Mixed data types damage data quality quietly
Data type problems can arrive from CSV files, APIs, JSON feeds, databases, spreadsheets, flat files, and older business systems. One field may contain 500 valid decimal values and one text value. The load may fail, the value may be converted incorrectly, or the target system may accept bad data with a cheerful lack of concern.
That is why field data type validation belongs before database loading, reporting, reconciliation, and automated decisions. It gives you a controlled failure instead of a mystery later. Mystery is fine in detective novels. It is less charming in month-end reporting.
Common type checks
- Integer and decimal checks for quantities, prices, balances, and percentages.
- Date and time checks before scheduling, aging reports, or SLA calculations.
- Boolean checks for yes/no, true/false, active/inactive, and similar flags.
- Text checks where identifiers must remain strings, even if they look numeric.
- Schema checks for API payloads, database extracts, and repeatable operational feeds.
A practical rule
If a field is used for calculation, sorting, grouping, filtering, joining, or routing, validate the type before loading it. If the source is a one-off file with ten rows, a manual check may be enough. If it feeds a scheduled workflow, automate the check and keep the rejected rows visible.
Useful references
Related reading: 22 data quality checks every data engineer should know, required field validation, and TechTarget's data validation definition.