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Analytics Quality Assurance

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Appian Analytics provides a thorough set of specialized quality assurance and testing approaches that have been specifically developed for analytics quality assurance. As a specialized analytics consulting and analytics software development company we have deep experience in quality assurance methods. Our software development and services involve development and execution of complex quantitative analyses, data processing and computer modeling. These services have helped us to develop work-class capabilities in analytics quality assurance and data validation.

Appian also has experience using and interpreting quality assurance surveillance plans (QASP). Development of a comprehensive QASP is done in conjunction with the quality assurance plan and includes a timeline related to performing surveillance activities, the methods which will be used to monitor performance, the measures which will be used to evaluate performance, and what standards will be used to evaluate performance. Metrics can include periodic inspections or reviews of the quality assurance process and performance measured in terms of bugs caught, accuracy of keeping the bug tracking system up to date, accuracy at documenting defects, etc.

While each project is unique, there are tried-and-true quality assurance methodologies and testing approaches that work very well for analytics quality assurance. These techniques are industry recognized and critical to effectiveness of the quality assurance process. Appian Analytics has a great deal of experience in the following techniques:

  • Quality Assurance Planning - Developing the approach to quality assurance testing in an analytics projects is the essential first step. The best approach is to build QA into the overall consulting or software development project.
  • Requirements Testing - Testing source documentation related to design and development of systems is the first key step in the quality assurance process. This includes developing functional specifications, usability testing, and user acceptance testing.
  • Use Case Development - Developing "use cases" is a well understood aspect of quality assurance testing in software development. For analytics projects, this includes developing a wide variety of analytic interactions and testing the output for accuracy.
  • Core Testing Methods - System quality assurance and testing methodologies form the core of system testing as performed in quality assurance. Some methodologies we use are Black Box, White Box, Unit Testing, Smoke and build, Integration tests, Regression testing, and Automated Testing.
  • Field Testing - Traditionally, Alpha and Beta testing has been performed by end-users but more and more this is being conducting by professional firms. The reason is that consumer and commercial users are providing less feedback. Also, the feedback received can be less valuable than the thorough test results provided by professional firms.
  • System Performance Testing and Analysis - Testing the system and measuring performance under varying levels of resource availability and load levels. This testing includes a quantitative analysis matrix of the performance under different load levels and resource sets.

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