Continue # Infosys Whitepaper 
Analyze Discover Data Masking Data Generation Data Copy / Sub-Set Validate Export & Refresh Virtualize Production Production Clone Production Sub-set Gold Copy Gold Copy (Sub-set) Gold Copy (SyntheticData) Masking Data Generation Sub-setting Non-Production DB Logs Files Virtualized Clone Developer Tester Release Manager Data Privacy Architecture Sensitive Data Discovery Self Service Virtualize Test Data Set up Contextual Synthetic Data Data Sub-setting Test Environment Provisioning Monitoring Diferential Privacy Data Scientist / Analyst 

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 4.  Tester  experience Are we building the right experience for the  tester? Is it easy for testers to get the data they  need for their tests? Customers struggle to meet the agile  development and testing demands of iterative  cycles. Testers are often forced to manually  modify the production data into usable values  for their tests. Teams struggle to effectively  standardize and sub-set the production data  that has been masked and moved to test data.<|endoftext|>Figure 4. Stakeholder experience • Test data automation puts the focus on tester experience by enabling  a streamlined and consistent process with automated workflows of  self-service test data provisioning • Test data virtualization allows applications to automatically deliver  virtual copies of production data for non-production use cases. It also  reduces the storage space required.<|endoftext|>5.  Data for AI Do we understand insights generated by the  data?  The probabilistic nature of AI makes it very  complex to generate test data for training AI  models.   • Adopt mechanisms for data discovery, exploration and due diligence.  Data resides in different formats across systems. Enterprises must  identify patterns across multiple systems and file formats and provide  a correct depiction of the data types, locations, and compliance  rules according to industry-specific regulations. They should also  focus on identifying patterns, defects, sub-optimal performance, and  underlying risks in the data.<|endoftext|>• For data augmentation, analysts and data scientists can be provided  with datasets for analysis. The datasets must be resistant to  reconstruction through differential privacy for effective data privacy  protection.<|endoftext|>External Document © 2021 Infosys Limited Stakeholder Experience Developer Not able to get the  right data for development Customer We are prepared to move the IT delivery to another consulting frm if you cannot handle Data Privacy and Security IT Head Not able to provision and create gold copy for testing Tester We need the right test data without sensitive  data, else we cannot fnish testing Business Sponsor We want zero defects, and no data security breaches in Product Development Scrum Master We had to meet critical business requirements; we  could not provision the right data for Testing {{ img-description : two people are looking at computers in a server room, in the style of miscellaneous academia, light silver and indigo, glazed surfaces, iso 200, rubens, weathercore, traditional craftsmanship }} 

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 1. Tester user experience – Testers  need to assess business and technical  requirements from the perspective of  testability as well as end users. Infosys  Next-Gen TDM provides a framework  that includes testers and gives them a  360-degree view of the TDM process.<|endoftext|>2. AI-driven data discovery – Modern test  data resides on a tower of abstractions,  patterns, test data sources, and privacy  dependencies. One of the key features  of Infosys Next-Gen TDM is smart data  discovery of structured and unstructured  data using AI. This helps uncover: •  Sensitive data (PII/PHI/SPI) to avoid data  privacy breaches •  Data lineages to build the right contextual  data while maintaining referential  integrity across child and parent tables 3. Data virtualization – This is needed for  organizations to access heterogeneous  data sources. Infosys Next-Gen TDM  provides a lightweight query engine that  enables testers to mine lightweight copies  that are protected.<|endoftext|>4. Data provisioning – There are numerous  challenges faced by testing teams in  getting access to the right data. Large  enterprises need approvals to access data  from businesses and app owners. Infosys  Next-Gen TDM provides an automated  workflow for intelligent data provisioning.  With this, testers can request data and  manage entitlements as well as approvals  through a simplified UX.<|endoftext|>5. Privacy-preserving synthetic data – It  is important to protect personal data  residing in the data sources being  curated for test data. There is always a  risk of personal data being compromised  when there is a large amount of training  or testing data involved. It can result  in giving too much access to sensitive  information. Improper disclosure of such  data can have adverse consequences  for a data subject’s private information.  It may put data subject at more risk of  stalking and harassment. Cybercriminals  can also use data subject’s bank details  or credit card details to degrade subject’s  credit rating. Privacy-preserving synthetic  data focuses on ensuring that the data  is not compromised while maximizing  the utility of the data. Differential privacy  prevents linkage attacks, which cause  records to be re-identified even after  being anonymized for testing.<|endoftext|>6. Smart augmentation of contextual  datasets – Dynamic data can change  its state during an application testing  process. To generate dynamic data, the  tester should be able to input the business  rules and build both positive and negative  test cases. Infosys Next-Gen TDM provides  a configurable rules engine that generates  test data dynamically and validates this  against changing business rules.   7. Image and audio file generation – Infosys  Next-Gen TDM can create audio files and  image datasets for AR/VR testing using  deep learning capabilities.<|endoftext|>8. Special file formats – Customers need  access to special communication formats  such as JSON, XML, and SWIFT, or specific  ones such as EDI files. Infosys Next-Gen  TDM provides templates for generating  various file formats.<|endoftext|>9. Intelligent automation – Built-in  connectors for scheduling the processes  of data discovery, protection, and data  generation allows testers to model,  design, generate, and manage their own  test datasets. These connectors include  plug-ins to the CI/CD pipeline, which  integrate data automation and test  automation.<|endoftext|>Future-proof Test Data through Next-gen TDM Innovation Every organization needs simplified testing models that can support a diverse set of data types. This has never been a higher priority.  Infosys Next-Gen TDM supports digital transformation by focusing on 9 key areas of innovation (see Figure 5). The offering leverages the  latest advances from data science in test data management, giving enterprises the right tools to engineer appropriate test data.<|endoftext|>Figure 5. Focus areas of Infosys Next-Gen TDM Model Development Training Testing Discover & Plan Explore Enrich Manage 2. AI Driven  Data  Discovery 1. Tester UX 9. Intelligent Automation 3. Data  Virtualization 4. Data  Provisioning 5. Privacy  Preserving  Synthetic  Data 6. Smart  Augmentation  of Training  Data Sets 7. Image,  Audio 8.  Special  
Formats (EDI,  SWIFT) External Document © 2021 Infosys Limited 

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 Accelerate through Next-Gen TDM Reference Architecture As organizations look to deliver high-quality applications at minimum cost, they need a test data management (TDM) strategy that supports  both waterfall and agile delivery models. With the rapid adoption of DevOps and increased focus on automation, there is also increasing  demand for data privacy. Enterprises are fast moving from traditional TDM to modern TDM in order to meet the needs of the current  development and testing landscape.  Infosys Next-Gen TDM focuses on increasing automation and improving the security of test data across cloud as well as on-premises data  sources.  Figure 6. Infosys Next-Gen TDM reference architecture Production Non-Production on Premise Data Source Logs Files Non-Production on Cloud Tester Developer ReleaseManager Data Scientist Self Service Portal Next Gen TDM Cloud Apps Commercial Testing Tools CI/CD Pipeline Data Masking Data Discovery Data Generation Gold Copy Data Mining Data Sub-setting Data Virtualization Data Quality Diferential Privacy Automated workfow Data Reservation Data Provisioning Data Generation Data base Refresh Unit and Functional Testing Integration, Regression and Performance Testing External Document © 2021 Infosys Limited 

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 1. User experience – Infosys Next-Gen  TDM focuses on building specific data  experiences for each persona, i.e., tester,  release manager, developer, and data  scientist. Its self-service capabilities  offer simplified intent-driven design  for better data provisioning and  generation.<|endoftext|>2. Contextual test data generation –  There is a library of algorithms that  helps teams generate different data  types and formats including images,  EDI files, and other unstructured data.<|endoftext|>Figure 7. Contextual test data and