June 16, 2018
Data Validation Testing
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 testing 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 bad data is handled well;
- Database Comparison Testing – compares the source and target DB despite the fact 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 end point we have correct data according to what we put into start point of the data flow;
- Data Warehouse Testing – makes sure that data goes successfully through all points of the system that uses data warehouse.
Data Warehouse Testing is a separate specific and complicated testing task that includes the number of 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 expected length of time;
- OLAP Testing – makes sure that the data is mapped from the data warehouse and designed correctly to the OLAP cubes;
- Report Testing – the final destination of the data is usually a report where data should be the same as expected according to the input.
What Is Database Validation Testing and Why Is It Important in This respect?
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:
- Data mapping – validating the data transferred from the application to the backend database and vice versa
- Atomicity, Consistency, Isolation, Durability (ACID) validation is performed to make sure every database transaction conformes to the above mentioned properties
- Data Integrity helps to verify that the stored data is not violated by any updates or retrievals
- Business Rule Compliance verifies the implementation of any business rule across a system
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. Explore more here.