AI innovation faces a chokepoint when it encounters high costs, limited availability and privacy issues of real-world datasets. This is the key reason for the AI industry to shift to high-quality synthetic datasets (using Generative AI) as a way to circumvent the “data walls” that prevent large-scale adoption.
Creating robust artificial intelligence models requires vast amounts of quality data. The drawback of using real-world data only is that it can be costly to collect, heavily regulated and may lack the edge cases needed to create comprehensive models. Synthetically generated datasets help us overcome these limitations.
Synthetic datasets aren’t just supplementary to real data, but a crucial component for industries like finance, healthcare and autonomous systems, which face big barriers related to data collection due to privacy and data scarcity. Organizations can use generative AI technologies to produce unlimited amounts of private compliant data that statistically represent the physical world accurately.
By 2028, synthetic data generation market will skyrocket to $2.1 billion dollars according to BCC Research. This growth shows just how big the demand is with an expected growth rate of 33.1% CAGR.
What is synthetic data and why is it mission-critical?
Synthetic data is an algorithmically generated dataset that replicates the statistical characteristics, patterns and distributions found within a real-world dataset. Most importantly, synthetic data sets don’t include any of the original sensitive information contained in the real-world dataset. Instead, synthetic data is a mirror of the real world created by AI for AI.
Generative AI is the primary technology behind creation of synthetic data.
Here’s how Generative Adversarial Networks (GANs), Variational Auto-encoders (VAE) and large language models (LLM) work along with the ‘why’ aspect that addresses their reason of success.
This approach addresses the “cold start” challenge in AI. Real-world data can be biased, scarce and/or have privacy issues. Synthetic data provides a blank slate with several strategic advantages:
Reports indicate that organizations using synthetic data can reduce their AI development costs by up to 70% and speed up the time-to-market.
Top 7 applications of generative AI for synthetic datasets
Synthetic data applications based on GenAI span autonomous driving, NLP training, fraud detection, and even medical imaging. Learn more about each of the applications in detail here:
1. Autonomous driving & computer vision
Autonomous vehicle training is perhaps one of the most data-intensive challenges engineers has faced to date. To successfully train an autonomous system, the system must be able to detect and identify pedestrians, other vehicles, and obstacles in virtually every conceivable environmental scenario. Using physical test drives to simulate rare events, such as a child running into the road at night during a storm, is both dangerous and inefficient.
Generative AI removes this risk by producing a range of synthetic images and videos. Developers can use simulation-to-real transfer for AI data generation of millions of miles of synthetic driving scenarios, letting them safely simulate critical edge cases. These can cover disparate scenarios as sudden lane changes, multiple car accidents or extreme weather conditions.
Two companies that use this method currently are Waymo and Tesla. By training on synthetic data sets, these companies make their systems ready to tackle potential hazards without having to put actual vehicles on the road.
Statistics: Training on synthetic driving data can reduce real-world testing costs by 40% and enhance the level of validation of safety.
Service: Image and Video Synthetic Data Generation to create large scale simulated data sets for your company.
2. Healthcare and medical imaging
Medical data is considered “gold” for AI development, but access to medical data is restricted by strict compliance regulations such as HIPAA/GDPR. Also, medical data for rare diseases is inherently limited, resulting in challenges developing accurate predictive models for disease diagnosis.
Generative AI helps address these problems by creating synthetic medical datasets that protect patient identity. Generative AI can create synthetic medical images, such as X-rays, MRIs and CT scans, which maintain the biological patterns of real patients, yet don’t include any identifiable information of the patients. Generative AI can also increase the size of medical datasets by creating synthetic data examples of rare pathologies, thus addressing class imbalance in diagnostic tools.
This helps hospitals and biotechnology firms to speed up development of AI for segmentation, diagnosis, and anomaly detection without navigating regulatory and compliance complexities.
Statistics: AI models trained on synthetic medical images can increase detection rates by 20-30%, especially in cases that were previously underrepresented.
Service: Domain Specific Synthetic Data Generation for Healthcare AI.
3. Financial fraud detection & risk modeling
Fraudulent financial transactions need to be detected by developing models based on large transactional logs. Since fraud is rare, a dataset may include millions of legitimate transactions, and only a few fraudulent ones. The rarity of fraud and restrictions on distributing real financial records for training make it tough for AI to identify fraud signatures from existing data.
By generating synthetic transaction data, tabular data and time series data are created that model the statistical characteristics of spending, but introduce rare fraud signatures. By simulating trading patterns and testing the detection of anomalies, banking institutions and FinTech providers can develop and validate algorithms to detect and prevent fraud while protecting all customer data.
Statistics: Synthetic financial data has reduced false positives in fraud detection by as much as 25%.
Service: Tabular + Time Series Synthetic Data Generation.s
4. NLP training with multilingual corpora
NLP models need enormous volumes of data to operate effectively. Although there are plenty of texts available for English, other languages don’t have enough resources. Plus, collecting domain-specific text for English is very difficult to obtain because of confidentiality issues (e.g., legal contracts, medical transcriptions).
Generative AI produces synthetic text data for multiple languages and domains. Thousands of domain-specific chat transcripts, customer service logs and legal documents can be generated at scale. This improves the effectiveness of machine translation, sentiment analysis and intent classification in low-resource environments.
Companies can scale multilingual chatbot functionality without the significant cost of manual data collection and transcription.
Statistics: Training with synthetic data can make NLP models reach up to 92% of the accuracy of those trained on real-world data.
Service: Synthetic Data Generation for Text and Domain-Specific Text Augmentation.
5. Manufacturing and industrial IoT
To determine if a machine will fail, predictive maintenance uses sensor data to analyze the data. The issue is that well-maintained equipment doesn’t fail often; therefore, there’s little to no failure data that exists to train models to identify the early warning signs of a potential failure.
Generative models simulate machine learning training data. The generative models mimic temperature, vibration and pressure signals that would be present under a variety of faulty conditions. With this capability, engineers can enhance digital twin environments with realistic anomaly datasets.
Manufacturing facilities and logistics companies use this data to optimize the timing of predictive maintenance schedules so that a part failure can be predicted before it stops the production process.
Statistics: Companies implementing synthetic IoT datasets see annual reductions in downtime of 15-20%.
Service: Synthetic Time Series Data Generation for IoT Systems.
6. Cybersecurity and threat detection
In terms of cybersecurity, it’s an ongoing battle between attackers and defenders. For security AI to learn about the variety of attack methods and how to defend against them, it must be exposed to a wide range of attack methodologies. But the same can’t be said for the data related to “zero day” attacks; since zero day attacks are defined as attacks that have never occurred prior to the first occurrence of an attack, there simply is no pre-existing data on zero day attacks.
Generative AI creates simulated network traffic, malware samples, phishing attempts and intrusion logs. Generative AI produces tabular and log-based data that replicates the characteristics of sophisticated cyberattacks. These characteristics allow defensive models to identify threats that have never existed in the wild.
Enterprises use this method to enhance the effectiveness of their anomaly detection systems and decrease their risk of exposure to unknown threats.
Statistics: Enterprises using synthetic threat datasets reported a 40% increase in the speed of identifying anomalies when compared to traditional methods of anomaly detection.
Service: Domain Specific Data Generation for cybersecurity platforms.
7. Retail, eCommerce and recommendation systems
Personalization is key to the success of modern retail. To provide recommendations, AI must be able to collect and analyze purchase logs, browsing behavior, and clickstream data. There are common privacy concerns and regulatory obstacles associated with the use of real customer data.
Synthetic data provides a way to circumvent these risks. We can get synthetic customer behavior datasets that closely mimic shopping experiences of different demographics just by running AI algorithms. The datasets that are created include both sequential interaction data and tabular data and used feed recommender algorithms. Many large ecommerce companies such as Amazon and Shopify use similar techniques to predict buyer intent.
The use of synthetic datasets for personalization increases the accuracy of recommended products and lets companies perform “what-if” modeling on their customers’ data without exposing their actual customer data.
Statistics: Companies using synthetic data for personalization experience an average increase in conversion rate of 15-20%.
Service: Synthetic Data Generation for Tabular and Domain-Specific Data for Ecommerce.
Additional emerging applications
These use cases represent some of the emerging industries where synthetic data is being used to develop new applications:
- Smart Cities: Developing synthetic video and sensor data to model traffic flow and utility usage for city planners.
- Defense: Creating robust simulation models for aerospace and defense applications.
- Education: Developing adaptive learning models in EdTech that respond to the needs of students without collecting student data.
Conclusion
Synthetic datasets produced using Generative AI are no longer experimental luxuries, but rather mission critical to the deployment of compliant, safe, and scalable AI in industries such as autonomous systems, healthcare, financial services and many others.
To be competitive, companies impacted by data limitations (such as class imbalance), compliance requirements, etc. need to consider using synthetic datasets as a viable solution to their data challenges. HabileData provides scalable synthetic dataset creation across all major types of data (tabular, image, video, text, time series) allowing organizations to more quickly and affordably implement AI within their business models.
