference.

• Handle Numbers Thoughtfully: Is “ten” the same as “10”? Sure, but only if it fits the situation. Make sure your approach matches the data’s purpose.

• Respect the Mess: User annotations—those bits in parentheses or brackets—often hold valuable information. Decide upfront what to keep and what to cut.

• Plan for Edge Cases: There’s always going to be that one entry that doesn’t fit your rules. Build flexibility into your process, and don’t be afraid to adjust as you go.

The Beauty of the Chaos

One of the most surprising parts of this work has been seeing just how creative people are with data. Whether it’s quirky abbreviations or personal notes added to entries, user-generated content reflects the individuality of its creators. That creativity is what makes normalization both a challenge and a joy.

Once the data is cleaned and standardized, though, something magical happens. Suddenly, things start to connect. It’s a reminder that even the messiest data has incredible potential—if you’re willing to put in the work to understand it.

Why It Matters

We live in a world overflowing with information. Making sense of it is a skill, whether you’re in AI, analytics, or just someone like me, tinkering with a project that caught your curiosity. Normalization isn’t glamorous, but it’s the foundation for turning messy data into something meaningful.

For me, this project has been a way to embrace the chaos and find clarity in it. I’d love to hear how others tackle these kinds of challenges. What’s been your biggest struggle with messy data? Please share so that we can all learn more along the way.