Empowering Software Testing through Test Data Management

Test data management is the process of creating non-production data sets to represent the organization’s actual data. In this article, let’s discover the test data management techniques that strengthen the software testing process.

Organizations use critical applications to improve efficiency and increase the ROI metrics of the business. There are modern approaches such as agile methodologies to quickly deliver efficient results. But have you ever wondered of the non-production data?

Test data management is the process of creating non-production data sets to represent an organization’s actual data so that it can be used by development engineers to perform accurate and valid test cases.

The Growing Need for Test Data Management

In early 2018, four major banks in the US underwent an immense load on the banking system as the services were not accessible via their mobile apps; this had the clients being restless and frustrated for several hours. The whole scenario came up due to a major traffic spike due to a payday. This operational inefficiency led to the reputational damage of the banks, which further went to dilute the brand value as the customers took up to the social media handles like Twitter, where they vented out their frustration with negative posts on the banks. This whole event was not a pleasant experience, as the inefficiency of the banking system to work under pressure led them to pay a fortune.

The above experience of the banks focuses on the importance of software testing. If the systems were thoroughly tested for functionality and performance even during heavy loads, the above scenario could have been easily avoided. Hence, software testing forms an integral part of the software development process. However, software system testing is only as good as its test data management strategy, which should be adopted and implemented to:

• Test and development process management to meet testing and application development requirements
• Identify appropriate replicable accounts and transactions from production to meet test criteria
• Secure data and streamline cloning processes, delivering clones needed to meet upgrade and patch cycles as well as maintain data security
• Mitigate the threat of identity theft concerns among consumers and regulators
• Increase turn-around-times during system upgrades through improved planning of data refreshes and overall data utilization
• Testing forms an integral part of test data management

Data management can be defined as the science of creating and maintaining the data sets generated by software system testing, driven by cause-effect relationships, producing predictable outcomes and responses. These data sets can be created by replicating each stage of the transaction, to produce the synthetic data. However, data generated by actual tractions is called migrated data, which forms the authentic sets. Data generation by both methods has its own set of values and challenges.

Synthetic data needs to be interoperable, and capable of performing in diverse systems and environments without having an effect on the complexity of inter-relationships. It is required for the data to be valid across the multiple rounds of manual testing, however, when it is in an automated environment, it should perform without manual intervention. On the other hand, migrated data that has been taken from authentic transactions has limited reusability across different test cycles, restricted by data confidentiality obligations.

Mitigating Risks Inherent in Test Data Management

The potential risks and associated impacts of implementing test data management should be strategized before working on test data management. The complexity of the data depends on different scenarios like whether the data is structured or unstructured or if the databases are new or from the existing systems. If kept in multiple environments, it may have the added challenge of access sensitivities and potential confidentiality breaches. Assessing the time available for data discovery, its generation and management is another factor that is important in ensuring representative samples as obtained.

Test data management requires different types of data for different types of testing. The kind of data required for performance testing may differ from the one required for user acceptance, hence, organizing data correctly is of paramount importance. Another potential risk is the measure to which the organization operates in a distributed multi-supplier or outsourced environment, with multiple users accessing the data in multiple locations. Operating in such an environment also highlights the importance of data security and protection. As a standard, new security protocols will be required as well as staff training, highlighting the importance of protecting live production data as well as guarding test environments.

Improving Software Testing through Test Data Management

To meet the requirements of time and efficiency, organizations should consider the following test data management techniques, however, all sensitive information should be masked while implementing the method.

• Database replication should be carried out by copying production data
• Data sub-setting should be done by substituting production data when appropriate
• Synthetic data generation, through the production of synthetic data based on a clear understanding of the underlying data model, requires no de-identification

The modern business environment is highly dynamic and should be optimized for efficient process continuity. Any discrepancies will create a negative impact on the entire system. Accordingly, if the process of choosing, understanding, and analysing the test results is time-consuming, tedious, and requires specific knowledge of underlying applications, it will have a direct negative impact on the entire system.