Monday, 16 July 2012

UAT (User Acceptence Testing)

In this phase you will involve the user to test the end results and ensure that business is satisfied with the quality of the data.
Any changes to the business requirement will follow the change management process and eventually those changes have to follow the SDLC process.
Optimize Development, Testing, and Training Systems
  • Dramatically accelerate development and test cycles and reduce storage costs by creating fully functional, smaller targeted data subsets for development, testing, and training systems, while maintaining full data integrity.
  • Quickly build and update nonproduction systems with a small subset of production data and replicate current subsets of nonproduction copies faster.
  • Simplify test data management and shrink the footprint of nonproduction systems to significantly reduce IT infrastructure and maintenance costs.
  • Reduce application and upgrade deployment risks by properly testing configuration updates with up-to-date, realistic data before introducing them into production .
  • Easily customize provisioning rules to meet each organization’s changing business requirements.
  • Lower training costs by standardizing on one approach and one infrastructure.
  • Train employees effectively using reliable, production-like data in training systems.
Support Corporate Divestitures and Reorganizations
  • Untangle complex operational systems and separate data along business lines to quickly build the divested organization’s system.
  • Accelerate the provisioning of new systems by using only data that’s relevant to the divested organization.
  • Decrease the cost and time of data divestiture with no reimplementation costs .
Reduce the Total Cost of Storage Ownership
  • Dramatically increase an IT team’s productivity by reusing a comprehensive list of data objects for data selection and updating processes across multiple projects, instead of coding by hand—which is expensive, resource intensive, and time consuming .
  • Accelerate application delivery by decreasing R&D cycle time and streamlining test data management.
  • Improve the reliability of application delivery by ensuring IT teams have ready access to updated quality production data.
  • Lower administration costs by centrally managing data growth solutions across all packaged and custom applications.
  • Substantially accelerate time to value for subsets of packaged applications.
  • Decrease maintenance costs by eliminating custom code and scripting.

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