Validating Big data workflows
Tenendo helps to build a cost-effective and scalable Big Data validation strategy and implement it in your project.
Top reasons that indicate you need Test Data Management
- A lot of false defects arise because of data issues.
- Testing deadlines are not met and the application goes live without proper testing because of issues with test data generation and test data availability.
- There is very little data available for testing compared to voluminous production data, hindering test efficiency, and quality.
- The test data set is of the same size as production data, hindering test efficiency, and speed.
- The team wastes significant time preparing test data rather than testing the application.
- The testing team depends on another team or a centralized team to provide the required test data.
- The testing team complains that test data generation is a very laborious and complicated process.
- Testing teams are not able to reuse the test data set.
- Teams are not able to regenerate the same test data set with sufficient precision and accuracy.
- There is a huge dependency on the upstream systems to create test data.
- Increases in data set size, upstream systems, database instance, and data sets makes it difficult to manage the test data.
Test Data Management (TDM) benefits
- Optimal data coverage is achieved through intelligent tools and techniques based on data analysis strategies.
- Version controlled data requirements and test data ensure complete traceability and easier replication of results.
- Detailed analysis and review of data requirements ensure early identification of issues and resolution of queries.
- Synthetic data can be created from the ground up for new applications.
- Errors and data corruption can be reduced by including defined TDM processes in the testing life cycle and by adopting TDM tools.
- Clear data security policies increase data safety and recoverability.
- Well-defined processes and controls for data storage, archival, and retrieval support future testing requirements.
How does Test Data Management deliver?
01.
Planning
- TDM team organization
- Define data requirements and templates for data management
- Establish a service level agreement
- Appropriate plans and papers signed off
02.
Analysis
- Collection/consolidation of data requirements
- Asses for gaps and impact of data modification
- Define data security, back up, storage, and access policy
- Decide strategy for data preparation
- Decide strategy for data profiling
- Define data security, back up, storage, and access policy
03.
Tools
- Choose the right tools for TDM needs.
- Select automation tools for data preparation and tests running
04.
Pilot
Offer a pilot or proof of value to show the selected tools can deliver the TDM required within the client organization, and that the proposed process can deliver the expected benefits.
05.
Deploy
- Provide a full TDM rollout
- Run automated tests
06.
Support
- Supports and trains customers all the way through the process and even after the rollout.
Related services:
Secure Software Development Life Cycle (Secure SDLC)
Build and maintain a mature process of secure development and confirm its compliance with the industry standards (HIPAA, PCI DSS, ISO/IEC 27001, SOC2, NIST, CCPA, and GDPR).
Automated testing challenges
The most important factor that drives test automation is the short development cycle. Agile teams have only a few weeks to get a grasp of the requirement, make the code changes, and test the changes. If all testing were to be done manually, the time required would surpass the actual development time. Alternatively, testing would have to be hurried, thus compromising on quality.
