Synthetic Data Generation Market

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Synthetic Data Generation Market by Offering (Solution/Platform and Services), Data Type (Tabular, Text, Image, and Video), Application (AI/ML Training & Development, Test Data Management), Vertical and Region - Global Forecast to 2028

FREE BROCHURE FREE SAMPLE REPORT
Report Code: TC 8669 May, 2023, by marketsandmarkets.com

Updated on : April 15, 2024

Synthetic Data Generation Market Size

The Synthetic Data Generation Market is expected to have grown from USD 0.3 billion in 2023 to USD 2.1 billion by 2028. during the predicted period, at a CAGR of 45.7%. Synthetic data generation involves creating artificial datasets that mimic real-world data's characteristics and statistical properties. It offers numerous benefits and is driven by various factors. Synthetic data generation provides organizations a cost-effective and time-efficient solution, eliminating the need to collect and label large volumes of real-world data. It enables businesses to overcome privacy and security concerns by generating data that does not contain sensitive information. Synthetic data also enhances data diversity, scalability, and customization, allowing organizations to simulate various scenarios and edge cases. Furthermore, it supports the training and validation of machine learning models, facilitates data sharing and collaboration, and accelerates innovation in healthcare, finance, and cybersecurity sectors. The increasing concerns about data privacy, the need for diverse and representative data, and the demand for efficient model training are some drivers propelling the growth of the synthetic data generation market.

Synthetic Data Generation Market

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Synthetic Data Generation Market

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Market Dynamics

Driver:  Increasing Demand for Data Privacy and Compliance

The rising importance of data privacy and compliance regulations, such as GDPR and CCPA, is driving the need for organizations to handle personal data with utmost caution. Synthetic data generation offers a solution by allowing organizations to generate realistic data while preserving privacy and adhering to regulatory requirements. The growing demand for data privacy and compliance is fueling the synthetic data generation market. Organizations are seeking ways to protect personal data and adhere to stringent privacy regulations. Synthetic data generation provides a solution by allowing the use of artificially generated data that mimics real data while preserving privacy. It helps organizations mitigate risks, ensure compliance, and maintain ethical and transparent data practices. Additionally, synthetic data generation enables access to restricted or scarce data, allowing industries to drive advancements while adhering to privacy regulations and data availability constraints. Overall, the demand for data privacy and compliance is driving the adoption of synthetic data generation as a privacy-preserving solution for various data-intensive activities.

Restraint: Regulatory and Ethical Considerations

While synthetic data can help address privacy concerns, regulatory and ethical considerations still apply. Different jurisdictions have varying regulations regarding the use of synthetic data, and organizations must ensure compliance with relevant data protection and privacy laws. Moreover, ethical considerations, such as potential biases introduced during the generation process or the potential impact on individuals or groups, need to be carefully addressed to maintain ethical standards and avoid unintended consequences.

Opportunity: Increasing deployment of large language models

Advances in large language models, or LLMs, and other generative ML tooling are streamlining content creation. LLMs are complex neural networks that can generate text. They underpin systems like OpenAI's GPT-3 (text) and Google's LaMDA (conversational dialogue) and helped inspire OpenAI's DALL-E and Midjourney (text-to-image). LLMs have been increasing an average of 10x per year in size and sophistication. The result: Modern AI can autonomously generate content—be it text, visual, audio, code, data, or multimedia—on par with human benchmarks.

As large language models improve, the AI industry is witnessing the advances flow to downstream tasks and multi-modal models. These models can take multiple input modalities (e.g., image, text, audio), and produce outputs of different modalities. This is not unlike human cognition; a child reading a picture book uses both the text and illustrations to visualize the story. Language models are progressively becoming the cognitive structure of real-world AI, and enterprises are set for a promising network effect—improvements in large language models tend to flow into downstream tasks and multi-modal models that span text, video, audio, image, code, and beyond.

Challenge: Lack of Maturity in the Market

The synthetic data generation market is still in its early stages of development and is expected to grow significantly in the coming years. This is due to the advantages of synthetic data over real data, which include privacy, cost, accuracy, and flexibility. However, a number of challenges need to be addressed before the market can reach its full potential, such as the lack of standards, trust, and awareness. Some steps that can be taken to address these challenges include developing standards for synthetic data generation, building trust in synthetic data, and increasing awareness of the benefits of synthetic data

Market Ecosystem

Synthetic Data Generation Market

By data type, text data to segment to record the highest growth rate during the forecast period

By data type, text data segment is expected to have the highest growth rate during the forecast period. The increasing demand for artificial intelligence (AI) and machine learning (ML) applications requires large amounts of data to train and develop models, further driving the text data segment.

Among applications, the Test data management segment has the highest market share during the forecast period.

Under the applications segment, Test data management segment is expected to have the highest market share during the forecast period. The need for high-quality, diverse, and representative data for testing and validation purposes will drive the segment. Businesses can enhance the effectiveness and efficiency of their testing processes using synthetic data leading to improved product quality, faster time-to-market, and reduced costs associated with traditional test data management approaches.

Synthetic Data Generation Market Size, and Share

Among regions, North America to hold the highest market share during the forecast period

North America is a hub for technological advancements, focusing strongly on AI, machine learning, and data-driven innovations. This region boasts a rich ecosystem of research institutions, tech companies, and startups, driving the demand for high-quality synthetic data for training AI models and conducting experiments. Additionally, the presence of key players in the region further drives the synthetic data generation market in this region.

Market Players

The report includes the study of key players offering synthetic data generation solutions and services. It profiles major vendors in the global synthetic data generation market. The major vendors Microsoft (US), Google (US), IBM (US), AWS (US), NVIDIA (US), OpenAI (US), Informatica (US), Broadcom (US),  Sogeti (France), Mphasis (India), Databricks (US), MOSTLY AI (Austria), Tonic (US), MDClone (Israel), TCS (India), Hazy (UK), Synthesia (UK), Synthesized (UK), Facteus (US), Anyverse (Spain), Neurolabs (Scotland), Rendered.ai (US), Gretel (US), OneView  (Israel), GenRocket  (US), YData (US), CVEDIA (UK), Syntheticus (Switzerland), AnyLogic (US), Bifrost AI (US), Anonos (US). These players have adopted various strategies to grow in the global market.

The study includes an in-depth competitive analysis of these key players in the synthetic data generation market with their company profiles, recent developments, and key market strategies.

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Scope of the Report

Report Metrics

Details

Market size available for years

2019 – 2028

Base year considered

2022

Forecast period

2023-2028

Forecast units

Value (USD Billion)

Segments Covered

Offering (Solution/ Platform and Services), Data Type (Tabular, Text, Image and Video, Others), Application ( AI/ML Training and Development, Test Data Management, Data analytics & visualization, Enterprise Data Sharing,  Others), Vertical (Banking, Financial Services, and Insurance, Healthcare & Life sciences, Automotive & Transportation, Government & Defense, IT and ITeS, Manufacturing, Other Verticals) and Region

Regions covered

North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Companies covered

Microsoft (US), Google (US), IBM (US), AWS (US), NVIDIA (US), OpenAI (US), Informatica (US), Broadcom (US),  Sogeti (France), Mphasis (India), Databricks (US), MOSTLY AI (Austria), Tonic (US), MDClone (Israel) TCS (India), Hazy (UK), Synthesia (UK), Synthesized (UK), Facteus (US), Anyverse (Spain), Neurolabs (Scotland), Rendered.ai (US), Gretel (US), OneView  (Israel), GenRocket  (US), YData (US), CVEDIA (UK), Syntheticus (Switzerland), AnyLogic (US), Bifrost AI (US), Anonos (US)

This research report categorizes the synthetic data generation market to forecast revenue and analyze trends in each of the following submarkets:

Based on Offering:
  • Solution/Platform
  • Services
Based on Data Type:
  • Tabular Data
  • Text data
  • Image and Video Data
  • Others
Based on Application:
  • AI/ML Training and Development
  • Test Data Management
  • Data analytics and visualization
  • Enterprise Data Sharing
  • Others
Based on Vertical:
  • BFSI
  • Healthcare & Life sciences
  • Retail & E-commerce
  • Automotive & Transportation
  • Government & Defense
  • IT and ITeS
  • Manufacturing
  • Other Verticals
Based on Region:
  • North America
    • US
    • Canada
  • Europe
    • UK
    • Germany
    • Italy
    • Spain
    • France
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • ANZ
    • Rest of APAC
  • Middle East & Africa
    • UAE
    • KSA
    • South Africa
    • Rest of MEA
  • Latin America
    • Brazil
    • Mexico
    • Rest of Latin America

Recent Developments:

  • In May 2023, Databricks acquired Okera, a data governance platform with a focus on AI. the acquisition will enable Databricks to expose additional APIs that its own data governance partners will be able to use to provide solutions to their customers.
  • In January 2023, Microsoft entered into a multi-billion-dollar partnership with OpenAI to accelerate the development of AI technology. The partnership aims to democratize AI and make it accessible to everyone. The partnership has already yielded impressive results, including the development of GPT-3
  • In December 2022, AWS and Stability AI collaborated to make its open-source tools and models. Stability AI, a community-driven, open-source artificial intelligence (AI) company, selected AWS as its preferred cloud provider to build and scale its AI models for image, language, audio, video, and 3D content generation. Stability AI will use Amazon SageMaker (AWS's end-to-end machine learning service), as well as AWS's proven computing infrastructure and storage, to accelerate its work on open-source generative AI models.
  • In October 2022, Microsoft partnered with Informatica, an enterprise cloud data management leader, announcing its inclusion as an initial partner of the Microsoft Intelligent Data Platform Partner Ecosystem. Microsoft announced the launch of this ecosystem during its Microsoft Ignite 2022. This initiative represents both companies' investment toward helping enterprises truly operationalize AI with trusted and governed data.
  • In June 2022, Tonic announced an integration with Snowflake, the Data Cloud company. The new integration will enable joint Tonic and Snowflake customers to build applications with realistic, de-identified data in the Snowflake Data Cloud. Joint customers can also tokenize data at scale and ensure regulatory compliance.

Frequently Asked Questions (FAQ):

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TABLE OF CONTENTS
 
1 INTRODUCTION (Page No. - 40)
    1.1 STUDY OBJECTIVES 
    1.2 MARKET DEFINITION 
           1.2.1 INCLUSIONS & EXCLUSIONS
    1.3 STUDY SCOPE 
           1.3.1 MARKET SEGMENTATION
           1.3.2 REGIONS COVERED
    1.4 YEARS CONSIDERED 
    1.5 CURRENCY CONSIDERED 
           TABLE 1 USD EXCHANGE RATE, 2019–2022
    1.6 STAKEHOLDERS 
 
2 RESEARCH METHODOLOGY (Page No. - 45)
    2.1 RESEARCH DATA 
           FIGURE 1 RESEARCH DESIGN
           2.1.1 SECONDARY DATA
           2.1.2 PRIMARY DATA
                    TABLE 2 KEY PARTICIPANTS OF PRIMARY INTERVIEWS
                    2.1.2.1 Breakup of primary profiles
                    2.1.2.2 Key industry insights
    2.2 DATA TRIANGULATION 
           FIGURE 2 DATA TRIANGULATION
    2.3 MARKET SIZE ESTIMATION 
           FIGURE 3 SYNTHETIC DATA GENERATION MARKET: TOP-DOWN AND BOTTOM-UP APPROACHES
           2.3.1 TOP-DOWN APPROACH
           2.3.2 BOTTOM-UP APPROACH
                    FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY, APPROACH 1 (SUPPLY-SIDE): REVENUE FROM SOLUTIONS/SERVICES OF MARKET
                    FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY, APPROACH 2, BOTTOM-UP  (SUPPLY-SIDE): COLLECTIVE REVENUE FROM ALL SOLUTIONS/SERVICES OF MARKET
                    FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY, APPROACH 3, BOTTOM-UP  (SUPPLY-SIDE): COLLECTIVE REVENUE FROM ALL SOLUTIONS/SERVICES OF MARKET
                    FIGURE 7 MARKET SIZE ESTIMATION METHODOLOGY - APPROACH 4, BOTTOM-UP (DEMAND-SIDE): SHARE OF SYNTHETIC DATA GENERATION THROUGH OVERALL  MARKET SPENDING
    2.4 MARKET FORECAST 
           TABLE 3 FACTOR ANALYSIS
    2.5 RESEARCH ASSUMPTIONS 
    2.6 LIMITATIONS AND RISK ASSESSMENT 
    2.7 IMPACT OF RECESSION ON SYNTHETIC DATA GENERATION MARKET 
 
3 EXECUTIVE SUMMARY (Page No. - 58)
    FIGURE 8 ASIA PACIFIC TO ACHIEVE HIGHEST GROWTH DURING FORECAST PERIOD 
 
4 PREMIUM INSIGHTS (Page No. - 60)
    4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN SYNTHETIC DATA GENERATION MARKET 
           FIGURE 9 INCREASING INVESTMENTS IN AI TO DRIVE MARKET GROWTH
    4.2 MARKET:  BY KEY VERTICAL & REGION 
           FIGURE 10 BFSI SEGMENT AND NORTH AMERICA TO ACCOUNT FOR SIGNIFICANT  SHARE IN 2023
           FIGURE 11 SOLUTIONS SEGMENT TO ACCOUNT FOR LARGER SHARE IN 2023
    4.3 MARKET, BY REGION 
           FIGURE 12 NORTH AMERICA TO ACCOUNT FOR LARGEST SHARE IN 2023
 
5 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. - 62)
    5.1 INTRODUCTION 
    5.2 MARKET DYNAMICS 
           FIGURE 13 SYNTHETIC DATA GENERATION MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
           5.2.1 DRIVERS
                    5.2.1.1 Rising adoption of AI and machine learning technologies
                    5.2.1.2 Increasing need for data privacy and compliance
                    5.2.1.3 Rise in investments in AI
                    5.2.1.4 Increase in content creation
                               FIGURE 14 SYNTHETIC DATA TO ACCOUNT FOR LARGER DATA VOLUME BY 2030
           5.2.2 RESTRAINTS
                    5.2.2.1 Regulatory and ethical considerations
                    5.2.2.2 Issues related to achieving quality data and realism
           5.2.3 OPPORTUNITIES
                    5.2.3.1 Increasing deployment of large language models
                    5.2.3.2 Growing interest of enterprises in commercializing synthetic images
                    5.2.3.3 Robust advancements in machine learning and computing innovation
           5.2.4 CHALLENGES
                    5.2.4.1 Market immaturity
                    5.2.4.2 Lack of skilled workforce
                    5.2.4.3 High costs associated with high-end generative models
    5.3 ETHICS AND IMPLICATIONS OF SYNTHETIC DATA GENERATION 
           5.3.1 PRIVACY PROTECTION
           5.3.2 FAIRNESS
           5.3.3 DATA OWNERSHIP AND CONSENT
           5.3.4 UNINTENDED CONSEQUENCES
           5.3.5 ACCOUNTABILITY AND TRANSPARENCY
           5.3.6 FAIR REPRESENTATION
           5.3.7 REGULATORY COMPLIANCE
    5.4 ADVENT OF SYNTHETIC DATA GENERATION 
    5.5 HISTORY OF SYNTHETIC DATA GENERATION 
           FIGURE 15 HISTORY OF SYNTHETIC DATA GENERATION
    5.6 TIMELINE OF ADVANCEMENTS IN SYNTHETIC DATA GENERATION MARKET 
    5.7 ECOSYSTEM ANALYSIS 
           FIGURE 16 ECOSYSTEM ANALYSIS
           5.7.1 SYNTHETIC DATA GENERATION TECHNOLOGY PROVIDERS
           5.7.2 SYNTHETIC DATA GENERATION CLOUD PLATFORM PROVIDERS
           5.7.3 SYNTHETIC DATA GENERATION CLOUD END USERS
           5.7.4 SYNTHETIC DATA GENERATION CLOUD REGULATORS
    5.8 SYNTHETIC DATA GENERATION TECHNIQUES AND METHODS BASED ON DATA TYPES 
           5.8.1 TABULAR
                    5.8.1.1 Rule-based Methods
                    5.8.1.2 Data Augmentation
                    5.8.1.3 Generative Adversarial Networks (GANs)
                    5.8.1.4 Variational Autoencoders (VAEs)
                    5.8.1.5 Bayesian Networks
           5.8.2 TEXT
                    5.8.2.1 Markov Chains
                    5.8.2.2 Neural Networks (RNNs)
                    5.8.2.3 Transformer Models
                    5.8.2.4 Language Models
                    5.8.2.5 Variational Autoencoders
           5.8.3 IMAGES AND VIDEOS
                    5.8.3.1 Generative Adversarial Networks (GANs)
                    5.8.3.2 Variational Autoencoders (VAEs)
                    5.8.3.3 Conditional GANs
                    5.8.3.4 Image and Video Synthesis with Neural Networks
                    5.8.3.5 Style Transfer and Data Augmentation
           5.8.4 TIME SERIES & TRANSACTIONAL DATA
                    5.8.4.1 Autoregressive Integrated Moving Average (ARIMA)
                    5.8.4.2 Long Short-Term Memory (LSTM) Networks
                    5.8.4.3 Hidden Markov Models (HMMs)
                    5.8.4.4 Sequence Generative Adversarial Networks (SeqGANs)
                    5.8.4.5 Gaussian Mixture Models (GMMs)
                    5.8.4.6 Synthetic Oversampling and Undersampling
    5.9 CASE STUDY ANALYSIS 
           5.9.1 CASE STUDY 1: MOSTLY AI HELPED RETAIL BANK SHORTEN DEVELOPMENT OF SPRINTS BY SEVERAL DAYS
           5.9.2 CASE STUDY 2: SWEDISH GOVERNMENT INCORPORATED ARTIFICIAL INTELLIGENCE INTO DAILY OPERATIONS
           5.9.3 CASE STUDY 3: EVERLYWELL GAINED 5X DEPLOYMENT VELOCITY WITH SUPPORT FROM TONIC API
           5.9.4 CASE STUDY 4: VODAFONE ADOPTED HAZY SYNTHETIC DATA TO QUICKLY AND ACCURATELY PREDICT CHURN
           5.9.5 CASE STUDY 5: SCALE AI HELPED KALEIDO AI’S ML TEAM TO IMPROVE MODEL PERFORMANCE ON BUSINESS-CRITICAL EDGE CASES
    5.10 SUPPLY CHAIN ANALYSIS 
           FIGURE 17 SUPPLY CHAIN ANALYSIS
    5.11 REGULATORY LANDSCAPE 
           5.11.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 4 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 5 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 6 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 7 MIDDLE EAST & AFRICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 8 LATIN AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
           5.11.2 PAYMENT CARD INDUSTRY DATA SECURITY STANDARD
           5.11.3 GRAMM-LEACH-BLILEY ACT
           5.11.4 HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT
           5.11.5 GENERAL DATA PROTECTION REGULATION
           5.11.6 PERSONAL INFORMATION PROTECTION AND ELECTRONIC DOCUMENTS ACT
           5.11.7 INFORMATION SECURITY TECHNOLOGY: PERSONAL INFORMATION SECURITY SPECIFICATION GB/T 35273-2017
           5.11.8 SECURE INDIA NATIONAL DIGITAL COMMUNICATIONS POLICY, 2018
           5.11.9 GENERAL DATA PROTECTION LAW
                    5.11.10 LAW NO 13 OF 2016 ON PROTECTING PERSONAL DATA
                    5.11.11 NIST SPECIAL PUBLICATION 800-144, GUIDELINES ON SECURITY AND PRIVACY IN PUBLIC CLOUD COMPUTING
    5.12 PATENT ANALYSIS 
           5.12.1 METHODOLOGY
           5.12.2 PATENT APPLICATIONS
                    FIGURE 18 NUMBER OF PATENTS GRANTED ANNUALLY, 2019–2022
           5.12.3 TOP 15 PATENT APPLICANTS IN LAST 10 YEARS
                    FIGURE 19 TOP 15 PATENT APPLICANTS IN LAST 10 YEARS
           5.12.4 TOP 15 PATENT OWNERS IN LAST 10 YEARS
                    FIGURE 20 TOP 15 PATENT OWNERS IN LAST 10 YEARS
    5.13 KEY CONFERENCES & EVENTS 
                    TABLE 9 KEY CONFERENCES & EVENTS, 2023–2024
    5.14 PRICING ANALYSIS 
                    TABLE 10 AVERAGE SELLING PRICE ANALYSIS
    5.15 PORTER’S FIVE FORCES ANALYSIS 
                    TABLE 11 IMPACT OF PORTER’S FORCES ON SYNTHETIC DATA GENERATION MARKET
           5.15.1 THREAT FROM NEW ENTRANTS
           5.15.2 THREAT FROM SUBSTITUTES
           5.15.3 BARGAINING POWER OF SUPPLIERS
           5.15.4 BARGAINING POWER OF BUYERS
           5.15.5 INTENSITY OF COMPETITIVE RIVALRY
    5.16 KEY STAKEHOLDERS AND BUYING CRITERIA 
           5.16.1 KEY STAKEHOLDERS IN BUYING PROCESS
                    FIGURE 21 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE APPLICATIONS
                    TABLE 12 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE APPLICATIONS
           5.16.2 BUYING CRITERIA
                    FIGURE 22 KEY BUYING CRITERIA FOR TOP THREE APPLICATIONS
                    TABLE 13 KEY BUYING CRITERIA FOR TOP THREE APPLICATIONS
    5.17 TRENDS/DISRUPTIONS IMPACTING BUYERS/CLIENTS OF SYNTHETIC DATA GENERATION MARKET 
                    FIGURE 23 TRENDS/DISRUPTIONS IMPACTING BUYERS/CLIENTS
           5.17.1 BUSINESS MODELS IN MARKET
                    5.17.1.1 Software licensing
                    5.17.1.2 Data-as-a-Service (DaaS)
                    5.17.1.3 Custom development
                    5.17.1.4 Consulting and professional services
                    5.17.1.5 Partnerships and collaborations
                    5.17.1.6 Data monetization
                    5.17.1.7 Integration with existing platforms
                    5.17.1.8 Research and development
                    5.17.1.9 Freemium model
                               5.17.1.10 Open source
 
6 SYNTHETIC DATA GENERATION MARKET, BY OFFERING (Page No. - 95)
    6.1 INTRODUCTION 
           6.1.1 OFFERINGS: MARKET DRIVERS
                    FIGURE 24 SERVICES SEGMENT TO REGISTER HIGHER CAGR DURING FORECAST PERIOD
                    TABLE 14 MARKET, BY OFFERING, 2019–2022 (USD MILLION)
                    TABLE 15 MARKET, BY OFFERING, 2023–2028 (USD MILLION)
    6.2 SOLUTIONS 
           TABLE 16 SOLUTIONS: MARKET, BY REGION,  2019–2022 (USD MILLION)
           TABLE 17 SOLUTIONS: MARKET, BY REGION,  2023–2028 (USD MILLION)
    6.3 SERVICES 
           FIGURE 25 MANAGED SERVICES SEGMENT TO REGISTER HIGHER GROWTH DURING FORECAST PERIOD
           TABLE 18 SERVICES: MARKET, BY REGION,  2019–2022 (USD MILLION)
           TABLE 19 SERVICES: MARKET, BY REGION,  2023–2028 (USD MILLION)
           TABLE 20 MARKET, BY SERVICE, 2019–2022 (USD MILLION)
           TABLE 21 MARKET, BY SERVICE, 2023–2028 (USD MILLION)
           6.3.1 PROFESSIONAL SERVICES
                    6.3.1.1 Rising demand for specialized expertise in synthetic data generation to boost market growth
                               FIGURE 26 SYSTEM INTEGRATION AND IMPLEMENTATION SEGMENT TO ACHIEVE HIGHEST GROWTH DURING FORECAST PERIOD
                               TABLE 22 SYNTHETIC DATA GENERATION MARKET, BY PROFESSIONAL SERVICE,  2019–2022 (USD MILLION)
                               TABLE 23 MARKET, BY PROFESSIONAL SERVICE,  2023–2028 (USD MILLION)
                               TABLE 24 PROFESSIONAL SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)
                               TABLE 25 PROFESSIONAL SERVICES: MARKET, BY REGION,  2023–2028 (USD MILLION)
                               6.3.1.1.1 Training and consulting
                               TABLE 26 TRAINING AND CONSULTING: MARKET, BY REGION,  2019–2022 (USD MILLION)
                               TABLE 27 TRAINING AND CONSULTING: MARKET, BY REGION,  2023–2028 (USD MILLION)
                               6.3.1.1.2 System integration and implementation
                               TABLE 28 SYSTEM INTEGRATION AND IMPLEMENTATION: MARKET, BY REGION, 2019–2022 (USD MILLION)
                               TABLE 29 SYSTEM INTEGRATION AND IMPLEMENTATION: MARKET,  BY REGION, 2023–2028 (USD MILLION)
                               6.3.1.1.3 Support and maintenance
                               TABLE 30 SUPPORT AND MAINTENANCE: MARKET, BY REGION, 2019–2022 (USD MILLION)
                               TABLE 31 SUPPORT AND MAINTENANCE: MARKET, BY REGION,  2023–2028 (USD MILLION)
           6.3.2 MANAGED SERVICES
                    6.3.2.1 Need for end-to-end management of synthetic data generation to drive market for managed services
                               TABLE 32 MANAGED SERVICES: MARKET, BY REGION,  2019–2022 (USD MILLION)
                               TABLE 33 MANAGED SERVICES: MARKET, BY REGION,  2023–2028 (USD MILLION)
 
7 SYNTHETIC DATA GENERATION MARKET, BY DATA TYPE (Page No. - 106)
    7.1 INTRODUCTION 
           7.1.1 DATA TYPES: MARKET DRIVERS
                    FIGURE 27 TEXT SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
                    TABLE 34 MARKET, BY DATA TYPE, 2019–2022 (USD MILLION)
                    TABLE 35 MARKET, BY DATA TYPE, 2023–2028 (USD MILLION)
    7.2 TABULAR 
           7.2.1 DEMAND FOR PRIVACY PRESERVATION TO DRIVE GENERATION OF TABULAR DATA
                    TABLE 36 TABULAR: MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 37 TABULAR: MARKET, BY REGION,  2023–2028 (USD MILLION)
    7.3 TEXT 
           7.3.1 NEED TO CREATE LABELED TRAINING DATASETS FOR NATURAL LANGUAGE PROCESSING TASKS TO DRIVE MARKET GROWTH
                    TABLE 38 TEXT: MARKET, BY REGION, 2019–2022 (USD MILLION)
                    TABLE 39 TEXT: MARKET, BY REGION, 2023–2028 (USD MILLION)
    7.4 IMAGES AND VIDEOS 
           7.4.1 DEMAND FOR COMPUTER VISION TRAINING, IMAGE RECOGNITION, AND VIDEO ANALYSIS MODELS TO DRIVE MARKET GROWTH
                    TABLE 40 IMAGES AND VIDEOS: MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 41 IMAGES AND VIDEOS: MARKET, BY REGION,  2023–2028 (USD MILLION)
    7.5 OTHER DATA TYPES 
           TABLE 42 OTHER DATA TYPES: MARKET, BY REGION,  2019–2022 (USD MILLION)
           TABLE 43 OTHER DATA TYPES: MARKET, BY REGION,  2023–2028 (USD MILLION)
 
8 SYNTHETIC DATA GENERATION MARKET, BY APPLICATION (Page No. - 113)
    8.1 INTRODUCTION 
           8.1.1 APPLICATIONS: MARKET DRIVERS
                    FIGURE 28 AI/ML TRAINING AND DEVELOPMENT SEGMENT TO ACCOUNT FOR LARGEST SHARE IN 2023
                    TABLE 44 MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
                    TABLE 45 MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
    8.2 AI/ML TRAINING AND DEVELOPMENT 
           8.2.1 NEED FOR SCALABLE AND DIVERSE DATASETS FOR TRAINING MODELS TO DRIVE ADOPTION OF SYNTHETIC DATA GENERATION
                    TABLE 46 AI/ML TRAINING AND DEVELOPMENT: MARKET,  BY REGION, 2019–2022 (USD MILLION)
                    TABLE 47 AI/ML TRAINING AND DEVELOPMENT: MARKET,  BY REGION, 2023–2028 (USD MILLION)
    8.3 TEST DATA MANAGEMENT 
           8.3.1 NEED TO ENHANCE EFFICIENCY OF SOFTWARE TESTING APPLICATIONS TO PROPEL MARKET
                    TABLE 48 TEST DATA MANAGEMENT: SYNTHETIC DATA GENERATION MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 49 TEST DATA MANAGEMENT: MARKET, BY REGION,  2023–2028 (USD MILLION)
    8.4 DATA ANALYTICS AND VISUALIZATION 
           8.4.1 GROWING NEED TO SAFEGUARD DATA PRIVACY TO BOOST SYNTHETIC DATA GENERATION FOR DATA ANALYTICS AND VISUALIZATION
                    TABLE 50 DATA ANALYTICS AND VISUALIZATION: MARKET,  BY REGION, 2019–2022 (USD MILLION)
                    TABLE 51 DATA ANALYTICS AND VISUALIZATION: MARKET,  BY REGION, 2023–2028 (USD MILLION)
    8.5 ENTERPRISE DATA SHARING 
           8.5.1 NEED TO LEVERAGE POWER OF SHARED DATA TO ENCOURAGE PLAYERS TO ADOPT ENTERPRISE DATA SHARING AND DETECTION
                    TABLE 52 ENTERPRISE DATA SHARING AND RETENTION: MARKET,  BY REGION, 2019–2022 (USD MILLION)
                    TABLE 53 ENTERPRISE DATA SHARING AND RETENTION: MARKET,  BY REGION, 2023–2028 (USD MILLION)
    8.6 OTHER APPLICATIONS 
           TABLE 54 OTHER APPLICATIONS: MARKET, BY REGION,  2019–2022 (USD MILLION)
           TABLE 55 OTHER APPLICATIONS: MARKET, BY REGION,  2023–2028 (USD MILLION)
 
9 SYNTHETIC DATA GENERATION MARKET, BY VERTICAL (Page No. - 121)
    9.1 INTRODUCTION 
           9.1.1 VERTICALS: MARKET DRIVERS
                    FIGURE 29 HEALTHCARE AND LIFE SCIENCES SEGMENT TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD
                    TABLE 56 MARKET, BY VERTICAL, 2019–2022 (USD MILLION)
                    TABLE 57 MARKET, BY VERTICAL, 2023–2028 (USD MILLION)
    9.2 BANKING, FINANCIAL SERVICES, AND INSURANCE (BFSI) 
           9.2.1 NEED FOR REGULATED LANDSCAPE WITH STRINGENT DATA PRIVACY REQUIREMENTS TO DRIVE MARKET GROWTH
           9.2.2 BFSI: MARKET USE CASES
                    TABLE 58 BFSI: MARKET, BY REGION, 2019–2022 (USD MILLION)
                    TABLE 59 BFSI: MARKET, BY REGION, 2023–2028 (USD MILLION)
    9.3 HEALTHCARE AND LIFE SCIENCES 
           9.3.1 DEMAND FOR PRIVACY IN HEALTHCARE SECTOR TO BOOST ADOPTION OF SYNTHETIC DATA GENERATION
           9.3.2 HEALTHCARE AND LIFE SCIENCES: MARKET USE CASES
                    TABLE 60 HEALTHCARE AND LIFE SCIENCES: MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 61 HEALTHCARE AND LIFE SCIENCES: SYNTHETIC DATA GENERATION MARKET, BY REGION,  2023–2028 (USD MILLION)
    9.4 RETAIL AND E-COMMERCE 
           9.4.1 SYNTHETIC DATA GENERATION TO ENABLE RETAIL AND E-COMMERCE COMPANIES TO CREATE REPRESENTATIVE DATASETS
           9.4.2 RETAIL AND E-COMMERCE: MARKET USE CASES
                    TABLE 62 RETAIL AND E-COMMERCE: MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 63 RETAIL AND E-COMMERCE: MARKET, BY REGION,  2023–2028 (USD MILLION)
    9.5 AUTOMOTIVE AND TRANSPORTATION 
           9.5.1 DEMAND FOR OPTIMIZED VEHICLE PERFORMANCE TO PROPEL ADOPTION OF SYNTHETIC DATA GENERATION
           9.5.2 AUTOMOTIVE AND TRANSPORTATION: MARKET USE CASES
                    TABLE 64 AUTOMOTIVE AND TRANSPORTATION: MARKET, BY REGION, 2019–2022 (USD MILLION)
                    TABLE 65 AUTOMOTIVE AND TRANSPORTATION: MARKET,  BY REGION, 2023–2028 (USD MILLION)
    9.6 GOVERNMENT AND DEFENSE 
           9.6.1 RISING NEED TO PROTECT SENSITIVE INFORMATION TO DRIVE USE OF SYNTHETIC DATA GENERATION IN GOVERNMENT AND DEFENSE SEGMENT
           9.6.2 GOVERNMENT AND DEFENSE: SYNTHETIC DATA GENERATION MARKET USE CASES
                    TABLE 66 GOVERNMENT AND DEFENSE: MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 67 GOVERNMENT AND DEFENSE: MARKET, BY REGION,  2023–2028 (USD MILLION)
    9.7 IT AND ITES 
           9.7.1 NEED FOR DATA PRIVACY AND SECURITY IN IT AND ITES SECTOR TO ENABLE PLAYERS TO UNDERSTAND VALUE OF SYNTHETIC DATA GENERATION
           9.7.2 IT AND ITES: MARKET USE CASES
                    TABLE 68 IT AND ITES: MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 69 IT AND ITES: MARKET, BY REGION,  2023–2028 (USD MILLION)
    9.8 MANUFACTURING 
           9.8.1 GROWING NEED TO IMPROVE PRODUCT DESIGN AND OPTIMIZATION TO PROPEL MARKET GROWTH
           9.8.2 MANUFACTURING: MARKET USE CASES
                    TABLE 70 MANUFACTURING: MARKET, BY REGION,  2019–2022 (USD MILLION)
                    TABLE 71 MANUFACTURING: MARKET, BY REGION,  2023–2028 (USD MILLION)
    9.9 OTHER VERTICALS 
           TABLE 72 OTHER VERTICALS: MARKET, BY REGION,  2019–2022 (USD MILLION)
           TABLE 73 OTHER VERTICALS: MARKET, BY REGION,  2023–2028 (USD MILLION)
 
10 SYNTHETIC DATA GENERATION MARKET, BY REGION (Page No. - 137)
     10.1 INTRODUCTION 
               FIGURE 30 ASIA PACIFIC TO REGISTER HIGHEST GROWTH DURING FORECAST PERIOD
               TABLE 74 MARKET, BY REGION, 2019–2022 (USD MILLION)
               TABLE 75 MARKET, BY REGION, 2023–2028 (USD MILLION)
     10.2 NORTH AMERICA 
             10.2.1 NORTH AMERICA: MARKET DRIVERS
             10.2.2 NORTH AMERICA: RECESSION IMPACT
                       FIGURE 31 NORTH AMERICA: MARKET SNAPSHOT
                       TABLE 76 NORTH AMERICA: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 77 NORTH AMERICA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
                       TABLE 78 NORTH AMERICA: MARKET, BY DATA TYPE,  2019–2022 (USD MILLION)
                       TABLE 79 NORTH AMERICA: MARKET, BY DATA TYPE,  2023–2028 (USD MILLION)
                       TABLE 80 NORTH AMERICA: MARKET, BY SERVICE,  2019–2022 (USD MILLION)
                       TABLE 81 NORTH AMERICA: MARKET, BY SERVICE,  2023–2028 (USD MILLION)
                       TABLE 82 NORTH AMERICA: MARKET, BY PROFESSIONAL SERVICE,  2019–2022 (USD MILLION)
                       TABLE 83 NORTH AMERICA: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)
                       TABLE 84 NORTH AMERICA: MARKET, BY APPLICATION,  2019–2022 (USD MILLION)
                       TABLE 85 NORTH AMERICA: MARKET, BY APPLICATION,  2023–2028 (USD MILLION)
                       TABLE 86 NORTH AMERICA: MARKET, BY VERTICAL,  2019–2022 (USD MILLION)
                       TABLE 87 NORTH AMERICA: MARKET, BY VERTICAL,  2023–2028 (USD MILLION)
                       TABLE 88 NORTH AMERICA: MARKET, BY COUNTRY,  2019–2022 (USD MILLION)
                       TABLE 89 NORTH AMERICA: MARKET, BY COUNTRY,  2023–2028 (USD MILLION)
             10.2.3 US
                       10.2.3.1 Technological advancements and widespread application of synthetic data generation to drive market
                                   TABLE 90 US: SYNTHETIC DATA GENERATION MARKET, BY OFFERING, 2019–2022 (USD MILLION)
                                   TABLE 91 US: MARKET, BY OFFERING, 2023–2028 (USD MILLION)
             10.2.4 CANADA
                       10.2.4.1 Need to enhance productivity and improve customer satisfaction to drive market growth
                                   TABLE 92 CANADA: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 93 CANADA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
     10.3 EUROPE 
             10.3.1 EUROPE: MARKET DRIVERS
             10.3.2 EUROPE: RECESSION IMPACT
                       TABLE 94 EUROPE: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 95 EUROPE: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
                       TABLE 96 EUROPE: MARKET, BY DATA TYPE,  2019–2022 (USD MILLION)
                       TABLE 97 EUROPE: MARKET, BY DATA TYPE,  2023–2028 (USD MILLION)
                       TABLE 98 EUROPE: MARKET, BY SERVICE,  2019–2022 (USD MILLION)
                       TABLE 99 EUROPE: MARKET, BY SERVICE,  2023–2028 (USD MILLION)
                       TABLE 100 EUROPE: MARKET, BY PROFESSIONAL SERVICE,  2019–2022 (USD MILLION)
                       TABLE 101 EUROPE: MARKET, BY PROFESSIONAL SERVICE,  2023–2028 (USD MILLION)
                       TABLE 102 EUROPE: MARKET, BY APPLICATION,  2019–2022 (USD MILLION)
                       TABLE 103 EUROPE: MARKET, BY APPLICATION,  2023–2028 (USD MILLION)
                       TABLE 104 EUROPE: MARKET, BY VERTICAL,  2019–2022 (USD MILLION)
                       TABLE 105 EUROPE: MARKET, BY VERTICAL,  2023–2028 (USD MILLION)
                       TABLE 106 EUROPE: MARKET, BY COUNTRY,  2019–2022 (USD MILLION)
                       TABLE 107 EUROPE: MARKET, BY COUNTRY,  2023–2028 (USD MILLION)
             10.3.3 UK
                       10.3.3.1 Government initiatives to drive demand for synthetic data generation
                                   TABLE 108 UK: SYNTHETIC DATA GENERATION MARKET, BY OFFERING, 2019–2022 (USD MILLION)
                                   TABLE 109 UK: MARKET, BY OFFERING, 2023–2028 (USD MILLION)
             10.3.4 GERMANY
                       10.3.4.1 Surge in adoption of artificial intelligence to drive demand for synthetic data generation tools in research and development
                                   TABLE 110 GERMANY: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 111 GERMANY: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.3.5 FRANCE
                       10.3.5.1 Growing demand for research and educational excellence to drive demand for AI-enabled technologies
                                   TABLE 112 FRANCE: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 113 FRANCE: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.3.6 ITALY
                       10.3.6.1 Demand for deep learning model to perform source separation and music generation to drive market growth
                                   TABLE 114 ITALY: MARKET, BY OFFERING, 2019–2022 (USD MILLION)
                                   TABLE 115 ITALY: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.3.7 SPAIN
                       10.3.7.1 Need to develop AI solutions and language models to drive market for synthetic data generation
                                   TABLE 116 SPAIN: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 117 SPAIN: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.3.8 FINLAND
                       10.3.8.1 Growing demand for AI applications in education sector to drive market growth
             10.3.9 REST OF EUROPE
                       TABLE 118 REST OF EUROPE: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 119 REST OF EUROPE: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
     10.4 ASIA PACIFIC 
             10.4.1 ASIA PACIFIC: MARKET DRIVERS
             10.4.2 ASIA PACIFIC: RECESSION IMPACT
                       FIGURE 32 ASIA PACIFIC: SYNTHETIC DATA GENERATION MARKET SNAPSHOT
                       TABLE 120 ASIA PACIFIC: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 121 ASIA PACIFIC: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
                       TABLE 122 ASIA PACIFIC: MARKET, BY DATA TYPE,  2019–2022 (USD MILLION)
                       TABLE 123 ASIA PACIFIC: MARKET, BY DATA TYPE,  2023–2028 (USD MILLION)
                       TABLE 124 ASIA PACIFIC: MARKET, BY SERVICE,  2019–2022 (USD MILLION)
                       TABLE 125 ASIA PACIFIC: MARKET, BY SERVICE,  2023–2028 (USD MILLION)
                       TABLE 126 ASIA PACIFIC: MARKET, BY PROFESSIONAL SERVICE,  2019–2022 (USD MILLION)
                       TABLE 127 ASIA PACIFIC: MARKET, BY PROFESSIONAL SERVICE,  2023–2028 (USD MILLION)
                       TABLE 128 ASIA PACIFIC: MARKET, BY APPLICATION,  2019–2022 (USD MILLION)
                       TABLE 129 ASIA PACIFIC: MARKET, BY APPLICATION,  2023–2028 (USD MILLION)
                       TABLE 130 ASIA PACIFIC: MARKET, BY VERTICAL,  2019–2022 (USD MILLION)
                       TABLE 131 ASIA PACIFIC: MARKET, BY VERTICAL,  2023–2028 (USD MILLION)
                       TABLE 132 ASIA PACIFIC: MARKET, BY COUNTRY,  2019–2022 (USD MILLION)
                       TABLE 133 ASIA PACIFIC: MARKET, BY COUNTRY,  2023–2028 (USD MILLION)
             10.4.3 CHINA
                       10.4.3.1 Growth in technology sector to boost popularity of synthetic data generation solutions
                                   TABLE 134 CHINA: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 135 CHINA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.4.4 INDIA
                       10.4.4.1 Rise in digital transformation across industry verticals to drive market growth
                                   TABLE 136 INDIA: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 137 INDIA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.4.5 JAPAN
                       10.4.5.1 Need to digitalize pharma industry to drive adoption of synthetic data generation solutions
                                   TABLE 138 JAPAN: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 139 JAPAN: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.4.6 SOUTH KOREA
                       10.4.6.1 Rise in initiatives by government to boost adoption of synthetic data generation tools
                                   TABLE 140 SOUTH KOREA: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 141 SOUTH KOREA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.4.7 SINGAPORE
                       10.4.7.1 Steady progress made in AI advancements to drive market for synthetic data generation solutions
                                   TABLE 142 SINGAPORE: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 143 SINGAPORE: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.4.8 AUSTRALIA & NEW ZEALAND
                       10.4.8.1 Popularity of ChatGPT and large language models to propel popularity of synthetic data generation solutions
                                   TABLE 144 AUSTRALIA & NEW ZEALAND: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 145 AUSTRALIA & NEW ZEALAND: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.4.9 REST OF ASIA PACIFIC
                       TABLE 146 REST OF ASIA PACIFIC: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 147 REST OF ASIA PACIFIC: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
     10.5 MIDDLE EAST & AFRICA 
             10.5.1 MIDDLE EAST & AFRICA: MARKET DRIVERS
             10.5.2 MIDDLE EAST & AFRICA: RECESSION IMPACT
                       TABLE 148 MIDDLE EAST & AFRICA: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 149 MIDDLE EAST & AFRICA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
                       TABLE 150 MIDDLE EAST & AFRICA: MARKET, BY DATA TYPE,  2019–2022 (USD MILLION)
                       TABLE 151 MIDDLE EAST & AFRICA: MARKET, BY DATA TYPE,  2023–2028 (USD MILLION)
                       TABLE 152 MIDDLE EAST & AFRICA: MARKET, BY SERVICE,  2019–2022 (USD MILLION)
                       TABLE 153 MIDDLE EAST & AFRICA: MARKET, BY SERVICE,  2023–2028 (USD MILLION)
                       TABLE 154 MIDDLE EAST & AFRICA: MARKET, BY PROFESSIONAL SERVICE, 2019–2022 (USD MILLION)
                       TABLE 155 MIDDLE EAST & AFRICA: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)
                       TABLE 156 MIDDLE EAST & AFRICA: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
                       TABLE 157 MIDDLE EAST & AFRICA: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
                       TABLE 158 MIDDLE EAST & AFRICA: MARKET, BY VERTICAL,  2019–2022 (USD MILLION)
                       TABLE 159 MIDDLE EAST & AFRICA: MARKET, BY VERTICAL,  2023–2028 (USD MILLION)
                       TABLE 160 MIDDLE EAST & AFRICA: MARKET, BY COUNTRY,  2019–2022 (USD MILLION)
                       TABLE 161 MIDDLE EAST & AFRICA: MARKET, BY COUNTRY,  2023–2028 (USD MILLION)
             10.5.3 SAUDI ARABIA
                       10.5.3.1 Major investments by tech giants to drive demand for synthetic data generation solutions
                                   TABLE 162 SAUDI ARABIA: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 163 SAUDI ARABIA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.5.4 UAE
                       10.5.4.1 Adoption of trailblazing technologies for advanced education system to boost market growth
                                   TABLE 164 UAE: MARKET, BY OFFERING, 2019–2022 (USD MILLION)
                                   TABLE 165 UAE: MARKET, BY OFFERING, 2023–2028 (USD MILLION)
             10.5.5 SOUTH AFRICA
                       10.5.5.1 Demand for advancements in healthcare sector to drive market for synthetic data generation solutions
                                   TABLE 166 SOUTH AFRICA: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 167 SOUTH AFRICA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.5.6 ISRAEL
                       10.5.6.1 Strong cluster of synthetic data generation and AI startups to lead to market growth
                                   TABLE 168 ISRAEL: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 169 ISRAEL: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.5.7 REST OF MIDDLE EAST & AFRICA
     10.6 LATIN AMERICA 
             10.6.1 LATIN AMERICA: MARKET DRIVERS
             10.6.2 LATIN AMERICA: RECESSION IMPACT
                       TABLE 170 LATIN AMERICA: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 171 LATIN AMERICA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
                       TABLE 172 LATIN AMERICA: MARKET, BY DATA TYPE,  2019–2022 (USD MILLION)
                       TABLE 173 LATIN AMERICA: MARKET, BY DATA TYPE,  2023–2028 (USD MILLION)
                       TABLE 174 LATIN AMERICA: MARKET, BY SERVICE,  2019–2022 (USD MILLION)
                       TABLE 175 LATIN AMERICA: MARKET, BY SERVICE,  2023–2028 (USD MILLION)
                       TABLE 176 LATIN AMERICA: MARKET, BY PROFESSIONAL SERVICE,  2019–2022 (USD MILLION)
                       TABLE 177 LATIN AMERICA: MARKET, BY PROFESSIONAL SERVICE,  2023–2028 (USD MILLION)
                       TABLE 178 LATIN AMERICA: MARKET, BY APPLICATION,  2019–2022 (USD MILLION)
                       TABLE 179 LATIN AMERICA: MARKET, BY APPLICATION,  2023–2028 (USD MILLION)
                       TABLE 180 LATIN AMERICA: MARKET, BY VERTICAL,  2019–2022 (USD MILLION)
                       TABLE 181 LATIN AMERICA: MARKET, BY VERTICAL,  2023–2028 (USD MILLION)
                       TABLE 182 LATIN AMERICA: MARKET, BY COUNTRY,  2019–2022 (USD MILLION)
                       TABLE 183 LATIN AMERICA: MARKET, BY COUNTRY,  2023–2028 (USD MILLION)
             10.6.3 BRAZIL
                       10.6.3.1 Establishment of cloud region to propel market growth of synthetic data generation solutions
                                   TABLE 184 BRAZIL: SYNTHETIC DATA GENERATION MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 185 BRAZIL: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.6.4 MEXICO
                       10.6.4.1 Demand for digitalizing banking sector to boost adoption of MARKET
                                   TABLE 186 MEXICO: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                                   TABLE 187 MEXICO: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
             10.6.5 ARGENTINA
                       10.6.5.1 Use of synthetic data generation solutions by government to address economic and political instability to drive market growth
             10.6.6 REST OF LATIN AMERICA
                       TABLE 188 REST OF LATIN AMERICA: MARKET, BY OFFERING,  2019–2022 (USD MILLION)
                       TABLE 189 REST OF LATIN AMERICA: MARKET, BY OFFERING,  2023–2028 (USD MILLION)
 
11 COMPETITIVE LANDSCAPE (Page No. - 186)
     11.1 OVERVIEW 
     11.2 STRATEGIES ADOPTED BY KEY PLAYERS 
               TABLE 190 STRATEGIES ADOPTED BY KEY PLAYERS
     11.3 REVENUE ANALYSIS 
             11.3.1 HISTORICAL REVENUE ANALYSIS FOR KEY PLAYERS
                       FIGURE 33 HISTORICAL REVENUE ANALYSIS FOR KEY PLAYERS, 2020–2022 (USD MILLION)
     11.4 MARKET SHARE ANALYSIS 
               FIGURE 34 MARKET SHARE ANALYSIS FOR KEY PLAYERS, 2022
               TABLE 191 MARKET: INTENSITY OF COMPETITIVE RIVALRY
     11.5 EVALUATION QUADRANT MATRIX FOR KEY PLAYERS 
             11.5.1 STARS
             11.5.2 EMERGING LEADERS
             11.5.3 PERVASIVE PLAYERS
             11.5.4 PARTICIPANTS
                       FIGURE 35 EVALUATION QUADRANT MATRIX FOR KEY PLAYERS, 2022
     11.6 COMPETITIVE BENCHMARKING FOR KEY PLAYERS 
               TABLE 192 PRODUCT FOOTPRINT ANALYSIS FOR KEY PLAYERS, 2022
     11.7 EVALUATION QUADRANT MATRIX FOR STARTUPS/SMES 
             11.7.1 PROGRESSIVE COMPANIES
             11.7.2 RESPONSIVE COMPANIES
             11.7.3 DYNAMIC COMPANIES
             11.7.4 STARTING BLOCKS
                       FIGURE 36 EVALUATION QUADRANT MATRIX FOR STARTUPS/SMES, 2022
     11.8 COMPETITIVE BENCHMARKING FOR STARTUPS/SMES 
               TABLE 193 MARKET: DETAILED LIST OF KEY STARTUPS/SMES
               TABLE 194 PRODUCT FOOTPRINT ANALYSIS FOR STARTUPS/SMES, 2022
     11.9 COMPETITIVE SCENARIO 
             11.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
                       TABLE 195 PRODUCT LAUNCHES AND ENHANCEMENTS, 2020–2023
             11.9.2 DEALS
                       TABLE 196 DEALS, 2020–2023
 
12 COMPANY PROFILES (Page No. - 203)
(Business Overview, Products Offered, Recent Developments, MnM View Right to win, Strategic choices made, Weaknesses and competitive threats) *
     12.1 INTRODUCTION 
     12.2 KEY PLAYERS 
             12.2.1 MICROSOFT
                       TABLE 197 MICROSOFT: BUSINESS OVERVIEW
                       FIGURE 37 MICROSOFT: COMPANY SNAPSHOT
                       TABLE 198 MICROSOFT: SOLUTIONS/SERVICES OFFERED
                       TABLE 199 MICROSOFT: PRODUCT LAUNCHES AND ENHANCEMENTS
                       TABLE 200 MICROSOFT: DEALS
             12.2.2 GOOGLE
                       TABLE 201 GOOGLE: BUSINESS OVERVIEW
                       FIGURE 38 GOOGLE: COMPANY SNAPSHOT
                       TABLE 202 GOOGLE: SOLUTIONS/SERVICES OFFERED
                       TABLE 203 GOOGLE: DEALS
             12.2.3 IBM
                       TABLE 204 IBM: BUSINESS OVERVIEW
                       FIGURE 39 IBM: COMPANY SNAPSHOT
                       TABLE 205 IBM: SOLUTIONS/SERVICES OFFERED
                       TABLE 206 IBM: PRODUCT LAUNCHES AND ENHANCEMENTS
                       TABLE 207 IBM: DEALS
             12.2.4 AWS
                       TABLE 208 AWS: BUSINESS OVERVIEW
                       FIGURE 40 AWS: COMPANY SNAPSHOT
                       TABLE 209 AWS: SOLUTIONS/SERVICES OFFERED
                       TABLE 210 AWS: PRODUCT LAUNCHES AND ENHANCEMENTS
                       TABLE 211 AWS: DEALS
             12.2.5 NVIDIA CORPORATION
                       TABLE 212 NVIDIA CORPORATION: BUSINESS OVERVIEW
                       FIGURE 41 NVIDIA CORPORATION: COMPANY SNAPSHOT
                       TABLE 213 NVIDIA CORPORATION: SOLUTIONS/SERVICES OFFERED
             12.2.6 OPENAI
                       TABLE 214 OPENAI: BUSINESS OVERVIEW
                       TABLE 215 OPENAI: SOLUTIONS/SERVICES OFFERED
                       TABLE 216 OPENAI: PRODUCT LAUNCHES AND ENHANCEMENTS
                       TABLE 217 OPENAI: DEALS
             12.2.7 INFORMATICA
                       TABLE 218 INFORMATICA: BUSINESS OVERVIEW
                       FIGURE 42 INFORMATICA: COMPANY SNAPSHOT
                       TABLE 219 INFORMATICA: SOLUTIONS/SERVICES OFFERED
                       TABLE 220 INFORMATICA: PRODUCT LAUNCHES AND ENHANCEMENTS
                       TABLE 221 INFORMATICA: DEALS
             12.2.8 BROADCOM
                       TABLE 222 BROADCOM: BUSINESS OVERVIEW
                       FIGURE 43 BROADCOM: COMPANY SNAPSHOT
                       TABLE 223 BROADCOM: SOLUTIONS/SERVICES OFFERED
             12.2.9 CAPGEMINI
                       TABLE 224 CAPGEMINI: BUSINESS OVERVIEW
                       FIGURE 44 CAPGEMINI: COMPANY SNAPSHOT
                       TABLE 225 CAPGEMINI: SOLUTIONS/SERVICES OFFERED
             12.2.10 MPHASIS
                       TABLE 226 MPHASIS: BUSINESS OVERVIEW
                       FIGURE 45 MPHASIS: COMPANY SNAPSHOT
                       TABLE 227 MPHASIS: SOLUTIONS/SERVICES OFFERED
             12.2.11 DATABRICS
                       TABLE 228 DATABRICS: BUSINESS OVERVIEW
                       TABLE 229 DATABRICS: SOLUTIONS/SERVICES OFFERED
                                   TABLE 230 DATABRICS: DEALS
             12.2.12 MOSTLY AI
                       TABLE 231 MOSTLY AI: BUSINESS OVERVIEW
                       TABLE 232 MOSTLY AI: SOLUTIONS/SERVICES OFFERED
                       TABLE 233 MOSTLY AI: DEALS
             12.2.13 TONIC
                       TABLE 234 TONIC: BUSINESS OVERVIEW
                       TABLE 235 TONIC: SOLUTIONS/SERVICES OFFERED
                       TABLE 236 TONIC: DEALS
             12.2.14 MD CLONE
                       TABLE 237 MD CLONE: BUSINESS OVERVIEW
                       TABLE 238 MD CLONE: SOLUTIONS/SERVICES OFFERED
                       TABLE 239 MD CLONE: DEALS
             12.2.15 TCS
     12.3 STARTUPS/SMES 
             12.3.1 HAZY
             12.3.2 SYNTHESIA
             12.3.3 SYNTHESIZED
             12.3.4 FACTEUS
             12.3.5 ANYVERSE
             12.3.6 NEUROLABS
             12.3.7 RENDERED AI
             12.3.8 GRETEL
             12.3.9 ONEVIEW
             12.3.10 GENROCKET
             12.3.11 Y DATA
             12.3.12 CVEDIA
             12.3.13 SYNTHETICUS
             12.3.14 ANYLOGIC
             12.3.15 BIFROST AI
             12.3.16 ANONOS
*Details on Business Overview, Products Offered, Recent Developments, MnM View, Right to win, Strategic choices made, Weaknesses and competitive threats might not be captured in case of unlisted companies.
 
13 ADJACENT AND RELATED MARKETS (Page No. - 250)
     13.1 NATURAL LANGUAGE PROCESSING MARKET 
             13.1.1 MARKET DEFINITION
             13.1.2 MARKET OVERVIEW
             13.1.3 NATURAL LANGUAGE PROCESSING MARKET, BY COMPONENT
                       TABLE 240 NATURAL LANGUAGE PROCESSING MARKET, BY COMPONENT,  2016–2021 (USD MILLION)
                       TABLE 241 NATURAL LANGUAGE PROCESSING MARKET, BY COMPONENT,  2022–2027 (USD MILLION)
             13.1.4 NATURAL LANGUAGE PROCESSING MARKET, BY TYPE
                       TABLE 242 NATURAL LANGUAGE PROCESSING MARKET, BY TYPE, 2016–2021 (USD MILLION)
                       TABLE 243 NATURAL LANGUAGE PROCESSING MARKET, BY TYPE, 2022–2027 (USD MILLION)
             13.1.5 NATURAL LANGUAGE PROCESSING MARKET, BY DEPLOYMENT MODE
                       TABLE 244 NATURAL LANGUAGE PROCESSING MARKET, BY DEPLOYMENT MODE,  2016–2021 (USD MILLION)
                       TABLE 245 NATURAL LANGUAGE PROCESSING MARKET, BY DEPLOYMENT MODE,  2022–2027 (USD MILLION)
             13.1.6 NATURAL LANGUAGE PROCESSING MARKET, BY ORGANIZATION SIZE
                       TABLE 246 NATURAL LANGUAGE PROCESSING MARKET, BY ORGANIZATION SIZE,  2016–2021 (USD MILLION)
                       TABLE 247 NATURAL LANGUAGE PROCESSING MARKET, BY ORGANIZATION SIZE,  2022–2027 (USD MILLION)
             13.1.7 NATURAL LANGUAGE PROCESSING MARKET, BY APPLICATION
                       TABLE 248 NATURAL LANGUAGE PROCESSING MARKET, BY APPLICATION,  2016–2021 (USD MILLION)
                       TABLE 249 NATURAL LANGUAGE PROCESSING MARKET, BY APPLICATION,  2022–2027 (USD MILLION)
             13.1.8 NATURAL LANGUAGE PROCESSING MARKET, BY TECHNOLOGY
                       TABLE 250 NATURAL LANGUAGE PROCESSING MARKET, BY TECHNOLOGY,  2016–2021 (USD MILLION)
                       TABLE 251 NATURAL LANGUAGE PROCESSING MARKET, BY TECHNOLOGY,  2022–2027 (USD MILLION)
             13.1.9 NATURAL LANGUAGE PROCESSING MARKET, BY VERTICAL
                       TABLE 252 NATURAL LANGUAGE PROCESSING MARKET, BY VERTICAL, 2016–2021 (USD MILLION)
                       TABLE 253 NATURAL LANGUAGE PROCESSING MARKET, BY VERTICAL, 2022–2027 (USD MILLION)
             13.1.10 NATURAL LANGUAGE PROCESSING MARKET, BY REGION
                       TABLE 254 NATURAL LANGUAGE PROCESSING MARKET, BY REGION, 2016–2021 (USD MILLION)
                       TABLE 255 NATURAL LANGUAGE PROCESSING MARKET, BY REGION, 2022–2027 (USD MILLION)
     13.2 ARTIFICIAL INTELLIGENCE MARKET 
             13.2.1 MARKET DEFINITION
             13.2.2 MARKET OVERVIEW
             13.2.3 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING
                       TABLE 256 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2016–2021 (USD BILLION)
                       TABLE 257 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2022–2027 (USD BILLION)
             13.2.4 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY
                       TABLE 258 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2016–2021 (USD BILLION)
                       TABLE 259 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2022–2027 (USD BILLION)
             13.2.5 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE
                       TABLE 260 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE, 2016–2021 (USD BILLION)
                       TABLE 261 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE, 2022–2027 (USD BILLION)
             13.2.6 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION SIZE
                       TABLE 262 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION SIZE, 2016–2021 (USD BILLION)
                       TABLE 263 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION SIZE, 2022–2027 (USD BILLION)
             13.2.7 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION
                       TABLE 264 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2016–2021 (USD BILLION)
                       TABLE 265 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2022–2027 (USD BILLION)
             13.2.8 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL
                       TABLE 266 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2016–2021 (USD BILLION)
                       TABLE 267 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2022–2027 (USD BILLION)
             13.2.9 ARTIFICIAL INTELLIGENCE MARKET, BY REGION
                       TABLE 268 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2016–2021 (USD BILLION)
                       TABLE 269 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2022–2027 (USD BILLION)
 
14 APPENDIX (Page No. - 265)
     14.1 DISCUSSION GUIDE 
     14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 
     14.3 CUSTOMIZATION OPTIONS 
     14.4 RELATED REPORTS 
     14.5 AUTHOR DETAILS 

The synthetic data generation market research study involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, preferred Synthetic data generation providers, third-party service providers, consulting service providers, end-users, and other commercial enterprises. In-depth interviews with primary respondents, including key industry participants and subject matter experts, were conducted to obtain and verify critical qualitative and quantitative information and assess the market's prospects.

Secondary Research

In the secondary research process, various sources were referred to for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendors' websites. Additionally, the spending of various countries on synthetic data generation was extracted from the respective sources. Secondary research was mainly used to obtain the key information related to the industry's value chain and supply chain to identify the key players based on solutions, services, market classification, and segmentation according to offerings of the major players, industry trends related to solutions/platforms, services, application, data types, verticals, and regions, and the key developments from both market- and technology-oriented perspectives

Primary Research

In the primary research process, various sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including chief experience officers (CXOs); vice presidents (VPs); directors from business development, marketing, and synthetic data generation expertise; related key executives from synthetic data generation solution vendors, SIs, professional service providers, and industry associations; and the key opinion leaders.

Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as chief information officers (CIOs), chief technology officers (CTOs), chief strategy officers (CSOs), and end-users using synthetic data generation solutions, were interviewed to understand the buyer's perspective on suppliers, products, service providers, and their current usage of synthetic data generation solutions and services, which would impact the overall synthetic data generation market.

Synthetic Data Generation Market Size, and Share

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

Multiple approaches were adopted for estimating and forecasting the synthetic data generation market. The first approach involves estimating the market size by the summation of the revenue companies generate through the sale of solutions and services.

Bottom-Up Approach

the bottom-up approach, the adoption rate of synthetic data generation market solutions and services among different end-users in the key countries with respect to their regions contributing the most to the market share was identified. For cross-validation, the adoption of synthetic data generation solutions and services among industries, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.

Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the synthetic data generation market's regional penetration. Based on secondary research, the regional spending on information and communications technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major synthetic data generation providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall synthetic data generation market size and segments' size were determined and confirmed using the study.

Top-Down Approach

In the top-down approach, an exhaustive list of all the vendors offering solutions and services in the synthetic data generation market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor's offerings were evaluated based on the breadth of solutions and services, deployment modes, applications, and verticals. The aggregate of all the companies' revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets repository for validation.

The list of vendors considered for estimating the market size is not limited to those profiled in the report. However, MarketsandMarkets prepared a list of vendors offering synthetic data generation solutions and services. They mapped their products related to the synthetic data generation market to identify major vendors operating in the market.

Synthetic Data Generation Market Size, and Share

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Data Triangulation

The market was split into several segments and subsegments after arriving at the overall market size using the market size estimation processes as explained above. The data triangulation and market breakup procedures were employed, wherever applicable, to complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment. The data was triangulated by studying various factors and trends from both the demand and supply sides.

Market Definition

The synthetic data generation market includes software, tools, and platforms provided by synthetic data vendors to design or create artificial data sets that mimic real-world data. It also includes managed and professional services provided by synthetic data service providers. Synthetic data is any information manufactured artificially which does not represent events or objects in the real world. Algorithms create synthetic data used in model datasets for testing or training purposes. Synthetic data can mimic operational or production data and help train machine learning (ML) models or test out mathematical models.

Key Stakeholders

  • Synthetic data generation vendors
  • Synthetic data generation service vendors
  • Managed service providers
  • Support and maintenance service providers
  • System integrators (SIs)/Migration service providers
  • Value-added resellers (VARs) and distributors
  • Independent software vendors (ISVs)
  • Third-party providers
  • Technology providers
  • Compliance regulatory authorities
  • Government authorities

Report Objectives

  • To define, describe, and forecast the synthetic data generation market by offering (solutions/platforms and services), data type, application, and vertical.
  • To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth.
  • To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
  • To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the synthetic data generation market
  • To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders.
  • To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To profile the key players and comprehensively analyze their market ranking and core competencies.
  • To analyze competitive developments, such as partnerships, product launches, and mergers and acquisitions, in the synthetic data generation market
  • To analyze the impact of recession in the synthetic data generation market across all the regions

Available Customizations

Along with the market data, MarketsandMarkets offers customizations as per the company's specific needs. The following customization options are available for the report:

Company Information:

  • Detailed analysis and profiling of additional market players (Up to 5)

Geographic Analysis:

  • Further breakup of the North American synthetic data generation market
  • Further breakup of the European market
  • Further breakup of the Asia Pacific market
  • Further breakup of the Latin American market
  • Further breakup of the Middle East & Africa market
Custom Market Research Services

We will customize the research for you, in case the report listed above does not meet with your exact requirements. Our custom research will comprehensively cover the business information you require to help you arrive at strategic and profitable business decisions.

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Report Code
TC 8669
Published ON
May, 2023
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