Tech & Trends Digest

Data has always been the fuel of innovation.

In the early days, businesses relied on small, structured datasets - spreadsheets, surveys, and transactional records. Then came the digital boom: unstructured data from social media, sensors, and online behavior flooded the world.

Today, AI demands something even greater: massive, diverse, and precise datasets that capture rare events, edge cases, and human-like complexity. But collecting such data is costly, time-consuming, and often restricted by privacy regulations.

That’s why the next evolution is here: synthetic data.

Synthetic data is artificially generated to mirror the statistical properties of real-world data. It’s created using simulations, statistical methods, or generative AI.

Though not “real,” it behaves like real data, making it a powerful substitute or supplement when actual datasets are scarce, expensive, or locked behind privacy rules.

No wonder analysts predict rapid adoption. Gartner forecasts that by 2026, 75% of businesses will use generative AI to create synthetic customer data.

Synthetic data generation addresses core data science challenges, improving the training of machine learning (ML) models and streamlining AI development.

• Data Scarcity: Synthetic data solves the scarcity of real data for novel use-cases. This is crucial for improving the performance and robustness of models, especially in niche applications with limited real-world data.
• Data Privacy: Synthetic data helps overcome privacy issues by generating training data that mimic real-world statistics without directly corresponding to individual records. This anonymization is critical in fields such as healthcare and financial services, where strict regulations control data privacy and usage.
• Data Quality: Real-world datasets can be imbalanced, which can result in biased outputs from generative models and ML models. Synthetically generated data can augment existing data for larger, more representative datasets. This helps to minimize model bias and improve accuracy.
• Testing: Synthetic test data powers true-to-life simulations for AI software testing and evaluation in safe environments before deployment in real-world scenarios.

Use Cases for Simulation-Based Synthetic Data

• Robotics – Train humanoid robots, AMRs, and industrial manipulators using 3D simulation data for perception and safe interaction.
• Autonomous Vehicles – Augment LiDAR, radar, and camera data with deep learning–generated datasets to optimize perception, planning, and prediction models.
• Retail & Personalization – Create synthetic consumer patterns to enhance recommendation engines while staying privacy-compliant. Impact: +25% recommendation accuracy, higher customer lifetime value.
• Financial Services -  Synthetic data enables the construction of diverse transaction scenarios, including rare fraud patterns, seasonal variations, and emerging attack vectors. This creates balanced datasets with sufficient examples of both legitimate and fraudulent behaviors.
• Manufacturing - Synthetic datasets simulate diverse failure scenarios, operational conditions, and environmental variables to help train machine learning models for predictive maintenance and quality assurance. That results in improved accuracy of predictive models.

1. Quality control. Synthetic data must balance accuracy and privacy. Too much anonymization can reduce quality, while manual checks are time-consuming at scale.
2. Technical complexity. Creating realistic data requires deep expertise. It’s hard to fully capture outliers and anomalies that exist in real-world datasets.
3. Stakeholder confusion. Some business users may distrust synthetic data, while others may overvalue it. Clear communication is needed to set realistic expectations.

The future of enterprise AI hinges on access to high-quality, domain-specific training data. Synthetic data has emerged as a powerful solution, addressing data scarcity, ensuring privacy compliance, and reducing cost barriers. By replicating real-world scenarios without incorporating sensitive information, it enables businesses to accelerate AI adoption while maintaining rigorous privacy standards.

SoftServe has pioneered enterprise-led techniques in synthetic data generation, driving remarkable advancements in Gen AI applications. These innovations not only solve existing challenges but also unlock new capabilities. Looking ahead, synthetic data will be indispensable in shaping the next generation of AI. With competition intensifying and regulations tightening, the decision is clear — businesses must adopt synthetic data now to lead, rather than follow, in this transformation. The future of AI belongs to those who innovate with synthetic data today.

unlocking-the-potential-of-synthetic-data.pdf

https://www.softserveinc.com/uk-ua/blog/ai-powered-synthetic-data-for-robotics

https://www.youtube.com/watch?v=RHYxI3gzXYk

https://info.softserveinc.com/gen-ai-powered-synthetic-data-generation-for-robotics