The Role of Synthetic Data in Cloud Modernization | Onix

Cloud modernization is more than just a technological upgrade. For enterprises striving to be agile, scalable, and innovative, cloud migration is just the first step. The real transformation happens when businesses embrace the cloud’s full potential, especially when it comes to data. Legacy data systems often stand in the way of quick, efficient cloud adoption. This is where synthetic data enters the picture as a game-changer.

By leveraging a synthetic data platform like Kingfisher, organizations can navigate the complexities of modern cloud systems without compromising security or privacy. This article explores how synthetic data helps businesses overcome barriers in their cloud modernization journey.

Data Challenges That Stall Cloud Modernization

While cloud technologies offer flexibility, scalability, and cost savings, the migration process can quickly hit a roadblock when it comes to data. Legacy data systems are often siloed, outdated, and difficult to integrate with cloud environments. Organizations face significant barriers such as:

• Security and privacy concerns: Production data is sensitive and often tied up in compliance requirements, making it difficult to use for cloud testing and validation.

• Slow data access: Getting approval for data usage or finding a secure way to share production data across teams or vendors takes time and often adds friction.

• Poor data quality: Legacy systems often store fragmented, incomplete, or dirty data that hampers the modernization process.

In a nutshell, data becomes a major bottleneck that delays cloud adoption and inhibits innovation. But what if there was a way to create data for testing, validation, and training purposes that mimicked real data—without exposing sensitive information?

This is where synthetic data platforms come into play.

What Is Synthetic Data?

Synthetic data is artificial data that’s generated to reflect the characteristics, patterns, and relationships of real-world data. Unlike anonymization or data masking, synthetic data doesn’t attempt to alter or obscure real data—it generates entirely new data that behaves like the original.

The benefits of synthetic data are numerous:

• Privacy protection: No real personal or business-sensitive data is used.

• Scalability: Organizations can generate vast amounts of data on demand, without needing to rely on limited datasets.

• Data diversity: Synthetic data can represent edge cases, rare events, and scenarios that may not be well-represented in real-world data.

Modern synthetic data generation tools use machine learning algorithms and statistical models to create data that’s both statistically accurate and privacy-compliant. This opens the door for secure testing, analytics, and AI-driven applications in the cloud.

How Synthetic Data Generation Tools Accelerate Cloud Modernization

1. Faster Testing and Validation in Cloud Environments

Testing is a critical part of cloud modernization, and synthetic data can speed up this process. Traditional testing requires real data, which often involves complex approval processes and compliance hurdles. With synthetic data, enterprises can generate realistic test datasets quickly and safely. This allows for:

• Continuous integration and testing: Run tests without waiting for real data to be approved or shared.

• Faster cloud migration: Validate data pipelines, applications, and integrations in the cloud environment without delays.

By providing an easily accessible and safe data alternative, synthetic data enables teams to perform rigorous testing and validation without compromising data privacy or security.

2. Supporting Scalable, Cloud-Native Architectures

One of the key advantages of cloud-native architectures is their ability to scale easily. But in order to validate scalability, you need massive amounts of data. Generating synthetic data at scale allows organizations to simulate:

• High-traffic scenarios: Simulate millions of concurrent users, transactions, or events.

• Data volume testing: Create large datasets that test how cloud applications and databases will perform under heavy loads.

• Edge cases and rare events: Generate data that represents uncommon but critical scenarios (e.g., security breaches, unexpected system failures).

This scalability ensures that cloud-native systems are ready for peak performance before going live.

Synthetic Data's Role in AI and Analytics Modernization

AI and analytics are core drivers of cloud modernization. However, the success of these technologies depends on the quality of data they’re trained on. Synthetic data generation tools offer a way to overcome the limitations of real-world data, such as bias, incompleteness, and privacy concerns.

1. Enabling Safe AI Model Training

AI models require large amounts of high-quality data to learn and improve. But using real-world data can lead to privacy violations and other risks. Synthetic data eliminates this concern while ensuring models are trained on realistic, high-quality data.

For example, Kingfisher enables enterprises to create synthetic datasets that can be used to:

• Train AI models without exposing sensitive information.

• Test AI algorithms across diverse data sets, including rare or extreme scenarios.

• Iterate faster on AI models, leading to quicker deployment and more accurate predictions.

2. Enhancing Analytics with Synthetic Data

In cloud-based analytics, having consistent and clean data is paramount. Real data can be fragmented, biased, or incomplete. Synthetic data generation tools provide an easy way to create fully normalized datasets for cloud-based analytics applications. This improves:

• Data quality: Ensures the data used for analytics is accurate, balanced, and reflective of real-world scenarios.

• Analytics readiness: Data prepared for advanced analytics and reporting is available faster, speeding up decision-making.

The Benefits of Synthetic Data Over Traditional Masking Methods

Data masking has long been a go-to solution for protecting sensitive information. However, masking techniques often fall short in terms of data usability, scalability, and security. Synthetic data, by contrast, doesn’t mask or alter real data—it generates entirely new data, preserving privacy and relationships while offering superior flexibility.

Here’s why synthetic data generation tools are superior:

• No re-identification risk: Unlike masked data, synthetic data cannot be traced back to real individuals.

• Preserved data relationships: Unlike data scrambling, synthetic data maintains accurate relationships between variables, enabling better insights.

• Scalability: With synthetic data, you can generate vast datasets at scale, simulating real-world traffic, transaction loads, and edge cases.

How to Choose the Right Synthetic Data Platform for Your Cloud Journey

Not all synthetic data platforms are created equal. When selecting the right solution for your cloud modernization needs, look for a synthetic data platform that offers:

• Scalability: Can it generate data on demand, at the scale required by your cloud environments?

• Data fidelity: Does it preserve the statistical accuracy and relationships of real data?

• Security and compliance: Is it built to comply with global data privacy regulations?

• Integration: Does it seamlessly integrate with your cloud-native applications, databases, and AI pipelines?

Kingfisher, from Onix, is designed to meet these criteria. As part of Onix’s Birds suite, it offers an enterprise-grade synthetic data platform that powers secure, large-scale cloud modernization initiatives.

Cloud Modernization: Fast-Track Your Journey with Synthetic Data

As cloud environments become more sophisticated and automated, synthetic data will move from a helpful tool to a critical enabler. Organizations that leverage synthetic data generation tools early in their cloud modernization journey will not only accelerate their transition to the cloud but will also build stronger, more resilient, and more innovative systems.

By adopting synthetic data platforms like Kingfisher, enterprises can innovate with confidence, free from the constraints of legacy data systems. With secure, scalable synthetic data, you can test, validate, and train across your cloud environment, ensuring a smoother, faster, and more successful modernization process.

Ready to modernize your cloud environment securely and efficiently?

Contact Onix today to see how Kingfisher-our enterprise-grade synthetic data platform-can help you accelerate cloud modernization, reduce risk, and scale faster.