We produce around 2.5 quintillion bytes of data in various forms: social posts, web searches, transactions, etc. (Forbes, 2018). To ensure this data is handled with care and quality, you must be able to test it. With GDPR (General Data Protection Regulation) becoming stringent, not using compliant data during testing could cost hefty fines and hamper smooth business continuity. Synthetic data saves you from these.
In a digitally driven economy, delivering high-quality applications at a competitive pace is mandatory. To deliver better software faster, you must be able to seamlessly test the applications and the data.
Lack of quality data is one of the primary contributors to defect slippages. In the book Applied Software Measurement, research suggests that the cost of fixing a bug exponentially increases with each stage of the software development lifecycle. In simpler terms, bug-fixing costs 160x more in production than in the unit testing phase.
High-quality, reliable, accurate, and compliant data can turn the tables around. With test data in place, your team is empowered to reduce production defects, deliver desired quality, enhance customer experience, maintain brand reputation, and generate better revenues.
Avo’s intelligent Test Data Management (iTDM) solution offers production-like, relevant, and compliant data with a few clicks. It streamlines the entire test data management process making testing cost-effective and faster.
- Fast forward time-to-market by delivering applications well within the timelines
- Build top quality software leveraging synthetic data that mimics your production environment
- Identify non-compliant data in non-production environments
- Adhere to data privacy regulations and provide only relevant data downstream
- Comply with on-demand and configurable data privacy regulations that are continually evolving
- Data discovery: Helps manage Personal Identifiable Information (PII) with data discovery and automation
- Data Obfuscation: Secures sensitive data for PII compliance
- Synthetic data generation: Dynamically generates synthetic data that mimics real-world data using AI/ML
- Data provisioning: Provisions, analyzes, and searches data
- Supports open architecture with easily pluggable custom modules
- Built and deployed on open-source technologies and container framework
- Multiple security options at the lowest levels of data
- Can handle billions of records on commodity hardware