We’re entering a new era of data-driven business-flow—one where the need for responsible, scalable, and safe data access has never been greater. If your teams are building machine learning models, conducting analytics, or sharing data across teams or partners, you’ve likely already felt the friction: real data is risky, slow to access, and often locked away. 

We built dbTwin to change that. 

In this post, we lay out our core beliefs and why synthetic data is the key to unlocking high-performance, privacy-safe data workflows. You’ll see why traditional approaches are breaking down and how dbTwin is different—faster than anything else out there, simple to adopt, and built for real enterprise-scale workloads. 

As Stafford Beer coined, “The purpose of a system is what it does” (POSIWID). If your de-identification process results in data that’s hard to use, slow to generate, statistically compromised, or still potentially re-identifiable through complex inference attacks, then the purpose of your system isn’t truly robust data enablement and  privacy. It’s likely optimized for a baseline level of compliance checkbox-ticking, accepting significant friction and utility loss as trade-offs. 

The last twenty years have seen “de-identification” (pseudoanonymization) as the go-to method for data-sharing and collaborative workflows. However, de-identification based techniques for dealing with enterprise data are fundamentally flawed in the age of AI. Why isn’t de-identification enough? 

• Utility Destruction: Aggressive masking or tokenization can destroy the subtle statistical patterns, correlations, and distributions that analytics and AI models rely on. Training an AI model on heavily masked data is like training a pilot in a simulator that doesn’t accurately reflect real-world physics. 

• Re-identification Risk: Even without direct identifiers, quasi-identifiers like zip code, age, gender carry re-identification risk, especially when linked with external datasets. Achieving true anonymity that withstands sophisticated attacks is incredibly difficult and often requires sacrificing even more data utility. 

• Operational Friction: Generating and managing token maps, ensuring consistent application across datasets, and handling updates creates significant operational overhead  for data teams that move quickly. 

• Scalability Issues: As datasets grow in size and complexity (more columns, intricate relationships), managing rule-based anonymization effectively becomes exponentially harder. 

This reactive, often inadequate approach was perhaps tolerable when data was primarily used for historical reporting. But today, the stakes are infinitely higher. 

The Rise of AI and the Data Imperative 

We’re in the age of AI. Machine learning models demand vast amounts of high-quality, representative data for training. Federated learning, complex analytics, and real-time decisioning systems all rely on data that accurately reflects reality. Simultaneously, privacy regulations have tightened, and the financial and reputational costs of data breaches are severe. 

This creates an almost impossible bind for data teams in regulated sectors: How do you fuel data-hungry innovation while rigorously protecting privacy? How do you provide realistic data for testing, development, and analytics without exposing sensitive information? 

Trying to force legacy de-identification methods to meet these modern demands is like trying to power a supercomputer with a hamster wheel. It simply doesn’t scale, isn’t safe enough, and cripples the very innovation it’s meant to support.  

Synthetic data, unlike masked or tokenized real data, is artificially generated data that statistically mirrors the characteristics, patterns, and correlations of the original dataset without containing any real, sensitive information. It breaks the direct link to real individuals, offering a path to high utility and  high privacy.

dbTwin: Synthesizing Reality, At Speed 

This brings us to dbTwin. We observed the challenges—the privacy risks, the utility loss of anonymization, the computational expense and complexity of existing deep-learning-based synthetic data generators—and dedicated over five years of research to building a fundamentally different approach. 

dbTwin is a state-of-the-art synthetic data platform designed for the enterprise. Our mission is to provide rapid, realistic, and safe synthetic data at scale for the regulated industries.  

What makes dbTwin revolutionary isn’t just that it creates synthetic data, but how it does it. We utilize proprietary, non-deep-learning technology. This isn’t just a technical footnote; it’s the core of our advantage: 

• Blazing Speed & Efficiency: Because we don’t rely on computationally intensive deep learning models, dbTwin generates high-fidelity synthetic data 20x faster and 20x more efficiently than market leaders like Gretel and DataCebo. This translates directly into saved time, reduced cloud costs, and faster project timelines. 

• Instant Deployment: Forget days of model training or requiring expensive GPU clusters. dbTwin needs no training process and runs efficiently on standard CPUs. You can start generating synthetic data in minutes via our API, a simple web app (using CSV files), or deploy it securely within your own Virtual Private Cloud (VPC). 

• Proven Fidelity & Privacy: Speed means nothing without quality. dbTwin meticulously preserves the intricate statistical details of your real data: 

• It accurately replicates the distribution of values in each column (both categorical and numerical). 

• It maintains the crucial pairwise correlations and associations between different columns—essential for realistic modeling and analytics. 

• On hold-out tests (train on synthetic, test on real), dbTwin achieves 95-96% accuracy across various datasets, demonstrating its value in ML and analytics. 

• Exclusive & Future-Ready: Our technology is backed by years of focused R&D and protected by exclusive IP licensing. It’s also designed to be extensible, positioning us to tackle emerging needs in synthetic time-series data  

Synthetic Data as Code: Enabling the Shift Left

Just as “Data as Code” brings software engineering discipline to data pipelines, dbTwin enables “Synthetic Data as Code.” By providing programmatic access (API) and rapid generation, dbTwin allows synthetic data creation to become an integrated, automated part of the data workflow, shifting capability left. Instead of being a downstream bottleneck handled by a specialized team, generating safe, usable data becomes an accessible tool for developers, data scientists, and analysts at the point of need. It empowers the producers and consumers directly. 

The Path Forward 

The era of compromising utility for privacy, or risking privacy for utility, is ending. Traditional de-identification methods, born from a different time, are insufficient for the demands of modern AI and stringent regulations. Synthetic data is the necessary evolution. 

We believe dbTwin represents the next leap in this evolution. By moving beyond the constraints of deep learning, we offer unparalleled speed and efficiency without sacrificing data quality or privacy. It’s about enabling data teams to move faster, build smarter, and operate safer. It’s about finally unlocking the full potential of enterprise data, responsibly and at scale. 

The federated, AI-driven world demands better data access. The future is synthetic. And with dbTwin, that future arrives 20x faster.