Data collection roles at NGOs and research firms ask for data quality checks again and again, and for good reason. If one enumerator types "Tamale", another "tamale" and a third "Tml", a report will count three towns instead of one. If a phone number is missing, an automation cannot send the reminder. Clean data is what makes everything you build trustworthy.
There are two jobs: prevent mistakes when data goes in, and find and fix the ones that still get through.
Prevent mistakes at the start
- Dropdowns. For any column with a fixed set of answers, such as district, item or payment method, use a dropdown list so people pick rather than type. In Google Sheets this is an in cell dropdown; Excel calls it data validation with a list.
- Validation rules. Most spreadsheets can reject or warn about values that break a rule: a quantity that is not a whole number, a date in the future, an age above 120.
- Required fields in forms. If data arrives through a form, mark the important questions as required so they cannot be skipped.
- Clear instructions. A short note in the heading or form question, such as "phone number, 10 digits, no spaces", prevents many errors.
- Fewer free text boxes. Every place someone can type freely is a place for spelling variations.