Nowadays, a fast-growing client demand leads to huge 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, every day you need to add something completely 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 important part of things to take care 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 in a proper way and finally, that you have all the Data you expect to see in the front end of your system been represented correctly corresponding to the input.
There is a number of data validation testing techniques and approaches to help you accomplish these tasks above:
Data Warehouse Testing is a separate specific and complicated testing task that includes the number of subsequent test activities:
Testing a database, as the background of almost any software application, is the validation of the stored data and metadata according to requirements. Data quality and application performance, objects controlling data, and the functionality wrapped around it are definitely better to be tested before going live. That’s why database validation testing including data type and length, index checks alongside metadata check across environments help validate application design specifications and the overall system performance.
Popular Types of Database Validation Testing include:
Ok, what I’m saying, you can’t test it yourself or with one suppa-duppa-cool-test-specialist. You need a good QA team to take care of all this stuff and specialists who are not afraid of huge amount of data testing.
Data validation might be a cure-for-all-ills option, but it requires a lot of efforts. Turning data into value is much easier with quality data. How important is data quality is to your organization? Data quality is a primary concern of both business and technology. Accurate reporting, well-conceived strategies, vital metrics and insights, ROI after all, those are actual assets gained with quality data. Data quality includes a number of aspects like accuracy, completeness, conformity and consistency. With innovations entering tech world, dark and historic data, natural language processing and big data are becoming the integral element of data quality mission enabled by artificial intelligence (AI) and machine learning applications.