Continue # Infosys Whitepaper 
DM requests for application  or application clusters are fairly high •   Technologies are widely supported and  not disparate •   High number of inter-dependent  systems which require data set up  across systems for end-to-end testing When to go for decentralized  TDM? •   The nature of portfolios or departments  within the organization are highly de- centralized •   A specific TDM process is required for a  prioritized set of applications within a  short span of time •   Scope of TDM is limited within the  project and does not continue after the  project is complete •   Disparate or obsolete technologies  used in the project are not supported  by common TDM tools •   Limited number of dependent /  external applications •   Need for test data provisioning is  very low and the requests flow is  manageable Common TDM challenges  and resolutions 1.  Inconsistent data relationship    Well-defined data relationship between  database objects is a key factor for  data subsetting, masking, and data  provisioning. It is often observed  that in case of legacy applications,  relationships are not present in the  database layer. The business rules and  logical constraints may be applied at  the application level, but will be poorly  defined at the database level. Logical  database model architectures may not  be available in most cases.<|endoftext|>Impact •   Data subsetting, data masking, and  data provisioning get affected •   Data integrity will not be maintained  Resolution  •   Understand the application and  database structure, relevance of tables,  and how they are related with help of  SME / DBA •   Analyze and understand the database  structure using data model artifacts  •   Validate the logically-related entities  and confirm with business analyst 2. Unclear test data requirements    Teams requesting data sometimes lack  information aboutwhich data sources  would have the related data that  needs to be set up. In some scenarios,  test data requirements can be very  complex, like for testing an end-to- end scenario with data spread across  multiple databases or with data spread  across tables.<|endoftext|>Impact •   Inaccurate test data Resolution •   Understand the requirement from QA  perspective •   Understand the database entities  involved and the relationships 3. Lack of application knowledge    System or application knowledge,  especially the data sources under the  TDM scope, is a prerequisite for the  TDM team. If teams possess a limited  knowledge about the application,  External Document © 2018 Infosys Limited 

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 it will result in writing incorrect test cases,  raising ambiguous test data requirements,  and finally, provisioning inaccurate data.<|endoftext|>Impact •   Inaccurate test data  •   Increased defects due to incorrect test  data Resolution •   Understand the application with the  help of SMEs •   Understand the database entities  involved and the relationships 4. Corrupted gold copy    Most projects will have a gold copy  database available from where data  will be provisioned to the lower  environments. If the gold copy is not  refreshed periodically, or the data in the  gold copy has been tampered with, it  can cause issues while provisioning data.<|endoftext|>Impact •   Inaccurate test data Resolution •   Periodically refresh gold copy database •   Restrict access to gold copy database 5. Data overstepping    If the same set of test data is used by  multiple teams for testing, it can lead to  conflicts and the test results will not be  as expected.<|endoftext|>Impact •   Affects test execution •   Incorrect test results •   Rework in test data provisioning and  test execution Resolution •   Data has to be reserved  •   Centralized TDM team can handle the  test data requirements 6.  Identifying correct sensitive fields  and masking techniques    While masking any application  database, it is important that the  correct sensitive fields are identified  for masking. Also, what is important is  that relevant masking techniques are  applied to these fields. For example,  email id should be masked in such a  way that the email id format is retained.  Otherwise, while using the masked  email id, it might break the application.  Another point to consider is while  masking the primary key columns, the  masking has to be consistently applied  for the child tables also where the  primary key columns are referenced.<|endoftext|>Impact •   Data inconsistency across tables •   Unnecessary masked data Resolution •   Identify sensitive fields belonging to  the category PII, PHI, PCI, financial  data, etc.<|endoftext|>•   Apply relevant masking techniques that  will preserve the format of the data Best practices Some of the best practices that can be  adopted while implementing test data  management processes in projects are  listed below: •   Automate TDM processes with reusable  templates and checklists •   Improve test data coverage and  test data reuse by provisioning and  preserving the right data •   Analyze TDM metrics and take  corrective actions •   Data refresh to gold copy can be  automated using scripts  •   Batch mode of data masking can be  implemented to improve performance  without exposing sensitive data to  testing teams •   Test data can be used for configuring  the masking rules, which can be  replaced with production data for  actual execution. Thus, production data  is not exposed to the execution team •   Reusable configuration scripts for  masking similar data (for example –  similar data for a different region) •   Developing automation scripts to  automate any manual TDM-related  activities •   Developing data relationship  architecture diagrams for the most  commonly used tables for provisioning  which can be used as a reference External Document © 2018 Infosys Limited 

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 Summary Reduced cost and improved management  with faster time-to-market are the key  points for any successful program. Central- ized and automated test data management  provides an organized approach in man- aging test data requirements across the  organization in a better and more efficient  way. Only the required masked, subset- ted, and reusable data sets, are stored as  gold copies centrally, which are used for  provisioning by the testing teams. Most of  the TDM tools available in the market offer  web-based solutions, which act as a single  interface for both the testing and provi- sioning teams. Testing teams can place the  test data request and provisioning teams  can address the request from a single por- tal. All test data requests are tracked using  a single solution. A centralized, automated  TDM system with streamlined processes  introduce increased accuracy and predict- ability to the entire testing process. Imple- menting centralized test data management  is certainly beneficial over the de-central- ized approach.<|endoftext|>Glossary Acronym Definition TDM Test Data Management PII Personally Identifiable Infor- mation PHI Personal Health Information PCI Payment Card Information SME Subject Matter Expert PoC Proof of Concept Case study – centralized TDM  for a leading pharmacy client Overview The client has a complex IT landscape with  data spread across multiple portfolios  including marketing, sales, corporate,  pharmacy, and supply chain. Some of the  applications across the portfolios have a  federated relationship with related data.  The TDM service engagement requirement  was to establish a well-defined TDM  process and governance, which will  address all the test data-related requests  for the projects under different portfolios,  gradually expand the TDM services to  newer portfolios, and finally consolidate  under the same umbrella.<|endoftext|>Problem statement •   Identify the test data and data masking  requirements in different portfolios and  application databases •   Perform gap analysis for the existing  TDM processes •   Establish a defined test data  management process and governance •   Implement an automated TDM process  using the right tools •   Test data provisioning for functional,  automation, and performance testing  teams •   Metrics-based approach for the  evaluation of test data management  implementation Challenges •   Complex IT landscape with  heterogeneous data source types •   Lack of defined test data management  processes / strategy •   Manual TDM activities for data  subsetting and masking •   Lack of integrated data across systems •   Sensitive data being moved to a  non-production environment without  masking •   Huge cycle time for generating test data,  impacting test execution schedules Solution approach •   Established a centralized TDM team to  provision test data for functional and  non-functional testing  •   Deployed a web-based, self-service tool  for the testing teams to place the data  request and provisioning •   Masked data is provisioned to testing  teams ensuring compliance to PIPEDA  (Personal Information Protection and  Electronic Documents Act) •   Established  automated TDM processes  and capabilities across portfolios  •   End-to-end testing made easy  by synching up test data across  interdependent applications Benefits / value-adds •   20% reduction in test data provisioning  cycle time