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The Synthetic Data Generation Market size is estimated to grow at a CAGR of 43.13% between 2022 and 2027. The market size is forecast to increase by USD 1,072 million. The growth of the market depends on several factors, including the rising demand for privacy protection, the increase in content creation, and the rise in the adoption of AI and ML technologies. Synthetic data generation refers to the creation of new datasets that simulate real-world data but are artificially generated. It involves creating data points that share similar statistical characteristics and patterns as the original data, without revealing any sensitive or confidential information. This technique is commonly used in various fields, such as ML, data analysis, and privacy-preserving research.
This synthetic data generation market report extensively covers market segmentation by end-user (healthcare and life sciences, retail and e-commerce, transportation and logistics, IT and telecommunication, and BFSI and others), type (agent-based modelling and direct modelling), and geography (North America, Europe, APAC, Middle East and Africa, and South America). It also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the report includes historic market data from 2017 to 2021.
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The rising demand for privacy protection is driving growth in synthetic data generation market. The growing number of data breaches and cyber-attacks are among the main reasons why there is an increased demand for privacy protection in the synthetic data generation market. These incidents have become more frequent and sophisticated, leading to a greater need for businesses to safeguard their sensitive information.
Moreover, the implementation of regulations such as the GDPR has also contributed to the growing demand for privacy protection in the global synthetic data generation market. Organizations are obliged to protect personal data by the GDPR and impose serious penalties for failing to do so. Few factors are expected to increase the demand for synthetic data generation, which will propel the growth of the global synthetic data generation market during the forecast period.
The increase in the deployment of large language models is a key trend in the synthetic data generation market. A growing use of big language models is being observed in the global synthetic data generation market. These advanced models play a crucial role in generating synthetic data for various industries. One example of the deployment of large language models is in the field of healthcare.
However, the deployment of large language models in the global synthetic data generation market provides numerous benefits. It enables the industry to tackle data privacy problems, advance innovation and reduce reliance on expensive real-world data collection. Thus, the abovementioned factors are expected to increase the demand for synthetic data generation, which will propel the growth of the global synthetic data generation market during the forecast period.
The high costs associated with high-end generative models are a major challenge faced in the synthetic data generation market. In view of the growing demand for data in a variety of industries, such as healthcare, financial, and retail, the global synthetic data generation market is set to grow significantly. Generative models are complex algorithms that create artificial data that closely resemble real data.
However, the high costs of implementing generative models often limit the availability of such datasets, hindering progress and innovation in this field. In the financial sector, it is also possible to use synthetic data in risk analysis, fraud detection, and algorithmic trading. And, the high costs associated with generative models make it challenging for smaller retailers with limited budgets to adopt these advanced data generation techniques. Thus, the factors are expected to hinder the growth of the global synthetic data generation market during the forecast period.
The market share growth by the healthcare and life sciences segment will be significant during the forecast period. The healthcare and life sciences segment is one of the fastest-growing industries in the global synthetic data generation market. With the increasing demand for data-driven decision-making and the need for privacy protection, as a valuable solution for healthcare and life science organizations, synthetic data generation has emerged.
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The healthcare and life sciences segments showed a gradual increase in the market share of USD 9.06 million in 2017 and continued to grow by 2021. Moreover, by generating synthetic data that simulate customer profiles and claims records, and in order to avoid affecting the privacy of its policyholders, insurance companies are capable of identifying patterns and anomalies which indicate possible fraud. Thus, a few factors are expected to increase the demand for synthetic data generation in healthcare and life science, which will propel the growth of the market focus during the forecast period.
Agent-based modeling is a technique used to simulate the behavior and interactions of autonomous agents within a given system or environment. It involves creating virtual agents that have their own set of rules, behaviors, and decision-making abilities. Then the agents will be in a simulated environment where they engage with one another and an environment of their own. One of the key advantages of agent-based modeling is its ability to capture the complexity and dynamics of real-world systems. To study the effect of their decision on market dynamics and general stability, agents may be created with a variety of investment strategies and risk appetites. By incorporating real-time market data, these models can be used to analyze market trends, assess risks, and inform investment strategies.
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North America is estimated to contribute 35% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. North America holds a significant position in the global synthetic data generation market. This is due to the increasing demand for high-quality data for various applications such as research and development (R&D), testing, and training in industries such as healthcare, finance, and automotive. The strong tech ecosystem in North America is one of the key factors that has helped to fuel growth in the synthetic data generation market.
Moreover, the region is home to numerous tech giants, startups, and research organizations that are at the forefront of developing innovative data generation solutions. There is an abundance of data scientists, algorithmic experts, and software engineers in the region that have the professional expertise required to generate Synthetic Data. Their ability to understand complex data patterns and create realistic synthetic datasets enables businesses to leverage the power of big data analytics without compromising data privacy and security. The above factors will increase the market growth in this region during the forecast period.
The COVID-19 pandemic had a negative impact on various industries in North America in 2020. The supply chain disruptions caused by the pandemic have negatively affected the availability of resources required for synthetic data generation. In order to obtain real-world data that serves as a basis for the development of artificial data, a number of companies rely on physical sensors and devices. The limitations on travel, shipping delays, and reduced manufacturing activities have resulted in a scarcity of these resources, hindering the production of synthetic data.
The Synthetic Data Generation Market report includes the adoption lifecycle of the market, covering from the innovator’s stage to the laggard’s stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their growth strategies.
Global Synthetic Data Generation Market Customer Landscape
Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.
Alphabet Inc. - The company offers synthetic data generation with a dataflow data generator flex template under the brand Google LLC.
Anyverse SL - The company offers synthetic data generation such as Hyperspectral synthetic data that combines hyperspectral rendering, accurate sensor simulation, built-in assets library, and scalable cloud data production to generate synthetic data.
Facteus Inc - The company offers synthetic data generation such as MIMIC that enhances sensitive data into actionable insights, helps drive innovation, and opens new revenue streams.
The research report also includes detailed analyses of the competitive landscape of the market and information about 15 market companies, including:
Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.
The synthetic data generation market report forecasts market growth by revenue at global, regional & country levels and provides an analysis of the latest trends and growth opportunities from 2017 to 2027.
Synthetic Data Generation Market Scope |
|
Report Coverage |
Details |
Page number |
162 |
Base year |
2022 |
Historic period |
2017-2021 |
Forecast period |
2023-2027 |
Growth momentum & CAGR |
Accelerate at a CAGR of 43.13% |
Market growth 2023-2027 |
USD 1,072 million |
Market structure |
Fragmented |
YoY growth 2022-2023(%) |
41.95 |
Regional analysis |
North America, Europe, APAC, Middle East and Africa, and South America |
Performing market contribution |
North America at 35% |
Key countries |
US, China, Japan, Germany, and the UK |
Competitive landscape |
Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks |
Key companies profiled |
Alphabet Inc., Amazon.com Inc., AnyLogic North America LLC, Anyverse SL, DADoES Inc, Facteus Inc, GenRocket Inc., Gretel Labs Inc., International Business Machines Corp., MDClone Ltd., Microsoft Corp., Neurolaboratories Ltd., NVIDIA Corp., OpenAI L.L.C., Synthesia Ltd., Synthesized Ltd, Syntheticus, Tata Consultancy Services Ltd., YData, and Hazy Ltd. |
Market dynamics |
Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and recovery analysis and future consumer dynamics, and Market condition analysis for the forecast period. |
Customization purview |
If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized. |
We can help! Our analysts can customize this market research report to meet your requirements.
1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation by End-user
7 Market Segmentation by Type
8 Customer Landscape
9 Geographic Landscape
10 Drivers, Challenges, and Trends
11 Vendor Landscape
12 Vendor Analysis
13 Appendix
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