Data Validation Testing: What Is It and Why Does It Matter?

Data validation testing is a must for any organization that handles big data, and with good reason.
Among other things, it helps ensure data accuracy and prevents the propagation of errors.

This article covers data validation testing, what makes it important, how it works, how it can be automated, and more.

What Is Data Validation?

Data validation is the process of checking the quality and accuracy of a data source before using, importing, and processing the information. In that sense, data validation is the foundation of data cleansing.

There are various types of data validation, such as:

  • Data integrity testing
  • Data uniqueness testing
  • Data consistency testing
  • Data migration testing
  • etc.

The appropriate type of data validation is decided based on the requirements, destination constraints, and/or data collection objectives. 

Today, companies demand access to big data to gain a competitive edge — assuming data is accurate and correctly interpreted. However, the volume of data increases every second, making it harder and harder to manage.

To balance out the complexities of ever-growing data, new techniques, business rules, and intelligence are used to enhance existing systems.

However, this process is demanding, tedious, and error-prone. Data validation can help ensure that there are no errors along the way.

Why Data Validation Matters

When merging data from various sources, all repositories must be compatible and follow the same rules without corrupting data fields. Yet, inconsistencies in both the type and context of the data are common.

This is where data validation testing comes in. Its main goal is to ensure that merged data is accurate, consistent, complete, and free of data loss.

What Is Data Validation Testing?

The process of performing data validation as part of testing is called data validation testing. 

The testing is performed on databases after applying transformations to them. That allows an end-user such as a
business intelligence architect to check whether the available data is valid and databases are compatible and follow business rules and requirements. 

Data validation testing makes sure that data integrity is not affected when extracting, transforming, and loading data. It also instructs end-users what to do with incorrect and inconsistent data in the form of test cases.

Data Validation Testing for Enterprises and Data Integration (ETL) Projects

Big enterprises deal with big data, and they must carry out validation testing during data collection to ensure the data is not corrupted and guarantee its authenticity. It is also important to do data validation testing whenever data integration processes (ETL for example) are involved.

ETL projects involve extracting data, applying logical rules, and transforming and loading that data in the target location.

This process requires validation testing to ensure accuracy and prevent error propagation in the data pipeline. Validation testing also helps keeping an eye on data losses and discrepancies.

Data Validation Testing for Migration Projects (Data Migration Testing)

When projects involve data migrations, data validation testing comes to the fore again. In these types of projects, huge volumes of data are moved from a source to a new target storage, which is often a highly usable, disruption-free, and smooth location. 

Data migration can be done for many reasons, including upgrades, ensuring compatibility with new technology, and optimization. In any case, such projects require data validation to ensure that the migration has not affected the system in any way.

Common data validation tests in migration projects include:

  • Checking the number of rows migrated
  • Functionality tests, such as providing the same input to several systems and comparing the results
  • Performance testing
  • Security testing
  • End 2 End validation
  • Regression tests

Data Validation Testing Steps

Data validation testing has four main stages: detailed planning, database validation, data formatting validation, and sampling. Let’s have a look at each one.

  • Detailed Planning: This step involves the creation of a blueprint and roadmap for the validation process. Detailed planning also helps testers identify problems in the data source and choose the iterations needed to validate the data.
  • Database validation: This helps ensure that data is available from source to destination. Source and target data fields are compared in terms of the number of rows, data size, and overall schema comparisons.
  • Data Formating Validation: This phase focuses on the target data to ensure that the user can understand it and that it meets all business expectations.
  • Sampling: The final step consists of testing small data sets before processing and testing larger data sets. Potential errors can be identified early on in the smaller sets, reducing processing power waste.

Benefits of Data Validation Testing

Data validation and migration testing improve data collection and data accuracy, which helps provide more reliable quantitative results.

Other benefits of data validation and related testing approaches include improved:

  • Fulfillment of business requirements
  • Data accuracy
  • Decision-making
  • Strategic business management
  • Profits

What Is Database Validation Testing?

Other than data validation, database validation is also important. Database validation testing involves stored data and metadata validation. The testing is done based on requirements against the quality and performance of the data. Testers also look into the data objects, functionality, types, and lengths before making the data live and available for users. Indexes and the entire environment where data will be moving and evolving are also checked against set parameters.

Common types of database validation testing include:

  • Data mapping

  • ACID validation

  • Data integrity checks

  • Business rule compliance tests

  • Data accuracy tests

  • Data completeness tests

  • Data transformation tests

  • Data quality tests

  • Database comparison test (comparison between source and target)

  • End-to-end tests

  • Data warehouse tests

These types of proactive and continuous testing can help prevent data errors.

Steps to Adopt Data Validation Testing

The following tests can help you easily incorporate data validation testing into your workflow. Some of them are intensive. And that's why data validation may take a lot of effort if you don't utilize proven data validation software like BiG EVAL.

  • Data accuracy and data completeness tests ensure the data is correct.
  • Data transformation tests verify that the data is not corrupted after transformation.
  • Data quality tests then handle the bad data.
  • Database comparison tests compare the source and target database.
  • End-to-end and data warehouse tests help with data validation. 

No matter how many steps it takes, validation testing and data quality are essential for correct business intelligence and optimal return on investment.

Data validation is the process of checking the quality and accuracy of a data source before using, importing, and processing the information.

What Kind of Software Is Needed for Testing?

It really depends on your requirements. You may start with more manual testing tasks that you may support with Excel and maybe Power Query. But however, these processes still require considerable thought, processing, and effort to shape the data. 

Because efficient, accurate data management is so vital for business intelligence, specialized software has been developed to help meet enterprise data needs.

BiG EVAL is one of these tools that support data validation testing in an optimal way. It automates test processes in data-centric projects like data migrations, data warehouses, imports, exports and many more. Best Practices templates collected from hundreds of BiG EVAL's customers who have done data validation testing before, make the process even easier and more efficient for you.

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