Organisations can handle this and make better decisions only when they get to integrate and analyse test and production monitoring data successfully. Quality test data management enables efficient testing that guarantees ROI (Return on Investment) and mitigates operational business risk, reduces development and maintenance costs and delivers effective and timely systems to the business. The detailed insights given into possible risks for a successful project promotes the effectiveness and control of test data management.

Software development teams are the ones responsible for updating data and protecting organization’s functions from any small defect that would cause serious trouble. In order to achieve this, a serious testing strategy -that is highly connected with the governance of test data- is needed. A flexible dynamic development process which helps in achieving the goal of easily creating targeted and right-sized test databases is the result of rapid access to the appropriate test data and easy management.



Simplify the testing process by following these 3 steps for successful test data management:

Discover and understand the test data                                                                     
Given the variety of sources and types of data, a lot of work needs to go into preparing the data before it is stored and analyzed. The data could vary in quality (e.g., an address may be missing a ZIP code or may contain a spelling mistake), may not be consistently recorded in the same manner in different sources, and may have a different format.

Teams should identify their test data requirements based on the test cases— which means they must capture the end-to-end business process and the associated data for testing. They should think of data in terms of design decisions – in terms of the requirements themselves, designing test cases with data linked directly to them. Aligning test data to requirements ensures that it is ‘fit for purpose’, while the ability to provision it quickly to test teams means that they can quickly respond to the changing demands of the business.

Stay Compliant
Legislation covering the use of data, especially personal data exists in many industries and is continuously increasing. Current data protection and legislation such as HIPAA, PCI DSS, the EU Data Protection Directive and the UK Data Protection Act means that much more vigilant practices around the use of data need to be adhered to. In fact, the use of production data in non-production environments carries huge risk in terms of financial penalties and brand damage as it is against regulations to use data for any other reason than its stated purpose.

Personally identifiable or otherwise confidential data (like names, addresses, phone numbers, email addresses, customer orders) or organization's sensitive information should never be included in test data sets in order to be compliant with data protection requirements and regulations.  The best option is the use of synthetic test data with the characteristics of production data but with no sensitive content, to ensure that data are safe and reflect the real-world scenarios so that the test cases will accurately portray what will happen when the app is live.

Automate, Automate, Automate!
Although the developers and testers can internally develop all TDM functions using scripts, including copying, transforming, masking, securing, cloning, and refreshing, consider automating them to minimize human errors. The overall quality of the application is protected since you identify problems -that might otherwise go undetected- before the product is released. The most efficient way to identify data inconsistencies during testing and achieve an overall improvement of application’s quality is to use an automated tool for comparing the baseline test data against successful tests’ results.