Data Validation refers to the process of ensuring the accuracy, integrity, and reliability of data by checking its quality, completeness, and consistency. It plays a vital role in maintaining data integrity and usability, making it a crucial step in data management and decision-making processes.
“Data validation is the meticulous data management procedure that uncovers errors, inconsistencies and discrepancies within datasets, ensuring that data remains accurate and trustworthy when aiding decision-making.”
What could happen if you don’t validate your data?
Inaccurate or incomplete data can lead to faulty insights, flawed analysis, and poor decision-making. Data validation directly impacts business operations, and enhance overall data quality by making sure that data meets relevant standards and guidelines.
Through various methods such as data profiling, rules-based validation, and statistical analysis, data validation helps identify errors, anomalies, and inconsistencies in datasets.
Methods include:
→ Data type validation – verifying that data is in the correct format and structure (i.e. rejecting data that contain special characters that the system cannot read).
→ Range validation – checking if data falls within specified limits (i.e. between 0.5 and 0.1)
→ Format validation – which ensures that data conforms to a predefined pattern
→ Referential integrity validation – validating relationships between different data elements.
→ Code validation – checking that data follows formatting rules in a valid list of options.
→ Cross-field validation – examining relationships between multiple fields to identify inconsistencies or dependencies.
→ Unique validation – validating data based on unique values (such as checking for duplicated data in a certain field).
What’s the difference between data validation and data verification?
While data validation focuses on assessing the quality and integrity of data, data verification involves confirming the accuracy of data.
Validation is internal – ensuring data meets internal standards and requirements.
Verification is external – confirms whether data matches an external source.