Let’s talk in this blog post about testing data. Every type of business, from insurance companies to financial institutions and even healthcare organizations or governments, needs data to develop and test the quality of their software and applications. In the era of big data we live, we leave traces of everything we do online and even in real life. This information can be practical for all types of institutions and not always against our interests.
However, this data production often comes from personal, sensitive, and private information – Not to mention that databases are inconvenient enough for testing. With so many details, numbers, and letters, how can one efficiently analyze them and make sure they are accurate? And that is where test data comes in. But what about data testing? What is the difference between them, and how do they correlate? Read on to find out about the technicalities and implications of each.
What is the difference between test data and data testing?
As a tester, you often need to create test data or at least identify suitable test data for your test cases. Test data, to put it simply, refers to a set of data used to evaluate the performance of a model or system. Typically, this data is separate from the data that trained the model, and it validates the accuracy and generalization capabilities of the model.
Data testing tests the quality and accuracy of the data itself. This part includes verifying that the data obtained is correct, complete, and consistent, ensuring it meets the requirements of the model and system, as well as complying with the regulations. Why? Because if the data was incorrect in the first place anyway, who cares about the results of the test data?
What is a data test?
As stated, test data is a data set created in sync with the test case. If you want to make sure the application or website works properly, you need to test it. And to test it, you need data that can be generated in multiple ways:
- With automated test data generation tools
- Mass copy of data from production to testing environment
- Mass copy of test data from legacy client systems
This information is valuable to test how well an application works. A researcher collects data to meet the requirements of a test or to determine whether the product is ready for further testing. It can help identify coding errors during the initial stages of the process, giving the workers enough time to make changes before further data testing begins.
What is data testing?
Data testing is a type of software testing. Data testing deals with testable items that are in the back end, hidden from the regular user. These items include SQL, MYSQL, Server, Assembly, and more. It determines whether the application or software has integrity and consistency. During the process, it may create complex queries to “stress” the database and check whether it’s responsive or not.
Data testing involves validating:
- the schema
- database tables
- keys and indexes
- stored procedures triggers
- database server validations
- data duplication
That is why it’s crucial to have top-quality test data in the first place. If the data is insufficient or not good enough, the app might pass the data testing but then fail to its users (whether regular people or powerful institutions) upon release.
If you want to become a data tester, it is essential to have a strong background in databases, servers, and SQL concepts. You can become a pro at these with our online software bootcamps at Test Pro. In a few months, with patience and effort, you will be able to manage all of these concepts and more.
Types of testing data
There are three primary ways of testing data:
- Structural Testing. This technique validates all the elements inside the data repository. These elements are not meant to be manipulated by clients or regular users. To do so, one needs to master SQL.
- Functional Testing. This technique validates the responsiveness of a database from the perspective of the client or regular user. Functional testing determines whether the transactions and operations performed by end-users relate to the database as expected. It answers questions like “Is data logically and well organized?” “Is data stored in the tables correctly?”
- Non-Functional Testing. As its name suggests, this type of testing puts the database and application under stress. It includes stress testing, security testing, compatibility testing, usability testing, and more. It helps determine whether the database is secure, can manage large loads of data, and lacks risk.
As you can see, data testing is fundamental for any institution or company that deals with large databases and applications. That is because it avoids bugs or incorrect information that can upset the clients or the stakeholders. Without data testing or a proper test data set, the initial code could very well be full of errors, which is in nobody’s interest.