Data testing isn’t new. However, it is taking on newfound urgency as major trends involving artificial intelligence (AI) and machine learning, cloud computing, edge computing, and DataOps drive a surge in organizational data consumption.
This mirrors how trends in global food production led to the rise and prominence of food testing and safety protocols. As our food supply becomes increasingly globalized, tracking, quality assurance, and transparency are paramount. Similarly, in the digital realm, as data is sourced and integrated from diverse global sources, ensuring its quality, reliability, and transparency through rigorous testing becomes crucial.
Doing so ensures that data-driven decisions are based on solid, trustworthy information, just as strict food safety protocols guarantee the food we consume is safe and of high quality.
What is data testing?
Data testing is the formalized process of validating the accuracy and quality of data within a system. This makes it an increasingly important process as more organizations and industries look to data-driven decision decision-making and real-time analytics to remain competitive.
The process of testing data involves a combination of techniques and methodologies. Together, they aim to help data teams identify and solve data quality issues that affect its value for data consumers and stakeholders—inaccuracies, inconsistencies, and incomplete data sets.
While the specifics vary across organizations and industries, data testing commonly encompasses four main areas:
- Validation of data integration processes ensures that data from different sources is accurately merged and transformed.
- Performance testing of databases and data processing applications ensures they meet the required speed, efficiency, and scalability.
- Security testing safeguards data integrity and privacy, which is especially critical in the context of regulations like GDPR and CCPA.
- Compliance testing verifies that data handling practices adhere to relevant legal and industry standards.
Does all data need to be tested?
Data quality is imperative for data-driven organizations. Therefore, data testing in some form or another is now essential. Without it, data teams and consumers within the organization would roll the dice regarding the integrity, accuracy, and reliability of the data pouring into and through the business.
More specifically, there are 10 key reasons why data testing is essential:
Ensuring data quality: Data testing verifies that the data obtained is correct, complete, and consistent, which is vital for maintaining high data quality standards. High-quality data is essential for accurate analytics and decision-making.
Identifying coding errors: By testing data, organizations can identify coding errors early in the development process, which saves time and reduces costs associated with fixing issues later on.
Supporting regulatory compliance: Data testing ensures that data management practices comply with relevant standards and regulations, such as GDPR and HIPAA, protecting sensitive information and avoiding legal penalties.
Improving user satisfaction: Accurate and reliable data testing leads to the development of applications that are dependable and of high quality, resulting in increased user satisfaction and trust in the company.
Reducing costs: Identifying bugs early through data testing is significantly cheaper than fixing issues discovered after a product release. This represents potential savings for companies and contributes to a better user experience.
Facilitating better decision-making: Reliable data testing ensures that the data used for analytics and informing business strategy is accurate, which is crucial for making informed business decisions.
Enhancing efficiency: By identifying and addressing issues with systems that store and analyze data, data testing improves the efficiency of these systems and processes, leading to better productivity and cost savings.
Protecting data privacy: Data testing plays a crucial role in ensuring data privacy and security by identifying potential vulnerabilities and ensuring that sensitive information is protected from unauthorized access or breaches.
Reducing data-related defects: Accurate test data reduces the number of data-related defects or false positives, thereby increasing the efficiency of the testing process.
Supporting Agile and DevOps practices: Proper test data management and testing are essential for supporting modern software development practices like Agile and DevOps, which require regular integration and continuous testing.
How to determine what you should test
Determining which data you should test involves a series of steps and considerations to ensure that the test data is representative, relevant, and comprehensive enough to validate the software application effectively. Here are the key steps and best practices for determining the test data:
1. Define clear test data requirements
Before creating or acquiring test data, it's essential to define the test data requirements that match the scope, objectives, and scenarios of your testing activities. This includes specifying the type, volume, format, and quality of the data needed to test the software effectively.
2. Understand the application's data dependencies
Analyze the application’s data dependencies, data types, and data sources to select the most appropriate test data. This involves understanding the features, functions, requirements, risks, and assumptions of the software under test.
3. Use activity diagrams to understand code performance
For Agile practices, activity diagrams are crucial to understanding what specific function a new modification or new piece of code is going to perform. Then, define data to support that use case or set of scenarios.
4. Design a strong data discovery process
A robust data discovery process involves identifying and understanding the data requirements for each test scenario. Proper data discovery helps in selecting the most appropriate test data and ensures that the test environments closely resemble real-world scenarios.
5. Refresh test data regularly
Regularly refreshing test data is essential to maintaining data accuracy and relevance. Outdated or stale data can lead to invalid test results and hinder the identification of critical defects.
6. Employ automation where possible
Automating Test Data Management tasks can significantly improve efficiency and reduce the risk of errors. Automation tools generate test data, refresh test environments, and anonymize data quickly, enabling testing teams to focus on the actual testing process.
7. Create a review and auditing process
Establishing a review and auditing process ensures that the test data is accurate, reliable, and complies with data privacy regulations. Regular reviews by designated stakeholders, along with internal audits, help identify any anomalies or data quality issues.
8. Maintain data security
Ensuring that sensitive and confidential data is protected requires implementing data masking, encryption, and access controls. This ensures that only authorized personnel can access and use sensitive data during testing.
9. Manage test data via business entities
Instead of working to understand the complexities of source systems, databases, tables, and columns, testing teams should be able to simply define the business entities for which they need test data.
10. Extract on the fly
Enable the tester, or test data automation tool, to request the data needed to perform a given test, in flight, without any preparation.
Data testing methods: When to use each
Data testing is a crucial aspect of ensuring data quality and integrity in various applications and systems. Different data testing methods are designed to address specific aspects of data quality and system functionality.
Here's an overview of various data testing methods and the appropriate contexts for their use:
Data completeness testing
Data completeness testing verifies that all expected data is present and that there are no missing values or records. This method is essential when data migrates from one system to another or when integrating data from multiple sources to ensure that no data is lost in the process.
Data accuracy testing
Data accuracy testing involves verifying that the data in the system is correct and accurately represents the real-world entities or transactions it is supposed to model. This method is crucial for applications where data precision directly impacts decision-making, financial calculations, or any other critical business processes.
Data consistency testing
Data consistency testing checks for uniformity in data across different databases, systems, or data stores. It is particularly important in distributed systems or environments where data is replicated across multiple locations to ensure that all copies of the data remain consistent over time.
Data integrity testing
Data integrity testing ensures that the data remains accurate and consistent as it undergoes operations such as transfer, retrieval, and storage. This method is vital for systems that involve complex transactions or operations that modify the data, ensuring that data relationships and constraints are maintained.
Data validation testing
Data validation testing verifies that the data meets specific criteria or constraints, such as data types, ranges, and formats. This method is used in scenarios where data is inputted into the system through forms or APIs to ensure that only valid data is accepted and stored.
Data transformation testing
Data transformation testing involves verifying that data transformation operations, such as aggregations, conversions, and calculations, are performed correctly. This method is crucial in data warehousing and ETL (Extract, Transform, Load) processes where data is transformed before being loaded into the target system.
Data migration testing
Data migration testing is conducted to ensure that data is accurately migrated from one system to another without loss, corruption, or alteration. This method is essential during system upgrades, platform changes, or when consolidating data from multiple sources into a single repository.
Performance testing for Big Data
Performance testing for Big Data involves assessing the system's performance, including data loading speed, data processing speed, and the performance of individual components. This method is critical for applications that process large volumes of data and require high performance to meet user expectations.
Regression testing
Regression testing ensures that new changes, updates, or additions to the system do not adversely affect the existing functionality, including data processing and handling capabilities. This method is used after software updates, bug fixes, or new feature implementations to ensure that the system continues to operate correctly.
Each of these data testing methods addresses specific aspects of data quality and system functionality. The choice of method depends on the particular requirements of the system being tested, the nature of the data, and the specific objectives of the testing process.
Consider testing the idea of data as a product
Data testing is a critical component of treating data as a product because it ensures the data product's quality, reliability, and usability. When data is treated as a product, it is developed, managed, and delivered with a high level of care and attention, akin to any other product offering.
Thus, our analogy with food isn’t merely playful. We are increasingly recognizing the problems created by prioritizing the availability of food over its quality, and, by extension, its lasting impact on our health.
As increasingly complex organisms in a digital ecosystem, this helps explain why the movement to treat data as a product is gaining momentum. Competitive advantages are increasingly reliant on the implementation of new tools, data contracts, and other digital transformation initiatives that drive holistic and sustainable data-driven decision-making.
That said, are you hungry to learn more? Exciting developments are on the horizon regarding data contracts and data quality in general. So, make sure you’ve signed up to join our product waitlist.
This article is part of our blog at Gable.ai — where our goal is to create a shared data culture of collaboration, accountability, quality, and governance.
Currently, our industry-first approach to drafting and implementing data contracts is in private Beta.
Curious to learn more? Join the product waitlist to be notified when we launch.
-#-
