Data validation is intended to provide certain well-defined guarantees for fitness, accuracy, and consistency for any of the various kinds of user input into an application or automated system. Data validation rules can be defined and designed using any of various methodologies, and be deployed in any of various contexts.
In evaluating the basics of data validation, generalizations can be made regarding the different types of validation, according to the scope, complexity, and purpose of the various validation operations to be carried out.
- Data type validation
- Range and constraint validation
- Code and Cross-reference validation
- Structured validation
Steps to Data Validation
1. Determine Data Sample
Determine the data to sample. If you have a large volume of data, you will probably want to validate a sample of your data rather than the entire set. You’ll need to decide what volume of data to sample, and what error rate is acceptable to ensure the success of your project.
2. Validate the Database
Before you move your data, you need to ensure that all the required data is present in your existing database. Determine the number of records and unique IDs, and compare the source and target data fields.
3.Validate the Data Format
Determine the overall health of the data and the changes that will be required of the source data to match the schema in the target. Then search for incongruent or incomplete counts, duplicate data, incorrect formats, and null field values.
Data Validations- Allowed character checks
- Batch totals
- Cardinality check
- Check digits
- Consistency checks
- Control totals
- Cross-system consistency checks
- Data type checks
- File existence check
- Format or picture check
- Hash totals
- Limit check
- Logic check
- Presence check
- Range check
- Referential integrity
- Spelling and grammar check
- Uniqueness check
- Table lookup check
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