Data Cleaning Verification: A Checklist

 

Correct the most common problems

Make sure you identified the most common problems and corrected them, including:

• Sources of errors: Did you use the right tools and functions to find the source of the errors in your dataset?

• Null data: Did you search for NULLs using conditional formatting and filters?

• Misspelled words: Did you locate all misspellings?

• Mistyped numbers: Did you double-check that your numeric data has been entered correctly?

• Extra spaces and characters: Did you remove any extra spaces or characters using the TRIM function?

• Duplicates: Did you remove duplicates in spreadsheets using the Remove Duplicates function or DISTINCT in SQL?

• Mismatched data types: Did you check that numeric, date, and string data are typecast correctly?

• Messy (inconsistent) strings: Did you make sure that all of your strings are consistent and meaningful?

• Messy (inconsistent) date formats: Did you format the dates consistently throughout your dataset?

• Misleading variable labels (columns): Did you name your columns meaningfully?

• Truncated data: Did you check for truncated or missing data that needs correction?

• Business Logic: Did you check that the data makes sense given your knowledge of the business? 

  
  

  Review the goal of your project

  Once you have finished these data cleaning tasks, it is a good idea to review the goal of your project and confirm that your data is still aligned with that goal. This is a continuous process that you will do throughout your project-- but here are three steps you can keep in mind while thinking about this: 

  • Confirm the business problem 

  • Confirm the goal of the project

  • Verify that data can solve the problem and is aligned to the goal