Digi-Data - Decoding TDM approach for Digital Assurance
Recent studies and trends indicates “Digital Assurance” will top software testing trends in coming years. As per latest reports, 30 - 35% of the QA and Testing Budget for new development is spent on customer channel solutions. Digital transformation will continue to drive IT strategy which has direct impacts on QA and testing functions. This provides great opportunity for digital testing to move from ‘Quality Assurance’ to ‘Digital Assurance’ that will measure, quantify and better understand the user experience by executing end to end comprehensive testing across the digital value chain (digital marketing, asset management, online analytics) and digital eco systems (Social, Mobility, Analytics & Big data, Cloud).
With the emerging technologies and customer facing applications getting complex, it is indeed imperative to assure the business across multiple digital platforms, devices and services. As digital assurance involves testing across digital eco system and digital value chain, we need to have a holistic and hybrid approach to manage provisioning of data to different needs like functional, performance, automation, security areas.
Testing apps properly means testing against real data, which means a copy of the production database has to be masked and set up on IT systems the test teams can use. Provisioning the hardware and software for a testing team can take weeks; then resetting each test can take hours, which means there's a limited amount of time to get the bugs out.
Providing right data to right environment at the right time helps the business to do proper testing and test data plays a vital role in digital testing across the end to end life cycle. It is important to have a right approach that will help to manage and optimize complex data set to support Agile, CICD and DevOps environments. It should focus on early detection of defects, faster time to market considering continuous integration and continuous deployment model, customer centricity and outstanding client experience. This involves considering different activities like providing data from production after masking, creating synthetic data based on test coverage, service Virtualisation etc.
Test Data Management for digital testing will be more challenging as it involves different technology around cloud, big data, social media, mobility areas. There are different options available to manage data for digital assurance platform. Let’s take a look at the different methods, tools involved-
• Traditional tools like Informatica ILM, IBM Optim provides comprehensive test data solution for subsetting, masking and provisioning with support to cloud environments. They were already established and market players in test data landscape. CA tools like test data manager also supports the above functionalities.
• Data Virtualisation tools like Delphix, Denode, TrustRadius helps to develop data management approach that allows application system to retrieve and manage data without requiring underlying technical details like where it is stored, what format and what type. This will ease the test data provisioning methods as it requires less technical knowledge on underlying database.
• Synthetic data generators help in creating manufactured data on the fly and in minutes. This will be best suited if the customer is looking for a quick solution with less cost. Again this will solve only for testing scenarios where usage of production data (integrated data from end to end perspective) is limited.
• Mobile application testing should get supported with automation lab equipped with devices, infrastructure, tools and data (test data, bench marks etc.)
• With Service Virtualisation and DevOps, teams can use the virtual services instead of production services thereby enabling frequent and comprehensive testing even when the core components are under development or missing from the system architecture. Some of the leading tools like CA Lisa, Parasoft, SoapUI will help testing with de-sensitizing and maintaining simulated / virtual data across multiple services.
• Data masking plays a major role in implementing relevant data from production. This includes implementing Industry standards, compliance, regulatory and customized logic specific to the customers. Multiple tools are available in the market, but one has to choose the right tool that can support the compliance and regulatory workflow, supports end to end masking with data integrity intact and helps in customizing the data masking logic customized to specific domain.
• There are few disruptive tools in market which can quickly snapshot, replicate, and deploy complete application service environments across virtually any cloud in minutes. These are developed on containers concept which will host all the dependent services (apps, environment, data) in one shot.
Availability of test data from the early phase of SDLC will allow continuous improvement, cost optimization, better product quality and improved customer experience. Utilization of a ‘Data Driven’ framework combined with Continuous Integration (CI) can be leveraged to improve the interaction of teams and allow for the most effective test cases in order to automate functional, regression, system integration and UAT testing.
An effective and efficient TDM strategy should focus on Agile/DevOps implementation framework, Self-service test data provisioning, Test data versioning, Synesthetic data for new development, Integration with Virtualisation tools, data masking, cloud support (public or private) and enabling end to end automation. It also should focus on analyzing the data storage, prevent from insecure data storage and analyze different data streams and preventing any vulnerabilities.