Pre-training and fine-tuning AI models requires an extensive amount of clean, structured, and balanced data. However, sensitive customer data is heavily regulated by data protection agencies such as PDPC, CCPA, and HIPAA while public data is heavily biased, imbalanced, and incomplete.
Challenges with Sensitive Customer Data:
❌ Strict regulations make data collection, use, storage, and sharing time-consuming and costly.
❌ Encrypting or masking personally identifiable information (PII) destroys data utility.
❌ Running circles to obtain and manage explicit customer consent for data usage.
❌ A constant struggle to ensure third-party vendors or tools used in training also adheres to data protection standards.
❌ Risking AI models from memorizing and potentially leaking sensitive data.
Challenges with Public Data:
❌ Public data is not accurate, reliable, and free from errors or inconsistencies.
❌ Spend time and money to identify and mitigate biases present in publicly available datasets.
❌ Ensuring the public data is applicable and useful for the specific AI model's purpose.
❌ Managing the sheer scale of public data can be overwhelming and resource-intensive.
❌ Public data is noisy and often is not up-to-date to reflect current realities.
These along with a million other challenges make it impossible for fast-growing enterprises to train AI models effectively, correctly, and on time, limiting their capacity to compete in global and regional markets.
Enterprises thus have to either work with low-quality data impacting model performance or risk legal action against them by exposing sensitive customer information.
The Cost of Non-Compliance 💸:
A report by IBM stated that the cost of data breaches in 2024 was 4.48 million dollars globally, a 10% increase from 2023. This makes sense since traditional anonymization techniques have become redundant in protecting data privacy and the AI race has exponentially become more competitive. Lawsuits are already being brought against GenAI companies with concerns that the data being used to train machine learning algorithms is either infringing on copyrights, scraping, or misuse of data without consent.
- GitHub faced a class action lawsuit claiming that their Copilot tool was copying and republishing code without attribution and that GitHub was misusing users’ personal data.
- Microsoft and Open AI were sued by The New York Times claiming Open AI used millions of articles published by The New York Times to train its chatbots which were being marketed as an alternative source for reliable information.
- Meta and OpenAI were sued by Sarah Silverman claiming that both of these organizations used illegally acquired copies of her books to train ChatGPT and large Language Model Meta AI (Llama) through torrenting.
- A class-action lawsuit was brought against Google claiming that Google allegedly misused personal information and infringed on copyrights to train Bard, a competitor to Chatgpt.
The winning formula is simple. The ones who have the highest amount of high-quality production data will win. As a result, organizations are now looking at alternative data sources. A source that protects data privacy without limiting and preserves data utility. In simpler words, enterprises are now shifting to scalable and programmable synthetic data.
What is Synthetic Data:
Synthetic data refers to artificial data generated by algorithms that mimic the statistical properties of real-world data without containing any personally identifiable information (PII). Synthetic data preserves data utility by replicating data points, statistical values, structures, and correlation, and since it does not contain any PII, synthetic datasets cannot be traced or linked back to real individuals.
Synthetic data gives enterprises, startups, or other businesses access to fast-moving, high-utility, and secure data enabling them to train high-performing AI models for data analysis, forecasting, GenAI, automation, and more.
Synthetic Data is the Future of AI Model Training 🚀:
Gartner predicts that by 2030, synthetic structured data will grow at least 3 times as fast as real structured data for AI model training. We can give 5 reasons why:
✔ Safe and Secure Data Sharing:
Synthetic data does not contain any PIIs making synthetic datasets easy and fast to share internally and externally without compromising data privacy thus reducing timelines for AI model training while ensuring compliance with local, regional, and international data protection regulations.
At Betterdata we incorporate differential privacy and advanced anonymization techniques to generate secure and safe synthetic data. Click here to read more.
✔ Synthetic Data can be Augmented:
As we run out of public data, synthetic data can be augmented to scale limited or scarce datasets, especially for edge cases quickly without running massive data collection campaigns, increasing domain coverage to maintain optimal amounts of quantity without compromising data quality and diversity, enabling better generalization in AI model training as you scale it.
Augment limited datasets for edge cases through our SOTA synthetic data model. Click here to read more.
✔ Enrichment and Customization:
Synthetic data can be enriched and customized to improve data quality by balancing datasets by creating synthetic samples for underrepresented classes, adding variability to training data for a wider range of scenarios, correcting inconsistencies and outliers, and customizing synthetic datasets to meet specific requirements for AI model training.
✔ Synthetic Data is Scalable:
Synthetic data generation is a subset of GenAI which essentially means that anyone can generate synthetic data on demand. Using synthetic data enterprises can generate diverse and balanced datasets to fulfill data needs quickly as they scale AI models.
We provide a comprehensive and scalable synthetic data generation system. Click here to read more.
✔ Preserves Data Utility:
Synthetic data replicates the statistical properties and patterns of real data, ensuring high utility for AI training without compromising data quality. Unlike anonymized data which often suppress, encrypt, or mask data points, reducing its usefulness, synthetic data maintains the integrity and richness of the original dataset.
We have a SOTA model for High-Utility Tabular Synthetic Data Generation. Click here to read more.
Training AI models require vast datasets, which can be expensive, time-consuming, and challenging to collect and process. Synthetic data offers a more efficient solution by creating a safe, controlled, and scalable environment for training and testing AI models. It ensures robustness and reliability before deployment by generating high-quality, diverse, and balanced datasets enhancing model performance, and accelerating scale while maintaining security and compliance.
