Wrangling Chaos: 6 Things I Wish I Knew Before Tackling Messy Data
We’ve all been there, you’re excited to get into a task, you open a data file expecting clean columns and neatly labelled headers, and instead, it looks like your spreadsheet threw a wild party and forgot to clean up. Sound familiar?
Messy data is not just a minor inconvenience, it can derail projects, skew insights, and waste hours of valuable time. Over the years, I’ve had my fair share of data horror stories with one happening very recently to me. And while I can’t promise to make data cleaning fun (unless you’re into that sort of thing – kudos 👏), I can share 6 things I wish I’d known before diving headfirst into the chaos.
1. Not all messes are equal
Before getting into the nitty gritty, it helps to take a step back first and figure out what kind of mess you’re dealing with. Messy data comes in all shapes and sizes:
- Missing values (blank cells, place holders like ‘N/A’, ‘-999’)
- Inconsistent formatting (dates in five different formats, anyone?)
- Duplicates (when ‘John Smith’ shows up three times with slightly different details)
- Typos and weird characters (ever tried parsing a column where half the entries have invisible spaces?)
- Mismatched encodings (cue the garbled characters)
Knowing what you’re dealing with makes it easier to plan your cleanup strategy.
2. Good documentation is half the Battle
I can’t stress this enough: Understand your data before you touch.
I won’t lie this is something I am personally guilty of skipping in the past, and I’m still working on getting better at it. It’s tempting to dive straight into cleaning or analysis when you’re starting a messy file, but without context, you’re just guessing.
Ask yourself:
Where did this data come from?
- What does each column actually represent?
- How was it collected, and are there any quirks or assumptions baked into it?
If no one can give you documentation, start making your own as you go. Jot down what each column means, note weird values or patterns you see, and flag things you need to double check later. Your future self (or teammates) will thank you.
3. Choose the Appropriate Tools for the Job
Not every data mess can or should be cleaned in Excel. Sometimes you need heavier duty tools. Here are a few worth having in your arsenal:
Python pandas/R (tidy verse): Ideal for larger, complex datasets or automating repetitive cleaning steps
- OpenRefine: A free open-source tool for cleaning, transforming, and exploring data.
- Trifacta Wrangler: A data preparation tool that uses machine learning to suggest data cleaning steps.
- SQL: A database language that can be used to perform data cleaning tasks, especially for larger datasets.
Find the tool that fits the mess. And if you’re like me, you’ll inevitably pick the wrong one once or twice and learn from it.
4. Small Wins Matter
Messy Data can be overwhelming, so tackle it in small, manageable chunks.
Fix one issue at a time – maybe start by standardising date formats, then move on to removing duplicates, cleaning category labels and so on. Don’t try to fix everything in one pass.
And celebrate tiny victories as you go.
I still give myself high-fives or occasionally a sweet treat every time I successfully fix a rogue column without breaking the rest of the dataset.
5. Validate As You Go
Cleaned data is only useful if it’s actually clean. That’s why validating your work is crucial. Double-check your changes:
- Did fixing the date column mess up your sorting?
- Did removing duplicates accidentally drop important rows?
- Do summary statistics look right after your changes?
I like to create quick before-and-after snapshots or run simple checks (like row counts or unique value lists) after each cleaning step. It’s saved me from embarrassing mistakes more times than I can count.
6. Don’t underestimate the ‘Rubber Duck’
When you’re stuck staring at a messy dataset for too long, sometimes the best fix isn’t a new tool or tutorial, it is explaining the problem out loud to someone (or... something)
‘The Rubber Duck’ is a legendary problem solving technique from software engineering. The idea is simple:
If you’re stuck, talk through the issue step by step to a colleague, a pet, or literally a rubber duck sitting on your desk. You’d be surprised how often saying the problem out loud makes the solution obvious.
I’ve lost count of how many times I untangled a data issue just by walking through it with a teammate or narrating the problem to an empty room like a slightly unhinged detective.
FINAL THOUGHTS
Messy data might be inevitable, but it doesn’t always have to be a nightmare. With a bit of strategy, the right tools, a couple of tiny victories, and maybe a helpful rubber duck, you can turn chaos into clarity.
By Obosekokhune Eselebor
Research Software Technician
LIDA Data Analytics Team
The Data Analytics Team are the in-house, technical LASER support at LIDA
The team provide end-to-end support from project conception to closure, and design personalised data environments tailored to your data and requirements. They are a team of dedicated specialists in data management, data analysis, software engineering and information governance, who collaborate with LIDA researchers across all stages of their projects.