Data validation testing

There is much more to data validation testing than you might think.

Nowadays, a fast-growing client demand leads to vast amounts of data that each competitive product should deal with correctly and effectively. Every day you need more and more terabytes to store that all, and every day you need to add something entirely new to your system. This process is cruel and never stops. To stay on a stage, you need to have a well-tuned team of professionals working effectively to satisfy all the market whims, and data validation is an integral part of things to take care of here.

Data validation testing allows you to make sure that the data you deal with is correct and complete; that your data and database can go successfully through any needed transformations without loss; that your database can dwell with specific and incorrect data correctly, and finally, that you have all the data you expect to see in the front end of your system been represented precisely corresponding to the input..

There are many data validation testing techniques and approaches to help you accomplish these tasks above:

• Data Accuracy Testing – makes sure that data is correct.
• Data Completeness Testing – makes sure that data is complete.
• Data Transformation Testing – makes sure that data goes successfully through transformations.
• Data Quality Testing – makes sure that insufficient data is handled well.
• Database Comparison Testing – compares the source and target DB even though their structure and volume differ.
• Data Comparison Testing – compares data between different points of data flow.
• End-To-End Testing – final system testing that makes sure that in the endpoint, we have correct data according to what we put into the start point of the data flow.
• Data Warehouse Testing – ensures that data go successfully through all points of the system that uses the data warehouse.

Data Warehouse Testing is a separate specific, and complicated testing task that includes some subsequent test activities:

• Test Data Acquisition – makes sure all data from all sources is acquired.
• ETL Testing – makes sure that all goes well in extract, transform, and load processes.
• Data Accuracy Testing.
• Data Transformation Testing.
• Data Load – makes sure data is loaded within the expected length of time.
• OLAP Testing – makes sure that the data is mapped from the data warehouse and designed correctly for the OLAP cubes.
• Report Testing – the data’s final destination is usually a report where data should be the same as expected according to the input.

At this point, data validation and data warehouse testing is approaching with many moving parts. Knowing these ensures you can use the technologies with a more significant deal of effectiveness. The same logic applies to database validation testing as well.

What is database validation testing and why is it important in this respect?

As the background of almost any software application, testing a database validates the stored data and metadata according to requirements. Data quality, application performance objects controlling data, and the functionality wrapped around it should be tested before going live. That’s why database validation testing, including data type and length and index checks alongside metadata checks across environments, help validate application design specifications and the overall system performance.

Popular types of database validation testing include:

• Data mapping – validating the data transferred from the application to the backend database and vice versa.
• Atomicity, Consistency, Isolation, and Durability (ACID) validation is performed to ensure every database transaction conforms to the abovementioned properties.
• Data Integrity helps to verify that any updates or retrievals do not violate the stored data.
• Business Rule Compliance verifies the implementation of any business rule across a system.

It means users can’t test it themselves or with a specialist on board. So it would help if you had a good QA team to handle all this stuff and specialists fearless of the enormous amount of data testing.

The bottom line

Data validation might be a cure-for-all-ills option, but it requires much effort. Turning data into value is much easier with quality data. Data quality is a primary concern of both business and technology. After all, accurate reporting, well-conceived strategies, vital metrics, insights, and ROI are tangible assets gained with quality data.

Data quality includes several aspects, like accuracy, completeness, conformity, and consistency. With innovations entering the tech world, dark and historical data, natural language processing, and big data are becoming integral to data quality missions enabled by AI and ML.

There is also a chance to see how data validation testing and its key types pan out in practical terms. Contact us and our expert QA teams will dive even deeper into the topic or show how the phenomenon works in real-life setting.