What Is Data Validation?
Explore the process of data validation, its role, and the essential steps you need to follow when implementing it in HR.
Data validation is the process of checking the accuracy and completeness of data to confirm the correctness of the data being transmitted. It involves verifying and cleaning data to meet specific predefined standards or criteria.
Why is data validation important?
HR professionals use data to make important people-related decisions. If that data is not accurate or reliable, those decisions will be flawed. Data validation offers a way to ensure the validity and reliability of data so HR leaders can be confident in the decisions they make.
What are the steps involved in data validation?
There are several steps involved in data validation, including:
Defining the specific criteria and rules that the data must meet. These are based on your exact type of data, the expected range of values, formats, and relevant standards or requirements that must be met.
Data collection and entry. This might include form controls like dropdowns or checkboxes to limit data entry errors.
Data cleansing. Identifying and removing any errors or inconsistencies in the data.
Various format and consistency checks are also performed to ensure that formatting is consistent, check the accuracy of data ranges, and verify data completeness. Data might also be cross-validated with external data sources to verify its accuracy.
However, note that data validation isn’t a one-time task; it’s an ongoing process of continually monitoring and updating data validation rules and processes to accommodate changes in the sources or types of data used as well as changing business requirements.
What is an example of data validation?
An example of the use of data validation in HR might be examining the validity of job applicants’ data through background check databases, or verifying employee Social Security Numbers (SSN) when hired.
What role does technology play in data validation?
Technology has vastly improved the data validation process by speeding and enhancing the efficiency, accuracy, and scope of data validation. It also allows you to access massive amounts of data available through proprietary and third-party databases. Plus, it lets you integrate databases and even validate the accuracy and relevance of the data.
Advanced technology like artificial intelligence (AI) and machine learning (ML) can be used to help identify patterns, anomalies, or risks that manual processes might otherwise miss in the data.
