Just two months into 2025, the tech world witnessed the launch of Deepseek, a groundbreaking generative AI large language model (LLM) that is directly challenging OpenAI’s ChatGPT—widely regarded as the global leader in AI. This marks the beginning of a new and much more competitive race for dominance in the AI and tech world. A boardroom discussion you might have already had.
But to compete enterprises need high-quality data. This is a resource that is increasingly difficult to access due to stringent data privacy laws, biases in public datasets, and the challenges of collecting balanced, representative data.
Pre-Training is Data Intensive:
This comes as no surprise but pre-training an AI model to think like a human requires billions of data parameters depending on the complexity of the model, the domain, and the desired level of generalization.
⭕ Model Complexity: Refers to the scale at which the model is being pre-trained. To put this into perspective GPT-3 has around 175 billion parameters. Deep learning models, vision transformers, and other such large-scale LLMs can have similar data requirements. Enterprises here have to pre-train these models on vast amounts of data to avoid overfitting and ensure better generalizability.
⭕ Domain Coverage: For the LLM, DGM, or others to generalize well, the pre-training dataset has to cover a broad range of domains and scenarios. Essentially it should be able to think on different levels for different scenarios. This requires training data to be diverse encompassing multiple scenarios, patterns, and characteristics.
The purpose of pre-training is transfer learning where a model can apply its learned knowledge to new, unseen tasks. Multiple factors influence how a model behaves post-training all of which data teams have to account for.
Chief among them is data quality. Low-quality real data irrespective of how much of it an enterprise has will not result in anything you can be proud of. The same is true for low-quality synthetic data. It all comes back to the century-old saying, Garbage in, garbage out.
Real Data is the Real Challenge 👀:
Can’t live with it, can’t live without it. This is the best way to describe the role of real data in building any AI model. Real data, especially sensitive customer data (which ironically cannot be used without anonymization) is immensely important for training machine learning models. However, since it is heavily protected and regulated globally under different privacy laws, it is next to impossible to access and share it without spending months in data anonymization and approvals increasing timelines and affecting data utility. This along with several other challenges, especially public data has created major bottlenecks for enterprises hindering AI innovation. To summarize a few,
❌ Data Collection: Collecting large datasets can be expensive, time-consuming, and logistically challenging, especially for specialized domains (e.g., medical imaging, and autonomous driving).
❌ Data Labelling: Supervised pre-training requires labeled data, which can be costly and labor-intensive to obtain.
❌ Storage and Processing: Storing and processing large datasets requires significant computational resources, including high-capacity storage systems and powerful GPUs/TPUs.
❌ Privacy and Ethics: Large datasets often contain sensitive information, raising concerns about privacy, consent, and ethical use.
❌ Data Scarcity: In some domains, data is inherently scarce or difficult to collect (e.g., rare diseases, extreme weather events).
❌ Limited Scalability: As datasets grow, organizations must continuously invest in infrastructure to keep up with demand.
❌ Edge Cases: The training dataset might not cover rare or unusual scenarios causing domain gaps.
❌ Data Bias or Underrepresentation: Datasets may overrepresent certain groups or scenarios while underrepresenting others.
While everyone including us agrees real data is important and can not be fully replaced. These challenges impact how quickly and efficiently enterprises scale their AI/ML models.
Why Synthetic Data?
Synthetic data is artificially generated data that mimics real-world data in terms of structure, patterns, and statistical properties. Unlike real data, which is collected from actual events, interactions, or measurements, synthetic data is created using algorithms, simulations, or generative models. It is designed to replicate the characteristics of real data while avoiding its limitations, such as privacy concerns, scarcity, or bias. Enabling enterprises to innovate and accelerate their data-driven growth and digital transformation strategies while remaining compliant with data privacy laws without compromising data utility.
Synthetic data is increasingly being used in AI/ML development, testing, and research because,
✔ Synthetic Data is Highly Private:
Synthetic data by nature does not directly link to real individuals as it contains no Personally Identifiable Information (PII) such as names, addresses, ID numbers, medical records, etc. It is generated using machine learning models that learn from real data first and then generate artificial records of the real dataset eliminating any risk of re-identification and is completely compliant with global privacy laws issued by GDPR, CCPA, PDPC, etc.
Read more on how synthetic data protects data privacy.
✔ Synthetic Data is High-Quality Data:
By using advanced techniques like GANs, VAEs, and LLMs it is now possible to generate synthetic data that surpasses real data in terms of utility. Synthetic data can replicate the statistical properties, patterns, and relationships found in real data with remarkable accuracy. This means that the important parts of the real dataset that were unusable under anonymization can now be freely and safely used by data teams in training AI models exponentially expediting the model development process and improving machine learning accuracy.
Read more on improving ML models with synthetic data.
✔ Synthetic Data can be Augmented:
Synthetic data can be augmented to increase the size and diversity of the dataset without collecting additional real-world data. This improves model generalization allowing it to learn more, especially useful for edge cases where real data is scarce.
Read more on synthetic data augmentation using TAEGAN.
✔ Synthetic Data can be Enhanced:
Synthetic data can be used to improve the quality, balance, or coverage of a dataset by adding new, artificially generated data that complements the existing data. This is particularly useful for training AI models on fair and unbiased data protecting brand image and trust.
✔ Synthetic Data is Scalable:
Synthetic data can be generated on demand quickly and without relying on additional real-world data to meet incremental data needs while training or fine-tuning an AI/ML model. This gives enterprises access and control to real-like data which can be generated and used anytime the enterprise needs it avoiding problems like data drifts.
Pre-Training with Real and Synthetic Data:
Using both synthetic and real data separately or merging them is a business decision that we will leave for you to decide. However, it is worth considering using both for a few reasons.
Leveraging both real and synthetic data can create a powerful, synergistic combination that drives exceptional AI model performance. Real data provides authenticity and real-world relevance, while synthetic data offers scalability, diversity, and control. Together, they form a deadly duo that addresses each other's limitations and unlocks new possibilities for AI development.
Synthetic data can be augmented to introduce diverse scenarios not present in real datasets, ensuring models are exposed to a wider range of patterns and edge cases. This helps avoid overfitting by introducing controlled variations that improve generalization, while also solving underrepresentation by generating synthetic data for rare or missing scenarios. Additionally, the combination of real and synthetic data can mitigate biases inherent in real-world datasets, leading to fairer and more robust models.
Average data quality leads to average-performing models, while great data quality achieved through the strategic use of both real and synthetic data leads to great-performing models. By integrating real and synthetic data, enterprises can create high-quality, balanced, and comprehensive datasets that empower AI models to perform at their best in real-world applications. This approach not only enhances accuracy and reliability but also future-proofs AI systems against evolving challenges and complexities.
Why BetterData:
Better data equals better-performing models. Our SOTA models are designed for scaling, generating, and augmenting high-quality and representative synthetic data that accurately and precisely replicate real data's statistical properties and underlying patterns at scale. With Betterdata you get,
🟣 A comprehensive infrastructure for managing, training, and deploying cutting-edge generative models, optimized for synthetic data generation.
🟣 Integrated Differential Privacy (DP) and advanced generative anonymization strategies to produce compliant synthetic datasets.
🟣 Uninterrupted operations through robust, active-active replication strategies that mirror data across multiple distinct environments.
🟣 Flexible deployment options across Linux-based systems, whether fully on-premise, air-gapped, or hosted in modern cloud infrastructures.
🟣 Robust monitoring and reporting solutions to gain real-time insights into model performance, data synthesis accuracy, and compliance posture.
Learn more on Scaling Enterprise AI/ML with Betterdata.
Additionally, by integrating differential privacy into the entire synthetic data generation pipeline we provide quantifiable privacy guarantees while balancing synthetic data utility and privacy to customize synthetic data according to the unique data schema the enterprise operates on.
Learn more about how we balance synthetic data privacy and utility.
