The Latin America, Middle East and Africa Synthetic Data Generation Market should witness market growth of 41.7% CAGR during the forecast period (2022-2028).
There are no consistent and objective methods for determining if a synthetic database is sufficiently distinct from the underlying real dataset to be considered truly anonymous. There are currently no regulations governing the use of synthetic data. Although there is a growing interest in the good applications of synthetic data, consumers and policymakers must also be aware of the potential downsides.
It has been anticipated that synthetic data would surpass actual data for algorithm development in the coming years, and investment in synthetic data is increasing. Synthetic data can further be used across a number of new applications in the coming years as technology advances. Synthetic data comprises the potential to bring a revolution in various crucial industries.
Various businesses can leverage synthetic data in order to anticipate customer interests and fulfill their demands to offer maximum customer satisfaction. Moreover, it can also be used in the aerospace and defense sector for a number of purposes. The rising number of applications of synthetic data due to rapid technological advancements would play a major role in bolstering the growth of this market in the coming years.
The retail industry in South Africa is well established and continues to grow in other African nations. According to the department of agriculture, as the South African economy started to recover from the impact of the COVID-19 pandemic, retail food sales in 2021 totaled $40 billion, a 0.2% rise from 2020. The expansion followed the relaxation of COVID-19 requirements and an increase in in-store shopping rates.
The Brazil market dominated the LAMEA Synthetic Data Generation Market by Country in 2021, and would continue to be a dominant market till 2028; thereby, achieving a market value of $19,556.1 Thousands by 2028.The Argentina market is experiencing a CAGR of 42.5% during (2022-2028). Additionally, The UAE market would display a CAGR of 41.3% during (2022-2028).
Based on Application, the market is segmented into Natural Language Processing, Data Protection, Predictive Analytics, Computer Vision Algorithms and Data Sharing & Others. Based on Offering, the market is segmented into Fully Synthetic Data, Partially Synthetic Data and Hybrid Synthetic Data. Based on Data Type, the market is segmented into Tabular Data, Text Data, Image & Video Data and Others. Based on Modeling Type, the market is segmented into Agent-based Modeling and Direct Modeling. Based on End-use, the market is segmented into Healthcare & Life sciences, IT & Telecommunication, Transportation & Logistics, Retail & E-commerce, BFSI, Consumer Electronics and Manufacturing & Others. Based on countries, the market is segmented into Brazil, Argentina, UAE, Saudi Arabia, South Africa, Nigeria, and Rest of LAMEA.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Kinetic Vision, Inc. (Deep Vision Data), MOSTLY AI Solutions MP GmbH, Synthesis AI, Inc., Statice GmbH, YData, Ekobit d.o.o, Hazy Limited, Kymera-labs, MDClone Limited, and Neuromation.
There are no consistent and objective methods for determining if a synthetic database is sufficiently distinct from the underlying real dataset to be considered truly anonymous. There are currently no regulations governing the use of synthetic data. Although there is a growing interest in the good applications of synthetic data, consumers and policymakers must also be aware of the potential downsides.
It has been anticipated that synthetic data would surpass actual data for algorithm development in the coming years, and investment in synthetic data is increasing. Synthetic data can further be used across a number of new applications in the coming years as technology advances. Synthetic data comprises the potential to bring a revolution in various crucial industries.
Various businesses can leverage synthetic data in order to anticipate customer interests and fulfill their demands to offer maximum customer satisfaction. Moreover, it can also be used in the aerospace and defense sector for a number of purposes. The rising number of applications of synthetic data due to rapid technological advancements would play a major role in bolstering the growth of this market in the coming years.
The retail industry in South Africa is well established and continues to grow in other African nations. According to the department of agriculture, as the South African economy started to recover from the impact of the COVID-19 pandemic, retail food sales in 2021 totaled $40 billion, a 0.2% rise from 2020. The expansion followed the relaxation of COVID-19 requirements and an increase in in-store shopping rates.
The Brazil market dominated the LAMEA Synthetic Data Generation Market by Country in 2021, and would continue to be a dominant market till 2028; thereby, achieving a market value of $19,556.1 Thousands by 2028.The Argentina market is experiencing a CAGR of 42.5% during (2022-2028). Additionally, The UAE market would display a CAGR of 41.3% during (2022-2028).
Based on Application, the market is segmented into Natural Language Processing, Data Protection, Predictive Analytics, Computer Vision Algorithms and Data Sharing & Others. Based on Offering, the market is segmented into Fully Synthetic Data, Partially Synthetic Data and Hybrid Synthetic Data. Based on Data Type, the market is segmented into Tabular Data, Text Data, Image & Video Data and Others. Based on Modeling Type, the market is segmented into Agent-based Modeling and Direct Modeling. Based on End-use, the market is segmented into Healthcare & Life sciences, IT & Telecommunication, Transportation & Logistics, Retail & E-commerce, BFSI, Consumer Electronics and Manufacturing & Others. Based on countries, the market is segmented into Brazil, Argentina, UAE, Saudi Arabia, South Africa, Nigeria, and Rest of LAMEA.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Kinetic Vision, Inc. (Deep Vision Data), MOSTLY AI Solutions MP GmbH, Synthesis AI, Inc., Statice GmbH, YData, Ekobit d.o.o, Hazy Limited, Kymera-labs, MDClone Limited, and Neuromation.
Scope of the Study
By Application
- Natural Language Processing
- Data Protection
- Predictive Analytics
- Computer Vision Algorithms
- Data Sharing & Others
By Offering
- Fully Synthetic Data
- Partially Synthetic Data
- Hybrid Synthetic Data
By Data Type
- Tabular Data
- Text Data
- Image & Video Data
- Others
By Modeling Type
- Agent-based Modeling
- Direct Modeling
By End-use
- Healthcare & Life sciences
- IT & Telecommunication
- Transportation & Logistics
- Retail & E-commerce
- BFSI
- Consumer Electronics
- Manufacturing & Others
By Country
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Key Market Players
List of Companies Profiled in the Report:
- Kinetic Vision, Inc. (Deep Vision Data)
- MOSTLY AI Solutions MP GmbH
- Synthesis AI, Inc.
- Statice GmbH
- YData
- Ekobit d.o.o
- Hazy Limited
- Kymera-labs
- MDClone Limited
- Neuromation
Unique Offerings
- Exhaustive coverage
- The highest number of market tables and figures
- Subscription-based model available
- Guaranteed best price
- Assured post sales research support with 10% customization free
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
Chapter 3. LAMEA Synthetic Data Generation Market by Application
Chapter 4. LAMEA Synthetic Data Generation Market by Offering
Chapter 5. LAMEA Synthetic Data Generation Market by Data Type
Chapter 6. LAMEA Synthetic Data Generation Market by Modeling Type
Chapter 7. LAMEA Synthetic Data Generation Market by End-use
Chapter 8. LAMEA Synthetic Data Generation Market by Country
Chapter 9. Company Profiles
Companies Mentioned
- Kinetic Vision, Inc. (Deep Vision Data)
- MOSTLY AI Solutions MP GmbH
- Synthesis AI, Inc.
- Statice GmbH
- YData
- Ekobit d.o.o
- Hazy Limited
- Kymera-labs
- MDClone Limited
- Neuromation
Methodology
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