Tips
When to stop cleaning and start analyzing
Perfectionism kills projects. Know when your data is clean enough.
Define 'clean enough'
Clean enough depends on your use case. A rough analysis needs less perfection than a financial audit. Define your threshold upfront.
- What decisions will this data inform?
- What error rate is acceptable?
- What's the cost of a mistake?
Fix blocking issues only
If an issue would break your analysis or cause wrong conclusions, fix it. If it's merely annoying, note it and move on.
- Blocking: wrong data types, broken joins
- Non-blocking: inconsistent capitalization
- Prioritize by impact
Quick CTA
Assess quickly
Use Readable CSV to quickly evaluate whether your data is clean enough for your needs.
Try itTime-box your cleaning
Set a time limit. When it expires, assess. Is the data usable? If yes, proceed. If not, request better source data.
- Set a specific time limit
- Review progress at the deadline
- Diminishing returns are real
Document what you didn't fix
Keep a list of known issues. This helps others understand limitations and lets you return to fix things if they become blocking.
- Note known issues
- Record potential impact
- Create a backlog for later
Key takeaway
Perfect is the enemy of done. Clean enough to answer your questions is clean enough.