Digital Transformation Quality at Speed

According to IDC, worldwide spending on digital transformation (DX) will reach $2.3 trillion in 2023, growing 17% per year and representing 53% of all IT spending. Annual enterprise budgets for DX are huge, ranging from $5 million to over $50 million for the majority of companies surveyed.

Unfortunately, many of these well-funded initiatives are doomed to fail as multiple studies report 70% to 85% of digital transformation efforts will fail to achieve their goals.

There are many reasons for this lack of success, but chief among them is quality. DX quality is critical to the delivery of accurate business intelligence, efficient process automation and creating a digital customer experience that maximizes revenue and retention across the lifecycle.

Here are some real-world examples of DX initiatives:

  • In retail, digital information enables the use of predictive models for understanding buyer preferences and creating personalized marketing campaigns that drive higher product sales.
  • In healthcare, data is used for modeling the spread of disease and to predict the outcome of various treatment options.
  • In banking, business intelligence tools use analytics to identify lending risks and detect fraudulent transactions.
  • In financial services, big data is used for large-scale algorithmic stock trading to maximize the return on investment portfolios.
  • In call centers, performance metrics are combined with processing call content to identify recurring product problems and improve the call center response.
  • In operational environments, machine data is used to identify patterns and predict system failures, build preventive maintenance programs and maximize operational efficiencies.

Ensuring data quality for DX applications is both critical and challenging.

  • Data is often sourced from disparate silos in different departments or lines of business
  • Human-dependent data collection is a leading and recurring cause of errors and omissions
  • Duplicate data collected over time and in different ways can introduce inconsistencies
  • Raw data not properly transformed by an ETL process can produce erroneous results
  • If newly integrated DX applications are released without adequate testing and data validation, DX quality will suffer, and strategic initiatives will underperform. Gartner estimates the average annual cost of poor data quality at $15 million and considers data quality a problem that undermines the effectiveness of DX initiatives.

    DX breaks down existing data silos and replaces monolithic systems with an integrated and adaptive microservices environment. Complex workflows use APIs to span multiple application environments as they aggregate and transform data into actionable business information.

    Ensuring DX quality at speed requires the use of intelligent test automation to maximize the coverage of code and ensure the quality of data. Automated test procedures require controlled and accurate test data to validate the business logic of end-to-end workflows and the accuracy of information produced by newly integrated digital applications.

    Successful Digital Transformation Requires Intelligent Automation

    DX has led to widespread adoption of DevOps and Agile to accelerate the development of these large complex systems. Small components of code are continuously checked in to a delivery pipeline for integration and testing prior to their production release. According to Gartner, one of the most difficult challenges encountered during the adoption of this model is ensuring the quality of the solutions developed.


    This puts pressure on QA to deploy comprehensive automated testing at all levels of the Agile software testing pyramid – a layered testing approach where automated tests at each level ensure quick delivery of quality code to the next higher level.

    Test Data Management (TDM), the approach created for provisioning data for manual testing is no longer sufficient to meet the demands of Continuous Integration and Delivery (CI/CD) and DevOps. Many QA teams have resorted to manually creating test data using Excel spreadsheets. While this approach can deliver the precise data they need for testing, it’s too tedious and too time-consuming for a CI/CD environment operating at scale.

    QA teams need total control over patterns and permutations of data to maximize test coverage. They need control over the structure and format of data used to replicate data feeds in a mixed-system environment. And they need control over the volume, velocity and security of the data used for all testing. Test data can, and should, be designed in combination with each test case, whether it’s a simple unit test, an integrated API test or a complex end-to-end test.

    QA must have the ability to design the test data needed for each category of testing at a moment’s notice, in terms of variety, volume, structure, security and output format. In other words, test data provisioning must be an on-demand, by-design, self-service capability.


    GenRocket calls this approach intelligent test data provisioning and it requires advanced Test Data Automation (TDA) technology. TDA has the ability to generate real-time synthetic test data, blended with specific production data values, using data designed to validate test objectives. When intelligent test data provisioning is combined with test automation, the result is intelligent automation, a critical success factor for DX quality assurance.

    GenRocket’s Staged Deployment Framework for Intelligent Automation

    The best way to deploy Intelligent Automation across an organization is by following a staged deployment process. The approach is straightforward – start with the most basic and highest volume of tests at the Unit Level. Then gradually progress up the pyramid to intelligently automate the more complex Service and UI Levels.


    At the Unit Level, test data can be made available for developers and testers with speed and precision. This allows fully tested code to be passed to integration testing at the Service Level. Once code is successfully integrated, it can undergo end-to-end system testing at the UI Level.

    Intelligent Automaton allows more comprehensive testing with greater speed and accuracy because tests are data driven. This subjects code to a full range of scenarios through positive and negative testing, boundary and edge case testing, performance and load testing and compliance and security testing. Through the use of automation, test procedures and data provisioning are available for testers to run at any time. Tests can be re-run for automated regression testing. All test procedures and test data scenarios can be stored, modified and version controlled for use by multiple QA teams.

    The diagram below shows a fully deployed Intelligent Automation framework. It illustrates GenRocket solutions for all forms of functional and non-functional testing. These solutions are based on real-world experiences with QA challenges at large enterprises in a variety of industries. The framework also illustrates the use of automated regression testing and the maintenance of automation projects throughout the test automation lifecycle.


    GenRocket and its partners provide educational resources and professional services to assist customers with this staged deployment process. Best practices are followed and new solutions are continuously added to our knowledgebase to increase the depth and breadth of GenRocket‘s Intelligent Automation methodology.

    If you would like to view a solutions video that demonstrates Intelligent Automation in action, click the link below to view a banking solutions demo.


    If you would like to learn more about how Intelligent Automation can be used to ensure DX quality for your organization, contact us to schedule a live demonstration with GenRocket Test Data Automation experts.