Solving for Modern Test Data Management Challenges

Finding the Right Approach for Your Use Cases 

Enterprises have preferences for data protection approaches based on use cases. For example, many use static data masking (45%) for software development and testing. Not every approach will fit the needs of the use case, meaning organizations should be critical of where they implement each. 

For professionals considering these approaches, the key is choosing the right approach to data to answer the right question. Let's compare some of the methods at an individual level:

• Static masked data is excellent for data fidelity, where the data is reasonably like production. Static masking can also reach scale with much less compute when properly combined with things like data virtualization. Since it can preserve the patterns of production data (including its outliers and anomalies), it can be much more attractive at scale. 
• Synthetic data is much better suited for new applications/features, edge cases, negative testing. It can be very compute intensive, and no matter how good an analyst you are, there are data patterns (especially outliers and anomalies) that synthetic will always miss.   
• Dynamic masking has a place in the lineup as well. It’s often the preferred choice when you need real-time access, but it has definite drawbacks. It can be expensive to maintain its security configuration, and the security configuration can differ among vendors resulting in either many configurations or having to federate data to protect it which affects response time. 
• Subsetting is often the choice for organizations that need to save storage — the vast majority cite this reason — or to save compute. It often shows up in analytics when there are just immense amounts of data to test against. However, building or configuring a set of rules to subset properly is often as expensive — and sometimes more expensive — than managing your data structure in the first place. When you attempt to scale it up, the subset rules start to become more complex as a whole than any of the individual pieces, and it also becomes subject to a large cost of change. 

Simplify Compliance with One Single Platform 

Ensuring compliance while also accelerating development speeds can be difficult. By unifying data delivery, masking, and governance in your portfolio approach, you can ensure complete control over your operations. Delphix offers one single platform for it all: data delivery, masking, synthetic generation, and governance. With a one-stop shop built for enterprise scale and complexity, teams can access trusted data on demand across hybrid and multicloud environments.