When cleaning messy data took longer than the analysis
I expected the analysis to be the hard part. I had hypotheses, charts sketched on the back of a napkin, and a deadline that felt generous enough. What I didn't budget for was three days of spelunking through a dataset that looked like it had been stitched together from someone else's leftovers.
Dates in three different formats. Nulls that were actually the string "N/A". A vendor column that mixed product names and SKU codes. Rows that looked identical until you inspected whitespace and invisible characters. Each discovery demanded a detour: parse this, normalize that, decide whether to drop or impute.
The analysis—when it finally happened—ran in under an hour. The cleaning took longer than the models, the plots, and the write‑up combined. It didn't feel glamorous, but it was the only reason the results were trustworthy.
What took the time
Data cleaning isn't a checklist you run once. It's a series of decisions:
- Understanding the provenance of each column and its valid domain.
- Reconciling formats and encodings across sources.
- Dealing with inconsistent categorical labels and typos.
- Designing sensible imputations and documenting why they were chosen.
Practical short fixes that helped
I learned to stop guessing and start profiling. A few quick tricks saved time later:
- Automated profiling to surface odd formats and outliers early.
- Small, representative samples for exploratory cleaning before applying transforms to the full table.
- Idempotent cleaning scripts so re-running after a tweak didn’t create more chaos.
- Versioned datasets and a changelog for every transformation step.
Final takeaway
Analysis is the visible payoff; cleaning is the scaffolding. When cleaning takes longer than the analysis, that's not failure—that's reality. Treat data prep as a core deliverable: measure it, automate what you can, and communicate the effort. The next time the deadline looms, you'll plan for the invisible work that makes the insights real.