7 data testing methods, why you need them & when to use them?

Data testing involves the verification and validation of datasets to confirm they adhere to specific requirements. The objective is to avoid any negative consequences on business operations or decisions arising from errors, inconsistencies, or inaccuracies. In a world where organizations rely heavily on data observability for informed decision-making, effective data testing methods are crucial to ensure high-quality standards across all stages of the data lifecycle—from data collection and storage to processing and analysis.This is part of a series of articles about data quality.

Find out the reasons why it is important to perform data testing.

1. Ensuring accuracy

One of the primary reasons data testing is essential is to ensure the accuracy of the data. Inaccurate data can lead to faulty decision-making, which can have severe consequences for a business. Data testing methods help identify and rectify errors, inconsistencies and inaccuracies in the data, ensuring that businesses have access to accurate and reliable information.

2. Maintaining data integrity

Data integrity refers to the consistency, accuracy and reliability of data over its lifecycle. Maintaining data integrity is vital for businesses because it ensures that data remains accurate and consistent even when it is used, stored, or processed. Data testing methods play a crucial role in preserving data integrity by identifying and resolving issues that could compromise the quality of the data.

3. Optimizing performance

Data testing methods are also essential for optimizing the performance of data systems and applications. By identifying bottlenecks, inefficiencies and performance issues, data testing methods enable businesses to optimize their data systems and applications to deliver optimal performance. This results in faster, more efficient data processing, cost savings and improved user experience.

Related content: Learn about data reliability

Here are a few common data testing methods you can use to improve the quality and integrity of your data.

1. Data completeness testing

Data completeness testing is a crucial aspect of data quality assurance. This method ensures that all required data is present in the system and no critical information is missing. Data completeness testing involves checking if all records, fields and attributes are present and verifying that they are populated with the appropriate values.

The first step in data completeness testing is to define the requirements for the dataset. This entails identifying the mandatory fields, records and attributes that must be present in the system. Next, you need to create test cases and test data that cover all possible scenarios where data may be missing or incomplete. Finally, execute the test cases and analyze the results to identify any gaps in the data.

When to use this method: Data completeness testing is essential when you’re migrating data between systems, integrating new data sources, or implementing new business processes that require additional data. It is also vital during data warehousing and reporting projects, where incomplete data can lead to incorrect insights and decision-making.

2. Data consistency testing

Data consistency testing focuses on ensuring that data across different systems or databases is consistent and follows the same rules and standards. Inconsistent data can lead to inaccuracies and affect the reliability of reports and decision-making processes.

To perform data consistency testing, you first need to identify the rules and standards that should be applied to the data. These may include data formats, units of measure, naming conventions and other domain-specific rules. Once the rules are defined, you can create test cases that check if the data follows these rules and standards.

When to use this method: Data consistency testing is crucial when you’re working with data from multiple sources, integrating systems, or consolidating databases. It is also important during data migration projects, where data is moved from one system to another and must maintain its consistency.

3. Data accuracy testing

Data accuracy testing verifies that the data in the system accurately represents the real-world entities it models. Inaccurate data can lead to incorrect analyses, faulty decision-making and overall mistrust in the data.

To perform data accuracy testing, you need to define the accuracy requirements for the dataset. This may include acceptable error rates, tolerances and thresholds for different data elements. Next, you need to create test cases that check if the data meets these accuracy requirements. You can use various techniques, such as comparing the data against known accurate sources, using statistical methods, or employing data profiling tools.

When to use this method: Data accuracy testing is essential for organizations that rely heavily on data for decision-making, such as financial institutions, healthcare providers and government agencies. It is also critical when implementing new data sources, as inaccurate data can lead to cascading errors and diminish the value of the entire dataset.

4. Data integrity testing

Data integrity testing aims to ensure that the data in the system remains unaltered and maintains its consistency and accuracy throughout its lifecycle. This includes verifying that data is protected from unauthorized access, corruption and loss.

To carry out data integrity testing, you need to define the integrity constraints and requirements for the dataset. These may include referential integrity, unique constraints, primary and foreign keys and other business rules that must be enforced. Once the requirements are defined, you can create test cases that check if the data adheres to these constraints and requirements.

When to use this method: Data integrity testing is essential when implementing new systems, databases, or applications that interact with the data. It is also important during data migration and integration projects, where data is moved or transformed and must maintain its integrity.

5. Data validation testing

Data validation testing ensures that the data entered into the system meets the predefined rules and requirements. This type of testing focuses on verifying that the data conforms to the expected format, range and other rules to ensure it is suitable for further processing and analysis.

To perform data validation testing, you need to define the validation rules and requirements for the dataset. These may include data type checks, range and length restrictions and format validations. Next, you need to create test cases that check if the data is valid according to these rules and requirements.

When to use this method: Data validation testing is crucial when developing new systems, applications, or databases that require user input. It is also essential during data migration and integration projects, where data is moved or transformed and must adhere to specific validation rules.

6. Data regression testing

Data regression testing is the process of retesting data-related components in a system or application after changes have been made. This type of testing aims to ensure that the changes have not introduced new defects or caused existing defects to reappear.

To perform data regression testing, you need to identify the components that have been affected by the changes and the related data elements. Then, you need to create test cases that cover these components and data elements, focusing on the areas that are most likely to be impacted by the changes.

When to use this method: Data regression testing is critical when implementing changes to the system, such as software updates, bug fixes, or new features. It is also important during data migration and integration projects, where changes to the data or its structure may affect the system’s behavior.

7. Data performance testing

Data performance testing focuses on ensuring that the system can efficiently handle the volume and velocity of data it is expected to process. This type of testing verifies that the system can meet the required performance criteria, such as response times, throughput and resource utilization.

To carry out data performance testing, you need to define the performance requirements for the system, such as the maximum number of concurrent users, the acceptable response times and the expected data volumes. Next, you need to create test cases that simulate these scenarios and measure the system’s performance under different conditions.

When to use this method: Data performance testing is essential when designing and implementing systems that handle large volumes of data or have strict performance requirements. It is also critical during data migration and integration projects, where changes to the data or its structure may affect the system’s performance.

Learn more about the IBM® Databand® continuous data observability platform and how it helps detect data incidents earlier, resolve them faster and deliver more trustworthy data to the business. If you’re ready to take a deeper look, book a demo today.