What We Do
Test Data Management
Ticking Minds has deep expertise of delivering 5+ data management engagements for large digital transformation initiatives.
Analyze
Data - Profiling & Identification
Design
Data - Generation, Population
Measure
Data - Validity, Relevance, Consistency
Execute
Data - Provisioning, Reporting & Analysis, Maintenance
Our Process
Test Data Framework
Key factors for automated data provisioning in successful digital transformation initiatives are
Right strategy for data provisioning with key personnel with the required skills.
Automated data provisioning solution that would cater to quick provisioning of data across environments.
Absolute compliance to mandatory data privacy laws.
Test Data Management Expertise
Industry Challenge
The accelerated digital transformation initiatives demand best practices that support rigorous and continuous testing to ensure successful outcomes from digital transformation initiatives. Provisioning the good quality in the right data format at the right time has a huge say in the digital transformation journey. Unless high quality data can be provisioned in the right format and size, progress in such initiatives is going to be slow. In many cases, data quality determines the transformation initiative outcome.
Skils
Test data management requires skills like understanding business, user requirements, strategic view to data provisioning mechanisms – synthetic, production extraction, subsetting, masking, etc, understanding the technical architecture, components, data flows, underlying database schemas – all play out to be crucial role in the test data management.
Process
Often test teams lack access to data for their testing needs. DB teams often provision the data in non-automated way, which forces the testing teams to push the testing to later cycles/sprints. This results in unearthing defects late in the life cycle, thereby increasing the risk of such digital transformation initiatives. The lack of emphasis on quality of data often derails such exercises.
Compliance
Not all engagements can afford synthetic data generation mechanism. Quick turnaround time requires to adopt production data extraction, subsetting and masking approaches. Often the lack of understanding the relevance to compliance can lead to sensitive being used in testing exercises, potentially leading to huge penalties associated with non-compliance.
Case Studies
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