Synthetic data: The artificial backbone of tomorrow’s AI revolution
Synthetic data is expected to accelerate AI development and drive industry-specific solutions, transforming data generation, and privacy concerns.
Data remains a pervasive challenge to AI advancement
Data, the "oil of this century," is vital for training AI, yet its acquisition is a formidable challenge, necessitating innovative strategies.
Key real-data challenges
Data Quality: Maintaining high data quality is vital for dependable insights but is hindered by inconsistency and inaccuracies.
Data Availability: Acquiring on-demand, pertinent data is crucial for informed decisions but is often impeded by access restrictions.
Data Bias: Striving for unbiased data is critical for fair outcomes, yet complicated due to inbuilt biases in data collection.
Data Governance: Implementing robust data governance is the key to avoiding misuse, but it requires considerable policy and infrastructure efforts.
Cost and resources: Allocating resources for data operations is essential yet often strains budgets, necessitating strategic investments.
Ethical Issues: Addressing ethical aspects of data usage is imperative in the data-driven society, yet complex due to shifting societal norms and legal requirements.
Data Integration: Integrating varied data sources is important for holistic insights, yet it's a complex task requiring high-level skills and robust technology.
Data Privacy and security:Achieving a balance between data utility and privacy is crucial in a security-focused world yet challenging due to emerging threats.
Synthetic data: A game-changing catalyst to AI development
Synthetic data is algorithmically generated artificial data which captures patterns, distributions, and relationships of real data. Its unique benefits can train AI models more accurately and facilitate faster turnaround times for AI testing.
Compliant
Data can be generated in a way that safeguards privacy.
Doesn’t require explicit consent, as data can be customized to not include real information.
Scalable
On-demand generation and replication in the desired quantity.
Lacks privacy-associated concerns and restrictions, easily shareable to facilitate scalability.
Unbiased
Can help in creating training datasets free from human bias.
Could be consciously designed to include a diverse range of demographics, scenarios, and characteristics.
Cost Effective
Costs a fraction of the real data acquisition cost.
Has automated labeling, which saves both time and money.
Customizable
Could be combined with real data to enhance datasets.
Allows customization to cover edge cases that could be absent in real data.
Exploring synthetic data classification
Business leaders are increasingly exploring partial and hybrid synthetic data solutions as data regulation gets more stringent.
By data type
Text
Artificially generated text in natural language processing (NLP) applications.
Media
Synthetic video, image, or sound data.
Tabular
Artificially generated data logs or tables. Synthetic datasets can have similar data types, formats, and structures as the original data.
By Input Type
Partially synthetic
Sensitive values of selected features are replaced with synthetic values in the real dataset.
Fully synthetic
Completely artificial data is generated without using any original data.
Hybrid synthetic
Data is generated by combining both real and synthetic data.
Generative AI potentially overcomes the data scarcity conundrum
Startups are utilizing emerging generative artificial intelligence (genAI) techniques to develop synthetic data solutions.
Generative adversarial networks
Typically consisting of a discriminator and a generator, these models involve a generator network that produces synthetic images, while the discriminator network aims to distinguish between real and synthetic images.
Diffusion models
Can produce high-quality images and audio by corrupting training data with Gaussian noise and utilizing a neural network to denoise and generate a new image.
Transformer models
The large language models/transformers learn the underlying probability distribution of the training data and create new data from that learned distribution.
Variational auto-encoders
Unsupervised machine learning models that use encoders to compress and condense the raw data, and decoders to analyze the compressed data to generate a representation of the original dataset.
Autoregressive models
Captures the conditional relationships between features by predicting each feature based on the values of preceding features. Facilitates the replication of complex dependencies and the generation of realistic data.
Synthetic data: key use cases
Businesses are utilizing synthetic data to train AI and ML models, test software, monetize data, and much more.
AI/ML Training
Synthetic data is customizable, cost-effective, and can be fine-tuned for training AI and ML models.
Data Retention
Organizations can retain synthetic data for a longer time without violating any data retention-related regulatory guidelines.
Data Analytics
Synthetic data can be combined with real data to generate hybrid datasets and drive analytical insights.
Simulations
Can be used to create simulations and mimic object relationships in 3D environments.
Benchmarking
Can be used to benchmark various AI and statistical models
Software testing
Synthetic data generators can rapidly generate customized testing data for software testing.
Data Sharing
Organizations can share synthetic data internally and externally across multiple departments and jurisdictions.
Data monetization
Synthetically generated datasets can be shared and monetized with 3rd parties without any regulatory or real-data-related contractual issues.
Barriers to synthetic data adoption loom large
Insight reliability
People may have concerns about using synthetic data to generate insights, as patterns and trends introduced by the synthetic data could be unrelated to the real data.
Businesses may be skeptical about using synthetic data to drive business decisions or develop products.
No Data Outliers
The absence of data outliers may lead to missing out on some valuable insights and unique characteristics.
Reproducing the diversity and variability captured by outliers in the real data can also be a challenge.
Validation and evaluation
Ensuring synthetic data effectively represents real-world distribution requires extensive evaluation and validation.
Measuring the generated data's quality, accuracy, and suitability can be difficult and time-consuming.
Real Data dependency
Synthetic data quality is heavily dependent on the reference real-world dataset. Real data is required to generate partial and hybrid synthetic data.
For fully synthetic data, similar real data is used to verify the quality of the generated dataset.
Privacy Preservation
Preserving statistical properties and relationships while removing personally identifiable information and sensitive data can be challenging.
Regulated data can be crucial to generating consumer insights.
VC investors eye synthetic data for big returns
Industry landscape for select startups
The evolving nature of the synthetic data landscape brings forth emerging startups that address the needs of different sectors.
*Includes manufacturing, aerospace & defense, oil & gas, consumer, energy, infrastructure, agriculture, edTech, business & consumer services, security, sports, government, and IT.
Synthetic data: The artificial backbone of tomorrow’s AI revolution
Synthetic data is expected to accelerate AI development and drive industry-specific solutions, transforming data generation, and privacy concerns.
Anticipated Advancement in synthetic Data
Short term (<2 years)
AI development and analytics projects increasingly utilize synthetic data.
Heavily regulated industries to be early adopters.
Utilized to train algorithms and applications before they are deployed in the real world.
Reduce personal customer data collection, avoiding privacy violation sanctions.
Extensively utilized across industries such as automotive and healthcare, enhancing accessibility and reducing AI model development time.
Medium term (2-5 years)
Play a crucial role in training AI foundation models, attracting significant investments.
Accelerated rate of adoption if data regulation gets stringent.
Incorporated into profitable data and analytics products.
Half of the AI development data will be synthetic rather than real.
Specialized synthetic data vendors emerge to cater to distinct use cases and buyers
Long term (>5 years)
Synthetic data increasingly substitutes real data for AI model training, likely driven by privacy concerns and growing AI adoption.
New generative models for synthetic data gain increased prominence in simulating complex networks and consumer interactions.
Experience substantial growth as the demand for training systems in object and scenario identification rises.
Surpass real data usage for AI model development