To improve operational efficiency, businesses are employing technologically enhanced ways. Artificial intelligence (AI), machine learning (ML), and nanotechnologies are propelling the growth of the synthetic data production solutions market. Organizations are leveraging new and developing technologies to establish their presence in the global market and generate additional income opportunities.
New York, United States, Nov. 29, 2023 (GLOBE NEWSWIRE) — Synthetic data generation generates false statistics that appear genuine. It can replicate the original data’s statistical characteristics, patterns, and relationships in newly generated data instances. It can be used instead of in conjunction with actual data in various situations, particularly when the latter is difficult to obtain, prohibitively expensive, or sensitive. The market for synthetic data production is driven by the rising demand for data privacy, the need for large and diverse datasets for machine learning, and the expanding use of artificial intelligence and data-driven technologies across numerous industries. Industry participants have increased their demand for simulated data in response to the growing popularity of privacy-protection solutions. In addition, the exponential growth of machine learning has transferred the focus to synthetic data. Using AI and machine learning, artificial data can gain access to vast data sets.
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Demand for Data and Compliance Drives the Market Growth
The GDPR and the CCPA emphasize data protection and compliance. These guidelines mandate that businesses collect, process, and secure personal information. Data exposures have brought to light the need for improved privacy and security. Data intrusions cause financial and reputational harm to businesses. Data breaches may result in fines, consumer mistrust, and legal action. Equifax’s 2017 data breach compromised 147 million people’s sensitive information. Equifax ultimately settled numerous lawsuits for USD 700 million. Such incidents highlight the importance of data privacy and the need for businesses to protect sensitive data. Data protection and compliance are gaining importance in the production of synthetic data.
However, data breaches and disclosures of sensitive information cost businesses money. Remediation activities, such as incident response, forensic investigations, notifying affected parties, and enhancing security, require time, money, and resources. These occurrences may hinder the growth and development of the market. According to IBM, the global average cost of data exposure increased by USD 0.11 million to USD 4.35 million in 2022, the highest figure in the report’s history. A 2.6% increase from USD 4.24 million in 2021 to USD 4.35 million in 2022. This includes incident response expenses, legal fees, regulatory fines, consumer notification, reputational harm, and company loss. Small and medium-sized enterprises (SMEs) with limited financial resources may be the most negatively affected.
Adoption of Advanced Technologies such as Artificial Intelligence (AI) and Machine Learning (ML) Creates Tremendous Opportunities
According to Straits Research, “The global synthetic data generation market size was valued at USD 194.5 million in 2022 and is projected to reach USD 3,400 million by 2031, registering a CAGR of 37.3% during the forecast period (2023-2031).” Technology-enhanced methods are assisting businesses in increasing their efficacy. The synthetic data generation solutions market is expanding due to AI, ML, and nanotechnologies. Utilizing new technologies, businesses are expanding globally and increasing revenue. Data management issues, such as privacy, predictive analytics, security, and data-centricity, will necessitate synthetic data. The report on the market for synthetic data generation reveals that today’s AI-powered synthetic data generation algorithms consume actual data, learn its characteristics, correlations, and patterns, and then generate an infinite amount of false synthetic data that matches the statistical properties of the original dataset. Synthetic datasets are scalable, compliant with privacy regulations, and preserve all meaning while removing sensitive data. These developments will bolster the sector of artificial data generation.
North America dominated revenue. End-use companies favor fraud detection, natural language processing, and picture data, making the U.S. and Canada valuable. J.P. Morgan, American Express, Amazon, and Waymo have expanded synthetic data spending. In June 2022, Amazon SageMaker Ground Truth generated tagged synthetic picture data. These industry players choose synthetic data for machine learning training, payment data for fraud detection, and anti-money laundering.
Asia-Pacific is quickly adopting AI. Over one-third of China’s AI market is in banking, retail, and high-tech. ByteDance and Alibaba are known for their customized AI-driven consumer apps in China. Due to the world’s biggest internet user base and the capacity to engage customers creatively to enhance revenue, customer loyalty, and market values, most AI implementations in China have been in consumer-facing industries.
- The market is divided into Tabular Data, Text Data, Image Video Data, and Others based on the data type. The Tabular Data will likely generate the most revenue over the projection period.
- Based on Modeling, the market is divided into Direct Modeling, Agent-Based Modeling. The Agent-Based Modeling segment generated the most revenue and is anticipated to grow significantly during the forecast period.
- Based on the offering, the market is divided into Fully Synthetic, Partially Synthetic, and Hybrid Synthetic Data. The Fully Synthetic Data segment is the highest contributor to the market and is estimated to grow significantly during the forecast period.
- Based on application, the market is divided into Data Protection, Data Sharing, Predictive Analytics, Natural Language Processing, Computer Vision Algorithms, and Others. The Natural Language Processing (NLP) segment owns the largest market share and is anticipated to grow significantly during the forecast period.
- Based on the end-user, the market is divided into BFSI, Healthcare and Life Sciences, Transportation and Logistics, Retail and E-Commerce, Manufacturing, Consumer Electronics, and Others. Healthcare and Life Sciences are the leading market and are estimated to grow significantly during the forecast period.
- Depending on the region, the market is analyzed in North America, Asia-Pacific, Europe, and LAMEA. North America topped the market.
Competitive Landscape of the Global Synthetic Data Generation Market
The key players in the global synthetic data generation market are Mostly AI, CVEDIA Inc., Gretel Labs, Datagen, NVIDIA Corporation, Synthesis AI, Amazon.com, Inc., Microsoft Corporation, IBM Corporation, and Meta.
- In March 2023, Gretel collaborates with Google Cloud to harness the power of synthetic data and accelerate enterprise adoption of safer generative AI.
- In June 2023, NVIDIA H100 GPUs Set the Standard for Generative AI in First MLPerf Benchmark
Global Synthetic Data Generation Market: Segmentation
By Data Type
- Tabular Data
- Text Data
- Image and Video Data
- Others (Audio, Time Series, etc.)
By Modeling Type
- Direct Modeling
- Agent-based Modeling
- Fully Synthetic Data
- Partially Synthetic Data
- Hybrid Synthetic Data
- Data Protection
- Data Sharing
- Predictive Analytics
- Natural Language Processing
- Computer Vision Algorithms
- Healthcare and Life Sciences
- Transportation and Logistics
- IT and Telecommunication
- Retail and E-commerce
- Consumer Electronics
- North America
- Latin America
- The Middle East and Africa
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Originally published at https://www.einpresswire.com/article/671677145/global-synthetic-data-generation-market-size-is-estimated-to-reach-usd-3-400-million-by-2031-growing-at-a-cagr-of-37-3-straits-research