Rise of Fake Data: Achieve 100% Use Case Coverage with Advanced Data Synthesis

Quality testing is not possible without quality data. The majority of DevOps teams still burden themselves with building test datasets in-house creating a negative impact on productivity. The fragility and limitations of those custom scripts also impede QA teams in maximizing their results. With the ever-increasing complexity of today’s data ecosystems, generating useful, safe test data has become harder and riskier than ever. An effective test data solution must work across a variety of database types, while de-identifying production data in a way that ensures data privacy without diminishing data utility. A challenge? Yes. Attainable? That, too.
Test data solutions now exist that integrate directly into your data ecosystem to create test data that looks, acts, and behaves like your production data because it's made from your production data. By hydrating QA and staging with useful, safe, mimicked data, dev teams are upleveling testing, catching bugs faster, and shortening their development cycles by as much as 60%. Data mimicking sets a new standard of quality test data generation that combines the best aspects of anonymization, synthesis, and subsetting.
Discover why in-house workarounds fail to provide QA teams with the data they need and demo a first of its kind platform that:

• Increases your team’s efficiency by 50%
• Increases releases 5x per day
• Maintains consistency in your test data across tables and across databases
• Subsets your data from PB down to GB without breaking referential integrity
• Achieves mathematical guarantees of data privacy

IMPORTANT NOTE: This session won't have a live Q&A