Clean data

Last updated Jun 5, 2023

Data that is complete, correct, and relevant to the problem you’re trying to solve.

Opposite:

• Dirty data

Pitfalls:

• Not checking for spelling errors
• Forgetting to document errors
• Not checking for misfielded values
• Overlooking missing values
• Looking at a subset of the data and not the whole picture
• Losing track of the business objective
• Not fixing the source of the error
• Not analyzing the system prior to the data cleaning
• Not backing up your data prior to data cleaning
• Not accounting for data cleaning in your deadline / process