AI has rapidly evolved from tools that assist humans in making decisions to autonomous agents of change.
Far from the niche academic pursuit spearheaded in the 1950s – smart but inflexible systems that relied heavily on programmed rules – AI’s vast computational power is now streamlining operations, enhancing customer experiences, and driving innovation across sectors worldwide.
This deep-learning revolution that was ignited some 60 years later with the advent of OpenAI shows no sign of slowing down either. Yet while we enter the exciting new Agentic Era of autonomous innovation, we still rely heavily on quality input from human hands to fuel its progress.
Enter synthetic data: a cheat code for tackling real-world challenges and unlocking new worlds.
Throughout this comprehensive guide, we’re covering the ins and outs of this exciting field. Business leaders looking for a deeper understanding of its effect on artificial intelligence and machine learning will discover valuable insights, practical knowledge, and real-world examples – all carefully engineered to help you keep pace with advancements.
From real-world data problems and risk management to a step-by-step process to starting your synthetic journey, you’ll get a strong foundation of knowledge for strategic planning, and that all-important competitive edge.
What is Synthetic Data Generation?
Synthetic data is artificially generated information that mimics real-world data in structure and characteristics. Unlike natural data harvested from real-world events or interactions, synthetic data is created through generative models and simulations. It’s customizable, diverse, and ethical in terms of privacy compliance. Enticing for the world’s most innovative enterprises, synthetic data generation techniques can create complex datasets tailored to specific use cases – future-proofed for tomorrow’s tastes.
But How Big is Synthetic Data Today?
Gartner predicted that 60% of the data used for AI development and analytics projects would be synthetically generated by this year, later becoming the main source of training data in AI models by 2030. Further research predicts the global synthetic data generation market size could reach 8,869.50 million dollars by 2034, expanding at a CAGR of 35.28% between 2024 and 2034. With so many possible applications of synthetic data for today’s businesses, it’s easy to see why.
How is Synthetic Data Used in AI Fields?
Following OpenAI’s GPT-4o, Google’s Gemini, and Meta’s Llama 3.3, Microsoft recently released its Phi-4 language model, which was also trained mainly on synthetic data.
Microsoft evaluated Phi-4’s output quality across over a dozen benchmarks and found the algorithm outperformed its predecessor across all but one and, in some cases, by more than 20%. Furthermore, Phi-4 bested GPT-4o and Llama 3.3 across two important benchmarks: GPQA and MATH. The former dataset comprises 448 multi-choice questions spanning various scientific fields.
According to Microsoft, Phi-4 outperformed Llama 3.3 by more than 5% across both tests – despite having a fifth as many parameters.
“Phi-4 outperforms comparable and larger models on math-related reasoning due to advancements throughout the processes, including the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations,” said Ece Kamar, managing director of Microsoft’s AI Frontiers group.
However, Natural Language Processing (NLP), e.g. text classification, translation, and summarization, is just one of the ways synthetic data is now being applied.
Computer Vision
Synthetic data is helping a range of organizations generate diverse, high-quality training data for images and videos to teach machine learning algorithms. Some of its applications include:
- Object detection and recognition: Efficiently labeling people and objects in images, e.g. cars and pedestrians for autonomous driving.
- Facial recognition: Creating synthetic faces with variations in ethnicity, gender, and age, without worrying about privacy concerns.
- Augmented Reality (AR) and Virtual Reality (VR): Producing 3D synthetic scenes for testing AR/VR applications.
- Medical Imaging: Synthesizing X-rays, MRIs, or CT scans to augment datasets for rare diseases.
Robotics & Autonomous Systems
Robotics and autonomous systems also use synthetic data as a safer and more cost-effective alternative by:
- Simulating environments: Simulators like Gazebo or Unity train robots in virtual environments so they can navigate or manipulate objects.
- Testing self-driving cars: Platforms like CARLA use synthetic data to simulate diverse traffic conditions, helping to test and improve autonomous vehicles.
Healthcare & Biomedicine
Healthcare is also harnessing the power of synthetic data to accelerate innovation while addressing ethical considerations and privacy concerns. It does so through:
- Medical imaging: Synthetic X-rays, ultrasounds, and pathology slides are becoming invaluable for training diagnostic AI.
- Drug discovery: Experts can predict drug properties or interactions, generating synthetic molecular data.
- Patient data: Synthetic electronic health records (EHRs) protect patient privacy, simultaneously enhancing records for rare conditions or underrepresented populations.
- Disease modeling: Simulating disease progression or patient outcomes to train predictive healthcare models.
Gaming & Entertainment
Synthetic data has revolutionized gaming and multimedia content creation via:
- Game testing: Developers use this data to simulate player behavior, quickly testing and debugging games.
- Character design: Efficiently generating synthetic avatars, textures, and animations.
- Content generation: Helping to embellish virtual environments, creating immersive gaming experiences and realistic graphics.
Fraud Detection & Cybersecurity
Organizations use synthetic data to train models in secure, controlled environments for:
- Financial fraud: Synthetic transaction records with fraudulent activities train AI detection systems.
- Phishing: Similarly, synthetic phishing emails and websites help AI spot scammers.
- Intrusion detection: Improving security measures by simulating cyberattacks in synthetic networks.
Speech & Audio Processing
Synthetic audio data improves speech recognition, language understanding, and audio synthesis systems:
- Speech recognition: Creating synthetic audio datasets with diverse accents, languages, and noise levels.
- Text-to-Speech (TTS): Synthetic voices are implemented to train and fine-tune TTS models.
- Emotion detection: Synthesized audio samples can now be applied with varied emotional tones for classification tasks.
Finance & Banking
Synthetic financial data enables safe and efficient model development in highly regulated environments such as banking. This helps with:
- Market simulation: Generating synthetic stock market data to train trading algorithms.
- Anomaly detection: Creating synthetic anomalies in transaction data, radically improving fraud detection.
- Risk assessment: Simulating credit and loan applicant data to test and validate risk models.
Environmental & Geospatial AI
Synthetic data is now addressing environmental challenges while improving geospatial analysis. It does this through:
- Satellite imaging: Generating synthetic satellite images to train models for land use analysis, disaster response, and environmental monitoring.
- Weather prediction: Improving forecasting models by simulating extreme weather events.
- Urban planning: Creating synthetic cityscapes for urban development simulations and traffic optimization.
However, despite having countless applications, this is just the tip of the iceberg of what synthetic data can do.
Why Synthetic Data is a Game-Changer For AI Training
Research suggests that 57% of content on the internet today is either AI-generated or translated using a machine learning algorithm. However, this massive amount of AI content is causing an issue for tools like Copilot and ChatGPT which rely on information from the internet for training. The major problem is that it’s limiting their scope, leading to inaccurate responses and misinformation. Consequently, over 35% of the world’s top 1,000 websites are now blocking OpenAI’s web scraper, and around 25% of data from “high-quality” sources has been restricted from the major datasets used to train models.
Should this access-blocking trend continue, researchers forecast that developers will run out of data to train generative AI applications between 2026 and 2032. Add to this copyright issues, objectionable material, and unrepresentative biases and we’re now facing a scarcity of quality data to empower our models.
“In an era where we are literally producing more data than humankind ever has before, we’re running out of the specific types of data needed for AI training,” says Bart Willemsen, VP analyst at Gartner to Fierce Network.
Synthetic data offers a sustainable, scalable, and flexible alternative. Importantly, it means that businesses can create tailored datasets for specific applications, without worrying about the limitations of traditional data.
Five Major Benefits of Synthetic Data
1. Privacy and Security
With regulations like GDPR and HIPAA acting as watchful compliance guardians, data privacy is more vital than ever in the era of AI. Synthetic data ensures models are trained on accurate and high-quality data, without any danger of exposing sensitive personal information. By mirroring the statistical patterns of real-world data samples – and without actually including any user records – synthetic data eliminates the risk of data breaches. This is important to note when in the US alone, the number of data breaches increased from 447 in 2012 to more than 3,200 in 2023.
2. Scalability
Undoubtedly, the demands of modern AI are growing exponentially and models like GPT-4 and DALL·E require billions of parameters to function effectively. The good thing is that synthetic data is inherently scalable; it allows organizations to generate large datasets, quickly matching these rising complexities. While real-world data has its limitations in volume and variety, synthetic data can be produced endlessly for a wide range of applications. Harvard research suggests that scalable synthetic datasets can accelerate AI development timelines by up to 40%, deploying much faster iterations.
3. Diversity
One of the most significant challenges in traditional datasets is their inability to represent rare events or minority groups effectively. Unfortunately, this can lead to dangerous biases and blind spots in AI models. Synthetic data solves this problem by enabling the deliberate creation of diverse datasets that consider outliers and edge cases. Not only does this improve the robustness of models, but it also ensures their performance across a broader range of scenarios. In medical imaging, synthetic data is being used to create training datasets for rare diseases and reports suggest it’s improving diagnostic AI accuracy by up to 20%.
4. Cost-Effectiveness
Whether manual labeling, cleaning, or experimentation, traditional data collection can be costly. Synthetic data provides a cost-effective alternative by eliminating many of these labor-intensive processes. Once a synthetic data generation pipeline is established, it can produce comprehensive datasets at a fraction of the cost – regardless of volume or complexity. A study by McKinsey & Company found that synthetic data can reduce data collection costs by 40% and improve model accuracy by 10%.
5. Ethical AI Development
When AI systems are trained on real-life data they often inherit biases embedded in the datasets. Synthetic data eliminates this issue by allowing developers to design balanced and unbiased datasets. This fosters the creation of AI systems that treat all users equitably, regardless of their demographic characteristics. One study showed that synthetic data can reduce biases in AI models by up to 15%. In particular, biased data due to underrepresentation or skewed demographics can be corrected by generating diverse, synthetic examples. Furthermore, tools like Generative Adversarial Networks (GANs) are now being employed to create more balanced datasets, significantly improving model fairness in healthcare, finance, and social science.
Leveraging advanced techniques like these, synthetic data providers are completely transforming how we think about AI training. However, although there are seemingly infinite applications for synthetic data, there’s one tried and tested process for getting it right.
The Synthetic Data Generation Process
1. Defining requirements
The first step involves identifying the type of data you require and its characteristics – often tailored to specific AI project goals. For example, Tesla and Waymo define requirements based on real-life driving scenarios when developing autonomous vehicles. These could include urban intersections, nighttime visibility, or adverse weather conditions. This phase is crucial in ensuring the generated data aligns with the needs of the machine learning application that’s being trained.
2. Data simulation
This step involves leveraging advanced techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), or rule-based statistical models to create synthetic data points. For instance, GANs are widely used in computer vision to generate realistic images of objects, faces, or environments that resemble real-world samples. In retail, e-commerce platforms simulate customer interactions using synthetic datasets to train recommendation engines. For example, generating purchase histories for fictitious users helps test how well algorithms recommend products to new customers.
3. Validation
Validation ensures synthetic data meets the quality and relevance standards you need for success. This step involves statistical analysis to compare the synthetic data with real-world benchmarks. For example, pharmaceutical companies like Pfizer ensure synthetic clinical trial data matches real-world results. Consequently, this layer of process ensures the safety and efficacy of predictive models.
4. Integration
The final step is incorporating synthetic data into existing AI workflows for testing and training machine learning models. This includes combining synthetic data with real-world datasets (if available) to enhance model performance and generalization. For example, businesses like IBM use synthetic conversational data to enhance chatbot training. By integrating diverse synthetic conversations, businesses ensure systems can handle nuanced and varied customer inquiries more effectively.
Finding The Right Partner & Starting Your Synthetic Journey
There’s a lot involved when embarking on a synthetic data journey. Firstly, you’ll need to identify gaps in your current datasets and define your specific business goals. Then it’s important to research providers to find the right fit for your organization and project. To select the most appropriate synthetic data provider, carefully consider the following:
- Domain expertise: Ensure the company understands the specific needs of your industry.
- Quality assurance: Look for providers with robust validation and testing methodologies.
- Customization: Only opt for companies that offer tailored solutions, not “one-size-fits-all.”
- Privacy measures: Verify their approach to data privacy and security; they should communicate these credentials on their website.
- Integration capabilities: Assess their ability to seamlessly integrate synthetic data with your existing systems via a synthetic data platform.
- Testing and scaling up: Start with pilot projects that can validate the effectiveness of synthetic data. Then use a synthetic data generation tool to integrate solutions across broader AI initiatives.
EC Innovations is now applying AI in many operational areas, including localization, software development, and testing. However, one of AI’s most significant upsides we’ve witnessed is in the field of AI data services. Fully ISO-accredited and trusted by major enterprises in critical business fields such as IT & technology, medical & pharmaceutical, LLMS (Large Language Models), autonomous driving, finance, and more, your project is in safe hands with us.
Conclusion: Joining The Synthetic Data Revolution
Synthetic data is no longer a supplemental resource for enterprises; it’s an essential element in the AI ecosystem. This is reflected in the reality that around 60% of machine learning models incorporated synthetic data for at least one stage of development last year. However, the growing prevalence of this data makes complete sense for businesses looking to innovate and stay relevant for their customers. Unlike real-world data that becomes outdated, this powerful insight is evolving in real time. With generative AI innovations, federated learning integration, and wider industry adoption, there’s never been a more exciting time to join the synthetic data revolution.
With that being said, synthetic data still has its challenges. Capturing the intricacies of ever-evolving real-world scenarios is a complicated process and ensuring synthetic data performs equivalently to a real dataset requires rigorous testing and validation.
However, by addressing the challenges of data scarcity, privacy, and cost, synthetic data can truly empower businesses seeking to unlock new worlds of AI performance.
For enterprises aiming to stay ahead in the AI-driven era, embracing synthetic data solutions isn’t just an option—it’s a necessity. Explore the endless possibilities of synthetic data and start transforming your AI projects into unparalleled success stories.
