Synthetic Data Market Size Worth $2.5 Billion By 2030: IndustryARC

The Global Synthetic Data market size is predicted to reach $2.5 billion by 2030, growing at a CAGR of 32% during the forecast period 2024-2030 according to the latest market research report published by IndustryARC. The market is expanding as a result of an uptick in business digital transformation and an increase in the use of cutting-edge technologies like AI and ML, finds IndustryARC in its recent report, titled “Synthetic Data Market – By Data Type (Tabular Data, Text Data, Image and Video Data, Others), By Modeling Type (Direct Modeling, Agent-Based Modeling), By Offering (Fully Synthetic Data, Partially Synthetic Data, Hybrid Synthetic Data), By Application (Data Protection, Data Sharing, Predictive Analysis, NLP, Computer Vision Analysis, Others),  By End Use Industry (BFSI, Healthcare and Life Sciences, Transportation and Logistics, IT and Telecommunication, Retail and E-Commerce, Manufacturing, Consumer Electronics, Others), By Geography - Global Opportunity Analysis & Industry Forecast, 2023-2030.”

In terms of market share, North America will dominate the synthetic data production industry in 2023. The BFSI, retail, healthcare, and other industries are increasing their use of synthetic data production, and it is projected that this will lead to significant growth prospects for this market in North America. But throughout the projected period, Asia-Pacific is anticipated to increase at a faster rate. The expansion of the synthetic data creation market in this area is driven by the increased use of cloud-based services and sophisticated technologies such as AI and ML.

 

Synthetic data is already an integral part of autonomous driving and Computer Vision Al systems. It combines the techniques of the movie and gaming industry (simulation, CG) with the techniques of the generative neural network (GAN, VAE). Synthetic data allows car manufacturers to create realistic datasets and simulations at scale without the need to drive in real-world environments. Synthetic data allows manufacturers to focus on particular objects of interest. Synthetic data also plays an increasingly important role in the market trends of synthetic data generation. Manufacturers are increasingly turning to synthetic data to meet the needs of the In-cabin Driver Safety Monitoring system without relying on real-world driver data. As privacy concerns increase, the use of synthetic data can improve the driver’s safety without compromising drivers’ privacy. Synthetic data also helps automotive manufacturers build strong computer visions systems and gives them an advantage in monitoring driver behavior.

Organizations are increasingly utilizing cutting-edge technologies to enhance operational effectiveness. Artificial Intelligence (AI), Machine Learning (ML), and NanoTechnologies are contributing to the growth of the Synthetic Data Generation Solutions Market. Organizations are taking advantage of new and innovative technologies to differentiate themselves in the global market and increase their revenue potential. Synthetic data will also be a key factor in addressing data management issues in areas such as privacy, predictive analysis, security, and data-centeredness. Today's AI-powered synthetic data generation algorithms are capable of ingesting real data, learning its characteristics, relationships, and patterns in detail, and then generating an infinite amount of entirely artificial, synthetic data that matches the statistical characteristics of the original dataset. Modern, synthetic datasets are highly scalable, privacy-friendly, and contain all original meaning without the need for sensitive information.

The agent-based modeling market accounted for the biggest revenue share in 2023 at 60%. A physical representation of real-world data may be created using agent-based modeling (ABM), and that model can then be used to replicate the data. In the financial industry, agent-based modeling has recently surpassed conventional models in popularity. It has grown to be quite popular for using as a source of business transactions for creating and testing fraud detection systems. Participants in the industry may rely on ABMs to take advantage of network modeling for various types of networks. ABMs are now widely used to simulate customer interactions, inventions, vehicles, and traffic patterns.

Synthetic data can assist in addressing privacy issues, but there are still legal and moral requirements. Organizations must guarantee compliance with applicable data protection and privacy laws as different jurisdictions have different rules regulating the use of synthetic data. In order to uphold ethical norms and prevent unexpected effects, ethical issues, such as potential biases introduced during the generation process or the potential influence on persons or groups, must be properly considered.

Content production is becoming more efficient because to developments in large language models, or LLMs, and other generative ML technology. Complex neural networks called LLMs have the ability to produce text. They support systems like Google's LaMDA (conversational dialogue) and OpenAl's GPT-3 (text), and they served as inspiration for OpenAl's DALL-E and Midjourney (text-to-image). LLMs have been growing in size and sophistication by an average of 10 times year. As a result, Modern Al is capable of producing material on par with human standards, whether it be text, visual, audio, code, data, or multimedia. The Al sector is seeing advancements migrate to downstream activities and multi-modal models as big language models get better. These models have the capacity to accept many input modalities (such as picture, text, and audio) and generate various output modalities.

AI and ML models require substantial and diverse datasets for training. However, acquiring and curating real-world data can be expensive, time-consuming, and sometimes impractical. Synthetic data can fill this gap by providing a cost-effective and efficient way to generate large, diverse datasets that match the characteristics needed for training AI and ML models. Different AI and ML applications have unique data requirements. Synthetic data can be customized to mimic specific data domains, use cases, or industries. This customization allows organizations to create training data that closely aligns with their particular needs, resulting in more accurate and effective models.

● Synthetic Data Market Size and Forecast 

● Synthetic Data Market Trends

● Synthetic Data Market Analysis by Data Type

● CAGR of the market during the forecast period 2024-2030

● Value Chain analysis of key stake holders

● Detailed analysis of market drivers and opportunities during the forecast period

● Synthetic Data Market size estimation and forecast

● Analysis and predictions on end users’ behaviour and upcoming trends 

● Competitive landscape and Vendor market analysis including offerings, developments, and financials

● Comprehensive analysis of challenges and constraints in the Synthetic Data Market

The epidemic has had a significant impact on businesses all around the world, thus it is not surprising that there is an increase in demand for synthetic data production. Companies are turning to AI, ML, computers, and analytics to support contactless operations as governments across the world tighten down. As a result, there is an increase in demand for AI-driven data production models, which is propelling the synthetic data generation market globally. With no infrastructure in place, it can be particularly difficult to handle complicated systems, report correctly, and communicate with coworkers—all issues that the epidemic has presented for businesses. As a result, an increasing number of companies are funding this kind of data production.

There may be an interruption in the availability of synthetic data or data-related services if the war directly impacts businesses or data service providers with operations in Ukraine. The availability and delivery of synthetic data to clients worldwide may suffer briefly as a result. Global supply networks, especially those involving technology and data services, might be disrupted by the violence. Technology infrastructure and data centers, which are frequently reliant on synthetic data solutions, may be indirectly impacted by supply chain disruptions.

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The Synthetic Data Market is fragmented with several global and regional companies operating with expansive manufacturing capabilities and extensive distribution networks. The key companies profiled are listed below:

● Mostly AI, Inc.

● Nvidia Corp.

● Meta

● CVEDIA Inc.

● Amazon.com, Inc.

● Synthesis AI

● IBM Corp.

● Microsoft Corp.

● Datagen, Inc.

● Gretel Labs

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Mr. Venkat Reddy

IndustryARC

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