Betterdata’s programmatic synthetic data engine is solving data scarcity, data bias, and data security providing high-quality and highly private real-like production data for enterprises to train, refine, and scale their AI/ML models.
But first. What exactly is synthetic data?
Synthetic data is artificial data that is generated through advanced machine learning models such as Generative Adversarial Networks (GANs), Large Language Models (LLMs), or Deep Generative Models (DGMs). These models are trained on real data first to learn its properties and distributions, after which they generate real-like synthetic data that mimics real data's statistical properties but does not connect back to real individuals. Enabling organizations to access data faster, share data safely, and innovate efficiently.
Read our complete synthetic data guide here.
1. Our Models are Designed for Synthetic Data At Scale:
At Betterdata, our state-of-the-art (SOTA) models can generate high-quality tabular and relational synthetic data at scale fit for enterprise AI/ML applications and other data-intensive use cases. We are currently developing models for sequential and text data generation.
Contact us to learn more!
2. Supported Data Types:
a. Tabular Data:
Data is organized into rows and columns, much like a spreadsheet. Each row typically represents a single record (or observation), and each column represents a specific attribute or feature for those records. Such as a CSV file with columns like Name, Age, Country, and Purchase_Amount for a list of customers.
b. Relational Data:
Data is stored in a structure designed to recognize relations among stored items of information. Typically associated with relational databases (like SQL). The data is organized into one or more tables (relations), and each table has a unique primary key that identifies each row. Tables can reference each other via foreign keys, establishing relationships (e.g., one-to-many, many-to-many).
c. Time Series Data (Launching Soon):
A sequence of data points indexed in chronological order. This data type explicitly depends on time, typically captured at consistent intervals (daily, monthly, per second, etc.). For instance, Stock prices are recorded at the close of each trading day, or sensor readings (e.g., temperature) are collected every minute.
d. Text Data (Launching Soon):
Data in plain or textual format, often unstructured or semi-structured (e.g., emails, web pages, written documents). Text data can contain sentences, paragraphs, or other text-based content needing specialized processing (like Natural Language Processing). For instance, a collection of customer reviews in free-text form.
3. The Betterdata Advantage:
a. Synthetic Data Generation & Management:
- Generative Model Management: Deliver a comprehensive infrastructure for managing, training, and deploying cutting-edge generative models, optimized for synthetic data generation. This enables streamlined version control, performance tracking, and rapid experimentation to produce consistently high-fidelity, privacy-preserving synthetic datasets.
- Advanced-Data Augmentation: Augment existing datasets with rich, high-fidelity synthetic records that expand dataset diversity and mitigate bias. This boosts downstream model performance, robustness, and adaptability, providing valuable data coverage for both development and production environments.
b. Security & Compliance:
- Privacy-Preserving Data Solutions: Integrate Differential Privacy (DP) and advanced generative anonymization strategies to produce compliant synthetic datasets. Protecting against re-identification attacks, ensuring adherence to regulatory mandates like GDPR and CCPA while maintaining data utility for analytical workflows.
- Versioning & Dataset Locking: Gain full oversight through advanced versioning and dataset locking mechanisms that preserve historical snapshots and ensure consistent data states. This granular control helps audit preparedness, fosters collaboration and reduces compliance risks when handling critical or regulated information.
- Role-Based Access Control (RBAC): Employ finely-tuned RBAC to define precise permissions for every team member or group, preventing unauthorized access and ensuring accountability. Customizable policies align data usage with organizational structures, enabling flexible yet secure collaboration across multiple projects and departments.
- Secured Deployment Package: Provide a rigorously tested, CIS-hardened deployment package that meets stringent security benchmarks. By enforcing best-practice configurations and protective measures, the installation process remains secure, minimizing risk vectors and supporting ongoing compliance throughout the software’s operational lifecycle.
c. Deployment & Integration:
- Active-Active Replication: Achieve uninterrupted operations through robust, active-active replication strategies that mirror data across multiple distinct environments. If any site experiences downtime, continuous availability persists, safeguarding mission-critical workflows and bolstering overall system resilience and fault tolerance.
- Cloud and On-Premise Deployment: Leverage flexible deployment options across Linux-based systems, whether fully on-premise, air-gapped, or hosted in modern cloud infrastructures. Compatibility with all major Kubernetes distributions simplifies orchestration, ensuring consistent, secure setups that align with organizational policies and resource constraints.
- S3 and Blob Storage Compatibility: Ensure full compatibility with object storage solutions such as MinIO, Amazon S3, Alibaba OSS, and Azure Blob. This flexible integration simplifies data flows, enabling straightforward data ingestion, retrieval, and management within modern cloud-native or hybrid environments.
d. Observability & Monitoring:
- Fine-Grained Monitoring and Metrics Reporting: Leverage robust monitoring and reporting solutions to gain real-time insights into model performance, data synthesis accuracy, and compliance posture. Interactive dashboards and exportable reports in PDF or HTML streamline analysis, facilitate audits, and inform proactive decision-making.
But this is not all. We have a pipeline full of features such as lifecycle management, wide database connectivity, integrated APIs, SDKs, GUI, and more ready to be rolled out within the upcoming months making it easier for you to integrate and scale synthetic data within your own data management systems and workflows, providing enterprises with strategic advantages such as,
- Accelerated Model Development: Cut down on the time spent waiting for or manually cleaning real datasets, and let data teams experiment more freely with quick, on-demand synthetic data generation.
- Risk-Free Testing & Prototyping: Evaluate new systems or algorithms with representative data—minus the regulatory risk of exposing confidential information.
- Seamless Collaboration: Share data internally or with external partners without revealing proprietary or private customer details, enabling frictionless teamwork.
4. Our Synthetic Data Generation Models:
At Betterdata we are building SOTA models such as,
a. ARF:
Which generates high-quality tabular synthetic data in principle through the following process. Starting with 200 “true” data rows, you create another 200 “false” rows by randomly permuting column values (breaking correlations). These 400 rows are used to train a first random forest (RF1). Then, to generate new “false” samples, you randomly pick one tree from RF1, choose a leaf proportional to its data coverage, and focus on the true-labeled data in that leaf. For each column within those true data, sample values by using categorical distributions (for categorical columns) or fitting truncated normal distributions (for continuous columns). After creating 200 such synthetic rows, label them “false” and train a second random forest (RF2) along with the original 200 “true” rows. This process can continue (RF3, RF4, etc.) until the random forest can no longer distinguish true from false (prediction accuracy ~0.5), indicating the synthetic data closely matches the real data.
b. TabTreeFormer:
Which is a specialized model that generates high-quality synthetic tabular data by combining tree-based insights with transformer capabilities. It uses a dual quantization tokenizer to handle numeric values efficiently, providing strong fidelity, privacy, and performance while maintaining a smaller model size and faster computation.
c. TAEGAN:
Large language models (LLMs) have become the standard for generating synthetic tabular data, but their immense size and complexity are often excessive for smaller datasets. Developed by the AI and research team at Betterdata, the Tabular Auto-Encoder Generative Adversarial Network (TAEGAN) is an innovative and specialized GAN-based framework designed for generating synthetic data efficiently specifically for small-scale or scarce tabular datasets. TAEGAN uses a masked auto-encoder generator, which enables self-supervised pre-training for tabular data generation. By allowing the network to “see” more information during training, TAEGAN produces synthetic data that is both realistic and reliable, while avoiding the computational costs of larger models. This balance of performance and efficiency makes TAEGAN particularly well-suited to small or scarce datasets.
Learn more about TAEGAN.
Our Thoughts:
The world is predicted to run out of public data by 2030 and privacy regulations are all set to become more strict in the upcoming years. This has and will create bottlenecks for data-first enterprises to scale their AI/ML models for effective use. Synthetic data is the next best thing to real data, if not better. It allows customizability, augmentation, and data enhancements to improve data fidelity and accuracy enabling models to train on high-quality production data ultimately producing high-quality results whether it is data analysis, forecasting, chatbots, financial modeling, and so on.
