In the age of AI-driven products and data-centric innovation, startups often face one massive hurdle: access to quality data. Real-world datasets can be expensive, hard to obtain, or subject to legal and ethical restrictions. Fortunately, there’s a powerful solution gaining momentum—synthetic data.
For startups, synthetic data offers a fast, scalable, and privacy-compliant way to build and test machine learning models without relying on sensitive or restricted datasets. This guide walks you through the process of creating synthetic data, its use cases, and the best practices to implement it effectively.
What Is Synthetic Data?
Synthetic data is artificially generated information that replicates the statistical characteristics of real-world data. It is not collected from actual events or users but is created using algorithms, simulation models, or generative AI techniques.
There are two main types:
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Structured synthetic data: Tabular data (e.g., sales records, health logs) generated to mimic original datasets.
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Unstructured synthetic data: Includes images, videos, audio, and text data generated using AI models like GANs or transformers.
Startups can use synthetic data to prototype, train, test, and even deploy AI solutions while avoiding the challenges tied to real-world datasets.
Why Synthetic Data Is a Game-Changer for Startups
Startups often operate with limited resources, little access to large-scale proprietary data, and tight timelines. Synthetic data helps solve several of these pain points:
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🚫 No compliance headaches: No personal or private data means GDPR, HIPAA, and other regulations don’t block progress.
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💸 Lower costs: No need to buy expensive licensed data.
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⚡ Faster iteration: Quickly generate datasets for A/B testing, model training, or validation.
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🔐 Privacy-safe: No risk of exposing user data.
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🔄 Customizable: Tailor datasets to specific edge cases, class distributions, or rare scenarios.
For startups in sectors like fintech, healthtech, retail, and mobility, synthetic data can accelerate go-to-market while preserving ethical standards.
How to Create Synthetic Data: A Step-by-Step Guide
Creating synthetic data can be done in-house or using third-party tools. Here’s a basic roadmap:
1. Define Your Data Requirements
Start by answering:
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What kind of data do you need? (e.g., text, image, tabular)
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What will it be used for? (e.g., classification, NLP, regression, simulation)
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What are the target features, distributions, and formats?
2. Choose a Generation Method
For structured/tabular data:
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Use statistical simulation (Monte Carlo, Bayesian networks)
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Use open-source tools like:
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SDV (Synthetic Data Vault)
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CTGAN/TVAE models
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For unstructured data:
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Use deep learning:
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GANs (for images)
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Transformers (for text)
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TTS/Voice models (for audio)
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For healthcare and finance, specialized synthetic data platforms like Mostly AI, Gretel.ai, or Opendatabay provide pre-generated, domain-specific datasets.
3. Validate and Test
Always test synthetic data:
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Compare statistical similarity with original data (if available)
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Check correlation, distribution, and edge cases
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Run your ML model to ensure synthetic data yields valid results
4. Document Your Dataset
Add metadata, methodology, generation date, and intended use cases. Transparency builds trust—especially if you plan to sell your data on a platform like Opendatabay.
Where to Sell or Use Synthetic Data
Once created, your dataset has value.
You can:
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Train internal AI models
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Validate algorithms or simulations
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Share with collaborators or open-source projects
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List on a marketplace like Opendatabay to monetize your efforts
Opendatabay, known as a leader in the AI data economy, allows startups to list their synthetic data securely, tag by industry, and connect with developers, researchers, and enterprises looking for custom datasets.
Best Practices for Startups Using Synthetic Data
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✅ Always disclose that your data is synthetic when sharing or selling it
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✅ Don’t overfit your model to synthetic-only data—combine with real data when available
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✅ Iterate quickly—use synthetic data to test ideas before investing in data acquisition
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✅ Monitor model performance—ensure accuracy and fairness aren’t compromised
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✅ Respect licensing if using tools or pretrained models to generate data
Final Thoughts
For startups, synthetic data isn’t just a workaround—it’s a launchpad. It enables experimentation, protects privacy, reduces costs, and unlocks scalability. Whether you’re in healthtech training diagnostic models or fintech simulating fraud detection, synthetic datasets are a powerful tool in your arsenal.
Platforms like Opendatabay make it even easier to access, create, and monetize synthetic data—making the benefits accessible to teams of all sizes.
In 2025 and beyond, data-driven innovation starts not with massive databases—but with smart, synthetic strategies.