Raw data is often messy, incomplete or full of errors. In this article, we’ll explore the top ways to clean data so it can be accurate, consistent and ready for analysis.
1. Spell Checking
Correcting spelling mistakes makes sure that text fields are consistent and professional. Typos in names, addresses or product details can cause confusion or errors.
2. Removing Duplicate Rows
Duplicate rows can distort results and inflate counts. Identifying and removing these duplicates makes sure that each record is unique and reliable.
3. Replacing Text
Sometimes you need to remove or change repeated parts of a string. For example, you might want to delete a label at the start of a field or remove an unnecessary phrase.
4. Changing the Case of Text
Standardizing text to uppercase, lowercase or title case makes sorting, filtering and comparisons more consistent.
5. Removing Spaces
Extra spaces in cells can cause mismatches or errors. Remove leading, trailing or extra spaces.
6. Fixing Numbers
Correcting misplaced decimals or negative signs makes sure that calculations and totals are accurate.
7. Fixing Dates and Times
Consistent date and time formats prevent confusion and mean that you can accurately sort, filter and calculate.
8. Merging Columns
Combining related information or separating combined fields into individual columns makes data easier to analyse.
9. Using Third-Party Providers
Sometimes a data analysis company, like shepper.com/, will rely on specialised tools to clean and validate large datasets.
10. Reconciling Table Data
Cross-checking data across multiple tables ensures consistency and identifies discrepancies that will need correction.
