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What does data cleaning involve and why does it take so long?
Data cleaning means fixing errors, removing duplicates, handling missing values, standardising formats and reconciling inconsistent records so data is reliable. It takes long because real-world data is messy, sources disagree and fixing issues often needs business decisions, not just code.
Typical tasks:
- Standardising names, dates, currencies and units
- Merging duplicate customers
- Deciding how to treat blanks and outliers
- Reconciling totals against source systems
It is often the largest part of analytics work. Fixing issues at the source and automating checks reduces the effort over time. Budget time for it explicitly in any project plan, because underestimating cleaning work is a leading cause of overruns.
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