How Data Augmentation Can Use Synthetic Data for Insufficient Datasets

“AI Needs More Data.” It’s not an understatement, but the truth.

Machine learning models require a lot of data to learn well. But, when there’s not enough data in the first place, your ML model will only memorize and work based on what it’s been fed. It may fail when shown something new. Here, data augmentation can help.

Data augmentation is the process that makes small changes to an existing dataset to create new datasets that can be used for training ML models. Here are a few examples:

• Flip, crop, or color-shift an image.
• Replace words in a sentence with synonyms.
• Add noise to sensor data.

These minor changes will help your ML algorithms learn from different versions of the same data, but here’s the catch — even data augmentation has limits.

The Big Problem: Insufficient Real-world Data

A recent report shows that 85% of AI projects could fail because the data is either low-quality or not enough.

Even with data augmentation, many AI projects will hit a wall. This is because real-world data is:

• Limited
• Hard to collect
• Expensive and time-consuming to label and clean
• Legally restricted (for medical, public and financial records)
• Incomplete or biased (missing diversity)

Feeding such kind of data to your ML models will lead to AI models that aren’t fully trained, and may not work well in real-world use.

The Big Solution: Synthetic Data

Synthetic data is defined as artificially generated data, which is commonly produced by synthetic data generation tools. It looks and behaves like real data but is generated artificially. Synthetic data can be in many formats:

• Text in the form of tabular data
• Images/ Videos and other media
• Audio
• Time-series data (e.g., sensor readings, stock prices)
• Graphs or Networks (e.g., social networks, molecular structures)
• Code
• And others

Since synthetic data is generated artificially, you can create unlimited examples and include rare or edge cases. Usually, AI engineers like to mix synthetic data with real data so the AI can train and perform better.

How Synthetic Data Supports Augmentation?

Synthetic data augmentation is a technique used in machine learning to artificially expand the size and diversity of a dataset by generating new, realistic data points. Here’s how synthetic data can benefit data augmentation:

• It fills in data gaps and helps simulate rare conditions that are hard to find in real-world data.
• It saves a lot of time and effort as you don’t have to wait for collecting real data.
• Since no real user data is used, it eliminates privacy concerns and ensures compliance.
• It saves time and expenses by skipping the manual process of collecting and cleaning real data.
• It allows you to control bias by adding underrepresented groups or scenarios to balance datasets.
• You can model and test rare or risky events without any real-world danger.

Synthetic Data Application for Data Augmentation

Industry	How Synthetic Data Helps
Automobile	Synthetic road scenes can train AI to handle rare cases like sudden obstacles or unidentifiable objects on the road.
Healthcare	AI models can use synthetic X-ray data to help with accurate diagnosis while keeping real patient information private.
Finance	Banks can create synthetic transactions to train fraud detection systems on both normal and suspicious patterns.
Retail	Synthetically generated product images can help AI recognize items in different lighting conditions or located in different store layouts.

How to Generate Synthetic Data?

You can generate synthetic data by using methods like GANs (Generative Adversarial Networks), statistical modeling, or even game engines that can create images, text, or sensor data that looks real.

You can customize these datasets to get labeled automatically and follow the same patterns as actual data. Another way to generate synthetic data is to use platforms like Syncora.ai, which can automate this entire process.

Syncora.ai is a synthetic data generation platform that is powered by Agentic AI. It creates high-quality, labeled datasets for AI projects where real data is missing, limited, or sensitive.

Here’s what Syncora.ai offers:

• AI agents that analyze and generate synthetic data automatically
• Generate synthetic data in minutes, which will save you weeks of manual work
• Compliant with HIPAA, GDPR, and other privacy regulations
• Data generation that works across formats like text, images, tables
• Get access to the dataset uploaded by the data contributors on the platform.

With Syncora.ai, create the right synthetic data faster - no privacy risks, no bottlenecks, just seamless data augmentation.

To Sum It Up

Data augmentation is a great way to expand limited datasets, but it will work only if you have enough real data to begin with. With synthetic data generation, you can fill in missing pieces, simulate rare scenarios, and let your AI model train and perform better. With synthetic data generation tools like Syncora.ai, you can create high-quality synthetic data fast and safely — all without privacy and labeling challenges.

FAQ

1. What is Synthetic Data Augmentation?
   Synthetic data augmentation is the process of creating new, realistic data points using AI. This helps expand your dataset and improve model performance, especially when real data is limited.
2. How is synthetic data different from traditional data augmentation?
   Traditional data augmentation modifies existing real data to create variations. For example, an image of a cat might be flipped, rotated, or color-adjusted to create more training examples. When it comes to Synthetic data, it is entirely new and generated by AI models like GANs or agentic AI agents like Syncora. Example: instead of just modifying a picture of a cat, synthetic data could generate a completely new image of a cat in a different pose, breed, or setting.
3. Why use synthetic data for data augmentation?
   Synthetic data for data augmentation helps by filling gaps, simulating rare events, and reducing bias without the need to use real user data. This makes the process fast, inexpensive, and privacy-safe.
4. What types of datasets benefit most from synthetic data augmentation?
   Datasets in different industries like healthcare, finance, banking, IoT, or any domain where privacy is important can benefit from synthetic data augmentation.
5. What tools are used for synthetic data generation in augmentation workflows?
   You can use tools like Syncora.ai that allow you to generate high-fidelity synthetic data in minutes. It can generate data for edge cases, is privacy compliant, and doesn't need manual efforts.