Top 7 Synthetic Data Generation Tools Helping Enterprises Modernize Data Operations

Enterprises modernizing their data operations face a common tension: they need more data to power analytics, AI, and automation – but they cannot afford increased compliance risk, storage sprawl, or uncontrolled data duplication.

Traditional approaches rely heavily on copying production data into development, analytics, and AI environments. That model is becoming unsustainable. It increases exposure of sensitive information, drives up infrastructure costs, and slows innovation when data access is restricted.

This is where synthetic data generation tools are playing a transformative role.

By generating realistic, production-like datasets that preserve structure and relationships – without exposing sensitive values – synthetic data allows enterprises to modernize data operations safely. It supports secure analytics, faster model development, DevOps automation, and scalable experimentation.

Below are seven synthetic data generation tools helping enterprises modernize data operations in 2026 – and what differentiates them in real enterprise environments where governance, lifecycle control, and operational integration matter as much as generation quality.

What Enterprise Buyers Should Look For

Before comparing tools, it helps to anchor evaluation criteria to modern operating realities. In enterprise environments, the most common breakpoints are rarely the quality of a single dataset. They’re operational:

• Multi-method generation options to match different risk levels and use cases
• Referential integrity across connected entities (customer → account → order → ticket) and across systems
• Built-in controls for compliance, access, and auditability
• Lifecycle management (reservation, versioning, aging, rollback, refresh) to prevent synthetic sprawl
• CI/CD and MLOps integration so data provisioning is automated, not ticket-driven
• Scalability across hybrid and multi-cloud architectures

With that lens, here are seven tools that come up frequently in enterprise modernization discussions.

1. K2view

K2view provides enterprise-grade synthetic data generation tools designed to modernize data operations across complex, multi-system environments. Rather than focusing solely on model-based generation, K2view manages the full synthetic data lifecycle – from data preparation and masking to generation, operational controls, and automated delivery.

A defining capability is its multi-method generation approach, which includes:

• AI-powered generation for realistic, production-like datasets
• Rules-based generation for edge cases, negative testing, and new functionality
• Data cloning for high-volume performance and load testing
• Intelligent masking for privacy-compliant lower environments

K2view also preserves referential integrity across business entities such as customers, accounts, orders, and products. This means that when synthetic data is generated, relationships across systems remain intact – a critical requirement for enterprise analytics and AI models that depend on multi-entity consistency.

Beyond generation, K2view includes lifecycle controls such as reservation, versioning, aging, and rollback. Synthetic datasets can be integrated directly into CI/CD and MLOps pipelines, enabling teams to operationalize data provisioning rather than treating it as an ad hoc task.

For enterprises modernizing data operations, K2view delivers not just synthetic data, but a governed, lifecycle-managed foundation that aligns data delivery with DevOps velocity and compliance requirements.

2. Mostly AI

Mostly AI generates privacy-safe synthetic datasets that statistically mirror production data. It supports tabular and multi-relational structures and provides fidelity metrics to help teams evaluate how closely synthetic output reflects source datasets.

For enterprises modernizing analytics workflows, Mostly AI can expand training coverage without exposing sensitive transactional information. Its interface is accessible to data science teams looking to accelerate dataset creation for experimentation.

In broader, heterogeneous enterprise environments, organizations may still need additional lifecycle and governance tooling to operationalize synthetic data consistently across teams, platforms, and downstream copies.

3. YData Fabric

YData Fabric combines data profiling with synthetic data generation, supporting relational, tabular, and time-series data. It emphasizes data quality improvements prior to model training and supports machine learning workflows.

For enterprises modernizing AI and predictive analytics, YData can generate balanced datasets and simulate alternative scenarios to improve robustness. Its data quality assessment can also help teams identify gaps in source data before generating synthetic alternatives.

It typically requires data science expertise to configure effectively, and many enterprises will pair it with broader policy and lifecycle processes to ensure governed use at scale.

4. Gretel Workflows

Gretel offers a developer-focused synthetic data platform built around pipeline automation. Supporting structured and unstructured data, it emphasizes scheduling and integration into engineering workflows.

In modern data operations, Gretel can be embedded into CI/CD or data engineering pipelines to generate and refresh synthetic datasets automatically, supporting continuous experimentation and rapid iteration.

Because it is developer-oriented and often cloud-centric, organizations with extensive cross-system integrity requirements or centralized governance needs may rely on complementary controls to manage policy enforcement and lifecycle oversight across the enterprise.

5. Hazy (SAS Data Maker)

Hazy, now part of SAS Data Maker, focuses on privacy-preserving synthetic data generation using differential privacy techniques and privacy-led generation methods. It is particularly relevant for regulated industries such as financial services and healthcare.

For enterprises modernizing data operations under strict compliance constraints, this approach can support sharing and analysis of realistic datasets while reducing exposure risk.

Implementation can be complex, and the platform is often best aligned to organizations where regulatory alignment and privacy guarantees are the primary driver for synthetic adoption.

6. SDV (Synthetic Data Vault)

SDV is an open-source Python library offering multiple generative models, including CTGAN and CopulaGAN, for tabular, relational, and time-series synthetic data.

For technical teams experimenting with generative approaches, SDV provides flexibility and customization. It can be useful for research initiatives, prototypes, or smaller-scale experimentation.

However, SDV lacks enterprise lifecycle management, automated governance, and integrated compliance features. As organizations modernize data operations at scale, open-source libraries typically need to be complemented with additional controls for policy enforcement, auditing, and operational repeatability.

7. Syntho

Syntho provides a self-service synthetic data engine focused on statistical realism and privacy compliance. It aims to preserve statistical properties while removing direct identifiers, supporting analytics and AI training.

For enterprises expanding AI experimentation, Syntho can help simulate both common and rare scenarios in training datasets, increasing model coverage beyond historical production logs.

As with other generation-focused platforms, organizations modernizing end-to-end data operations often require additional lifecycle management and governance capabilities to ensure synthetic datasets remain controlled, traceable, and consistent across environments.

Conclusion

Modernizing data operations requires more than migrating to the cloud or adopting new analytics platforms. It requires rethinking how data is provisioned, protected, and operationalized across environments.

Synthetic data generation tools are becoming foundational to this shift. They allow enterprises to:

• Reduce dependence on full production clones
• Lower compliance exposure
• Accelerate AI experimentation
• Support DevOps and MLOps automation
• Improve analytics coverage without increasing risk

Some tools focus primarily on statistical fidelity. Others emphasize developer workflows or differential privacy. Open-source options provide flexibility but limited governance.

For enterprises managing complex, multi-system environments, the key differentiators are often multi-method capability, referential integrity, lifecycle control, and seamless integration into operational pipelines.

Among the tools reviewed, K2view stands out for combining multi-method synthetic data generation with built-in masking, cross-system integrity, and lifecycle management. By unifying preparation, generation, operation, and delivery within a governed platform, it enables enterprises to modernize data operations while maintaining security, compliance, and control.

As synthetic data becomes central to enterprise AI and analytics strategies, choosing the right platform will play a defining role in how effectively organizations transform their data operations for the future.