# Infosys Whitepaper 
Title: Solving the Test Data Challenge to Accelerate Digital Transformation 
Author: Infosys Limited 
Format: PDF 1.7 

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 WHITE PAPER SOLVING THE TEST DATA  CHALLENGE TO ACCELERATE DIGITAL  TRANSFORMATION Abstract Organizations are increasingly adapting to the need to deliver products and  services faster while continuously responding to market changes.<|endoftext|>In the age of mobile apps, test automation is not new. But traditional test  data management (TDM) approaches are unable to help app development  teams address modern delivery challenges. Companies are increasingly  struggling to keep up with the pace of development, maintain quality of  delivery, and minimize the risk of a data breach.<|endoftext|>This white paper illustrates the need for a smart, next-gen TDM solution to  accelerate digital transformation by applying best practices in TDM, zero- trust architecture, and best-in-class test data generation capabilities.  {{ img-description : two people looking out of a glass door in a building, in the style of light navy and dark azure, security camera art, webcore, uniformly staged images, selective focus, grainy, masculine (to left) }} 

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 External Document © 2021 Infosys Limited 

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 External Document © 2021 Infosys Limited Traditional Test Data Management.........................................................................................4 Why the New Normal was not Enough?.................................................................................4 Five Key Drivers and Best Practices in Test Data Management.........................................5 Future-proof Test Data through Next-gen TDM Innovation..............................................8 Accelerate through Next-gen TDM Reference Architecture...............................................9 The Way Forward - Building Evolutionary Test Data for your Enterprise...................... 11 About the 
Authors................................................................................................................... 12 Figure 1. Key focus areas emerging in test data management.........................................4 Figure 2. Key drivers and best practices in TDM...................................................................5 Figure 3. Zero trust architecture..............................................................................................6 Figure 4. Stakeholder experience............................................................................................7 Figure 5. Focus areas of Infosys Next-Gen TDM....................................................................8 Figure 6. Infosys Next-Gen TDM reference architecture.....................................................9 Figure 7. Contextual test data and its different formats.................................................. 10 Table of Contents Table of Figures 

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 External Document © 2021 Infosys Limited Traditional Test Data Management  Test data management (TDM) should ensure that test data is of the highest possible quality and available to users. In the digital age,  managing test data using traditional TDM practices is challenging due to its inability to accelerate cloud adoption, protect customer data,  provide reliable data, avoid data graveyards, ensure data consistency, and automate and provision test data.<|endoftext|>Why the New Normal was not Enough? While the ‘new normal’ has become a catchword in 2021, in the world of testing, this ‘normal’ was not effective for many organizations. The  pressure to adapt to changing customer expectations, new technology trends, changing regulatory norms, increased cybersecurity threats,  and scarcity of niche skills has raised many challenges for organizations. In light of this, many are wondering whether they should revisit their  test data strategy.  Figure 1. Key focus areas emerging in test data management As time-to-market for products and  services becomes critical, test data  generation and provisioning emerge as  bottlenecks to efficiency. Further, test data  management has been represented as  the weak link for organizations looking to  accelerate digital transformation through  continuous integration and delivery. High  quality test data is a prerequisite to train  machine learning (ML) models for accurate  business insights and outcome predictions.  To build a competitive difference,  organizations today are investing in three key  focus areas in test data management (refer  Figure 1): • New business models – With a strong focus  on customer experience, organizations  must adopt new business models and  accelerate innovation. There is a need to  generate data that can be controlled and  is realistic as well as accurate to meet real- world production needs.  • Hyper-productivity – Automation  and iterative agile processes push the  need for better testing experiences  with faster and more efficient data  provisioning, allowing organizations to  do more with less.<|endoftext|>• New digital workplace – Millions of  employees are working from home.  Organizations must focus on building  a secure, new-age digital workplace to  support remote working.<|endoftext|>Changing Customer  Expectations Head winds – New  Tech Trends Regulatory  Changes Cyber Security  Threats Skill Scarcity APPS Hyper productivity - Agile New Business Models  - Multi-cloud  environment Employees Partners Customers New Digital  Workplace Community Data Privacy and  Security 

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 External Document © 2021 Infosys Limited Five Key Drivers and Best  Practices in Test Data  Management Companies are increasingly struggling to  keep up with the pace of development,  maintain quality of delivery, and achieve  absolute data privacy. On-demand  synthetic test data is a clear alternative to  the traditional approach of sub-setting,  masking, and reserving production data  for key business analytics and testing. In  this context, three key questions to ask are: 1. What are the drivers and best practices  to be considered while building a test  data strategy?  2. How can CIOs decide what is the right  direction for their test data strategy? 3. What are the trade-offs in test data  management? There are five elements – cost, quality,  security and privacy, tester experience, and  data for AI – that drive a successful test  data management strategy. Understanding  the best-practices around these will guide  CIOs in making the right decision.  Figure 2. Key drivers and best practices in TDM Accelerate Next-gen TDM Adapting for agile & devOps | Self-serviced data provisioning | Increased test data automation Test data automation {{ img-description : a man in glasses is holding a tablet in front of a room full of server racks, in the style of light gray and indigo, uniformly staged images, restored and repurposed, ue5, traditional  }} 

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 External Document © 2021 Infosys Limited Key Drivers Impact on Test Data Strategy Best Practices 1.  Cost What is the return on investment (ROI) and  acceptable investment to create, manage,  process, and, most importantly, dispose of test  data?  Production data must be collected, processed,  retained, and disposed of. The processing and  storage cost must offset the investment in TDM  products. Procurement, customization, and  support costs need to be considered.<|endoftext|>• Test data as a service – Test data on cloud with a subscription for  testers can lower the provisioning of full-scale TDM.<|endoftext|>2.  Quality Do we have the right quality of data? Can we  get complete control over the data? Can we  generate test data in any format? Testers have very limited control over the data  provided by production. The test data is usually  a subset of data from production and cannot  cater to all the use cases including negative and  other edge use cases. Further, there is a need to  generate electronic data interchange (EDI) files,  images, and even audio files for some of the use  cases.<|endoftext|>• TDM suite can help build a subset of data designed with realistic and  referentially intact test data from across the distributed data sources  with minimal cost and administrative effort.<|endoftext|>• Synthetic data generators should have the breadth to cover key data  types and file formats along with the ability to generate high-quality  data sets, whether structured or unstructured, across images, audio  files, and file formats.<|endoftext|>3.  Security  and privacy Do we have the right data privacy controls while  accessing data for testing? How do we handle a  data privacy breach? The focus on privacy and security of the data  used for testing is increasing. Complying with  GDPR and ensuring the right data privacy  controls is a catalyst for organizations to move  away from using direct production data for  testing purposes. There is increased adoption  of masking, sub-setting, and synthetic data  generation to avoid critical data breaches when  using sensitive customer, partner, or employee  data.<|endoftext|>• Zero trust architecture provides a data-first approach, which is secure  by design for each workload and identity-aware for every persona in  the test management process including testers, developers, release  managers, and data analysts.<|endoftext|>         Figure 3. Zero trust architecture • To ensure security of sensitive information, organizations can create  realistic data in non-production environments without exposing  sensitive data to unauthorized users. Enterprises can leverage  contextual data masking techniques to anonymize key data elements  across an enterprise.<|endoftext|>