Data Integrity Testing Best Practices

The term “Best Practices” defines a technique that reliably leads to a proven result. The best practice for data testing is built on a foundation with four corners that are the keys to successful data testing. These best practices focus on objectives, cooperation, clarity and efficiencies.  This overcomes ineffective and error-prone processes that really do not validate data integrity. Through these four corners in the data testing best practices, there are measurable quality results with provable data integrity.

The Four Corners of Data Testing

1. Measurable Quality Objectives
Proving data integrity is troublesome without specific objectives. It will cause the team to focus on the wrong needs and fall short of accurate integrity validation. Create measurable data quality goals supported by objectives that focus on attributes of accuracy, completeness, validity and consistency. 

Use these attributes to define one or more attainable objectives. Describe how objectives are to be achieved and in what time frame. Be sure to perform a reality check ensuring the goals are attainable and support the process of data integrity.

2. Collaboration
Testing data is a matter of validating deliverables that describe the proper use of information, correctness of data analysis, reliability of integration mechanisms and accuracy of transformation rules. Collaboration points are used throughout the integration and design efforts to capture these deliverables, enabling the test team to effectively test integration.

Data integration efforts are typically problematic for test teams as testable requirements are thin while favoring other deliverables like data analysis, technical specifications and transformation rules. Inject enough touch points with the analysis and development efforts to ensure test planning and design encapsulates these deliverables that are at the heart of the integration.

3. Modeling
Following data through the subsystems and transformation paths is the only true way to validate and test data. Abstracting the integration subsystems and transformation paths into models is the best way to follow the data. At the same time, it creates a top-down analytical view that simplifies a complex system. Simply, it helps eliminate ambiguities of text.

Models are used to represent the system as the designers conceptualize their work. This is useful for ensuring proper test coverage and critical junctures in the design. Models are also excellent for capturing the test workflow making it easer for the test team to divide and implement the work.

4. Automation
Automating data testing reduces errors and provides a strategy for ongoing integrity validation. Through automation, the team is able to develop repeatable tests that can automatically analyze and evaluate the results of data integration. Automation also removes the tedious tasks of test setup and execution. Because automation allows the team to concentrate on test design rather than execution, the team is able to increase test coverage while digging deeper into the validity checks.

Manual data testing is no alternative to automation. Manual data testing is resource intensive, tedious and prone to errors. It cannot be repeated and may cost several times more than application testing. Automation is vital to ongoing data validation because data can be changed in dozens of places by any person or system that touches it even after integration development.