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TEST DATA MANAGEMENT CAN BOOST TIME TO MARKET FOR INTELLIGENT MODELLING
The ability to deliver new functionality in applications with speed and confidence is key to building and sustaining competitive advantage . But without quality , development efforts can deliver a poor user experience which in turn will impact brand , revenues , customer loyalty , explains Greg Harrowsmith at CASA Software .
Test Data Management or TDM supports Artificial Intelligence , AI and Machine Learning , ML data models by ensuring data is not only high quality but preserves anonymity by removing personally identifiable information .
TDM involves the creation , maintenance , and use of test data sets that are representative of production data . Effective TDM ensures that test data is not only realistic but also secure and compliant with data privacy regulations . Techniques like data masking and synthetic data generation are integral to TDM , allowing teams to deidentify sensitive information while preserving its utility for testing and training AI , ML models .
Unnecessary testing and poor understanding of test coverage can lead to software that is over-tested in one area and under-tested in others .
TDM can support AI , ML by :
• Testing data at scale , while masking offers reliable solutions for obtaining large volumes of data required by data sciences teams at scale and low cost .
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