SAS's Strategic Data Play: Unlocking AI With Synthetic Clones

CARY, NC – November 24, 2025 – In a significant move that underscores the shifting dynamics of artificial intelligence development, data and AI leader SAS has launched its SAS Data Maker solution on the Microsoft Marketplace. This is more than a simple product release; it represents a strategic investment aimed squarely at resolving one of the most pressing challenges in modern technology: the need for vast amounts of data for AI training while upholding stringent privacy and regulatory standards.

The Data Dilemma Driving a New Market

The insatiable appetite of AI models for high-quality data has created a critical bottleneck for innovation, particularly in highly regulated sectors like finance, healthcare, and government. The risk of exposing sensitive customer information, coupled with complex privacy laws like GDPR and CCPA, has often left organizations data-rich but insight-poor. This paradox has fueled the explosive growth of the synthetic data market, which is projected to expand at a compound annual growth rate (CAGR) of over 34%, potentially reaching a valuation of over $2 billion by the end of the decade.

Synthetic data—artificially generated information that statistically mirrors real-world data without containing any actual, sensitive details—is emerging as the key to unlocking this impasse. It allows companies to train, test, and validate AI models with data that is both realistic and completely anonymized. "Synthetic data that is accurately generated and rigorously validated is an indispensable resource for robust and trustworthy AI models," noted Kathy Lange, Research Director, AI Software at IDC, highlighting the market's core need. The challenge, she adds, lies in overcoming privacy concerns, legal restrictions, and high data acquisition costs—precisely the pain points SAS aims to address.

SAS’s Strategic Answer: Enterprise-Grade Synthetic Data

SAS Data Maker is the company's answer to this challenge. Billed as an enterprise-grade synthetic data generator, the platform is designed to create statistically representative data that can be used in place of real, regulation-protected information. This strategic entry is fortified by the company's acquisition of synthetic data pioneer Hazy in November 2024. That move, a quiet but critical capital investment, integrated Hazy’s advanced generative AI technology, effectively accelerating SAS Data Maker's product maturity by an estimated two years and embedding deep capabilities for handling complex structured and time-series data.

The resulting product boasts several key differentiators in a competitive landscape that includes specialized players like Mostly AI and Gretel.ai. First is SAS's decades-long credibility in regulated industries, providing a level of trust that startups often struggle to match. Second is its user-friendly, no-code graphical interface, a strategic choice designed to democratize access to synthetic data beyond data scientists to include business analysts and other non-technical users.

Furthermore, the platform includes built-in data quality and evaluation tools, allowing users to visually confirm the statistical fidelity of the synthetic data against the original—a crucial validation step that some competitors lack or offer only through programmatic APIs. By combining these features with robust privacy-enhancing technologies (PETs), SAS allows organizations to seamlessly swap synthetic data into existing workflows, minimizing disruption and accelerating AI development cycles.

From Theory to Practice: Proving the ROI

The true measure of any strategic investment lies in its real-world impact, and SAS has been quick to highlight tangible results from its private preview program. These early use cases provide compelling evidence of the return on investment that synthetic data can deliver.

For a UK financial services firm, the inability to access sufficient data for training credit scoring models was a significant business risk. By using SAS Data Maker to generate a synthetic dataset, the firm was able to close this data gap, leading to a remarkable 28% improvement in model accuracy. This not only facilitated machine learning efforts but also pointed to a potential reduction in future credit losses.

In the US healthcare sector, a provider leveraged the tool to simulate patient behavior and outcomes. This enabled the testing of new treatment plans and the optimization of care pathways without ever putting actual patient privacy at risk—a clear demonstration of how synthetic data can accelerate life-saving research while adhering to strict ethical and regulatory boundaries.

Meanwhile, a European telecom company struggling with customer churn found a powerful new lever. The traditional process of gaining access to customer data for model building took weeks, rendering predictive models quickly outdated. With SAS Data Maker, that data access time was reduced to mere minutes, enabling the development of a much more timely and effective customer churn model to improve retention efforts.

The Marketplace Maneuver: A Cloud-First Power Play

The decision to launch SAS Data Maker initially and exclusively on the Microsoft Marketplace is a deliberate strategic maneuver. It signals a deeper alignment with the cloud ecosystem and a modern go-to-market strategy designed to meet enterprise customers where they are. The marketplace is far more than a digital storefront; it is a powerful channel for customer acquisition and a catalyst for co-selling opportunities with Microsoft's global sales teams.

By listing on the marketplace, SAS simplifies the procurement and billing process for millions of existing Azure customers, reducing friction and shortening sales cycles. This move also positions SAS to compete more effectively against cloud-native competitors and demonstrates an understanding that the future of enterprise software is deeply intertwined with major cloud platforms. It is a capital move that leverages a partner's ecosystem to amplify reach and accelerate adoption, reflecting a broader trend of strategic alliances shaping the AI landscape. The platform's planned integration into SAS Viya, the company's end-to-end AI platform, further solidifies this strategy, creating a cohesive environment for data preparation, model development, and deployment.

Navigating the Ethical Frontier of Fabricated Data

As organizations increasingly turn to synthetic data, they enter a new ethical frontier. While the technology offers a powerful solution for privacy and can even be used to mitigate biases by creating more balanced datasets, it also demands a new level of governance. Experts in AI ethics caution that the lines between real and synthetic data are blurring, necessitating robust frameworks to ensure transparency and prevent misuse.

The integrity of synthetic data hinges entirely on the quality of its generation and validation. Poorly generated data could inadvertently introduce or amplify biases, leading to flawed or unfair AI models. Therefore, the responsibility falls on vendors like SAS and the organizations using these tools to implement rigorous governance, maintain clear data lineage, and conduct regular audits.

"Effective, impactful AI needs appropriate and sufficient data – and synthetic data is a game changer for organizations looking to innovate responsibly," stated Brett Wujek, Head of Next-Generation AI Product Strategy at SAS. This statement reflects the dual promise and responsibility of the technology. As synthetic data becomes a cornerstone of AI development, its successful and ethical deployment will depend not just on the sophistication of the algorithms, but on the strength of the human-led governance that guides them. The launch of SAS Data Maker is a bet that enterprises are ready to make that investment.