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Synthetic Data Generation Market Report Scope & Overview:

The Synthetic Data Generation Market size was USD 0.2 billion in 2023 and is expected to Reach USD 2.5 billion by 2031 and grow at a CAGR of 36.0% over the forecast period of 2024-2031.

The market for creating synthetic data was already picking up steam and exhibiting bright development possibilities. Synthetic data is defined as intentionally created data that resembles actual data but does not include any PII or sensitive information. It is used for a variety of things, including developing machine learning models, sharing data without disclosing sensitive information, and improving data privacy and security. Synthetic data presented a practical alternative to real data as the importance of data privacy laws and the necessity to protect sensitive information increased.

Synthetic Data Generation Market Revenue Analysis

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It made it possible for businesses to exchange data for research, analysis, and collaboration without disclosing any private information. The market for synthetic data generation saw use in a number of sectors, including healthcare, finance, retail, and automotive. Every area saw the potential benefits of synthetic data in accelerating innovation and improving decision-making.

The advancement of data testing and sharing capabilities, both within and internationally with other government departments, academia, and other industries, is one of the major advantages that synthetic data may offer. Collaboration will be facilitated through reducing friction between services, which will ultimately help to improve services for customers. Additionally, the use of synthetic data will aid to advance data literacy and comprehension. Quick dashboards that can be created on this accurate reproduction will be beneficial. Businesses can then use this data literacy to make better decisions. Additionally, it might pave the way for increased data sharing and cross-sector collaboration, utilizing the advantages of crowdsourcing innovation and collaborating with businesses and academic institutions. In the current landscape these opportunities can be very limited, and require a lot of effort to materialize, thus, likely to drive the growth of the market. Synthetic data is a valuable tool for testing and verifying systems, algorithms, and models, as well as for reducing risk. Organisations are able to model and assess numerous scenarios and edge cases without taking any risks or suffering any unfavourable effects from using real data. Before deploying systems in actual situations, thorough testing using synthetic data ensures robustness and dependability.

MARKET DYNAMICS

KEY DRIVERS

  • The market for synthetic data production has new growth potential due to the rising demand for IoT and connected devices.

  • Increasing demand for data security and privacy, rising investment in advanced technologies drive the growth of the market.

The adoption of synthetic data generation drives the BFSI market, automotive industry, healthcare industry, eCommerce industry, and IT and technology sector. Synthetic data generation is valuable in the automotive industry for training autonomous driving systems and testing vehicle performance in various scenarios.

RESTRAIN

  • Synthetic data generation often relies on high-end generative models.

such as Generative Adversarial Networks (GANs), which can be expensive. It requires significant investments in computing power, hardware, and the supervision of data scientists. These costs may be prohibitive for organizations with budget constraints

OPPORTUNITY

  • It creates Opportunity in Data analytics and visualization.

  • Synthetic data can be used to create new revenue streams by selling datasets to third-party organizations

Synthetic data can be used to create large datasets for data analytics and visualization, enabling organizations to gain insights and make data-driven decisions

CHALLENGES

  • Inconsistencies in replicating complexities

  • Ensuring the accuracy of synthetic data is crucial for its effectiveness.

Complexity replication errors can occur when transferring complicated patterns from actual to artificial data. The synthetic data may differ from the original data since it can be difficult to capture all the nuances and intricacies of the original data.

IMPACT OF RUSSIAN UKRAINE WAR

War has both positive and negative effects on synthetic data.  Some businesses' demand for synthetic data is anticipated to fall as a result of the war. Because of the uncertainty produced by the war, firms in Russia and Ukraine may be affected or possibly forced to close, while businesses in other countries may be hesitant to invest in new technologies. Concerns about data security and privacy may have risen as a result of the war. Businesses may have been warier about exchanging sensitive data across international borders due to current geopolitical concerns. As a result, there may be a greater need for synthetic data as a privacy-preserving alternative for training and testing machine learning models. The conflict and its aftermath may have motivated governments to put in place or amend data-related policies. regulations. Such regulations could impact the usage, sharing, and generation of data, potentially influencing the demand for synthetic data services. However, it is worth noting that the synthetic data generation market is likely to rise significantly in the next years, owing to a variety of factors such as rising data privacy concerns, increased usage of AI and ML technologies, and rising cybersecurity concerns.

 IMPACT OF ONGOING RECESSION

The ongoing recession is anticipated to have an uncertain outcome on the market for synthetic data production. Although the market is still in its early stages of development, there is a significant chance for long-term growth. The recession will probably cause some businesses to reduce their desire for fake data. This is due to the possibility that companies may be less willing to invest in new technology during a recession. The recession may also result in less money being allocated for research and development in the market for synthetic data production. It might also open up new prospects for the market for producing synthetic data. This is due to the possibility that during the recession, firms will be seeking for methods to cut costs. Since it can replace the requirement to gather and label real-world data, synthetic data can be a more affordable way to train machine learning models. The recession may also cause a greater emphasis to be placed on data security and privacy. These issues can be addressed with synthetic data because it can be used to create data that is compliant with privacy regulations and that does not contain sensitive personal information. As of now in 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated. In 2022 alone, MOSTLY AI raised a $25 million Series B round led by Molten Ventures; Datagen landed a $50 million Series B led by Scale Venture Partners, and Synthesis AI pocketed a $17 million Series A. As it rises the market for synthetic data is also set to grow. Synthetic data start-ups that have raised significant amounts of funding already serve a wide range of sectors, from banking and healthcare to transportation and retail. But they expect use cases to keep on expanding, both inside new sectors as well as those where synthetic data is already common.

KEY MARKET SEGMENTS

 By Data Type

  • Tabular Data

  • Text Data

  • Image and Video Data

  • Others

 By Application

  • AI Training and Development

  • Test Data Management

  • Data Sharing and Retention

  • Data Analytics

  • Others

 By Industry Vertical

  • BFSI

  • Healthcare

  • Life Sciences

  • Transportation

  • Logistic

  • Government

  • Defense

  • IT

  • Telecommunication

  • Manufacturing

  • others

Region Coverage:

North America

  • US

  • Canada

  • Mexico

Europe

  • Eastern Europe

    • Poland

    • Romania

    • Hungary

    • Turkey

    • Rest of Eastern Europe

  • Western Europe

    • Germany

    • France

    • UK

    • Italy

    • Spain

    • Netherlands

    • Switzerland

    • Austria

    • Rest of Western Europe

Asia Pacific

  • China

  • India

  • Japan

  • South Korea

  • Vietnam

  • Singapore

  • Australia

  • Rest of Asia Pacific

Middle East & Africa

  • Middle East

    • UAE

    • Egypt

    • Saudi Arabia

    • Qatar

    • Rest of Middle East

  • Africa

    • Nigeria

    • South Africa

    • Rest of Africa

Latin America

  • Brazil

  • Argentina

  • Colombia

  • Rest of Latin America

REGIONAL ANALYSIS

North America dominated the market share in 2021 for the synthetic data generation market. The increase in usage of synthetic data generation in BFSI, retail, healthcare and other sectors to improve businesses and the customer experience are anticipated to provide the lucrative growth opportunities for the synthetic data generation market in North America. In 2022, North America held the largest share of 35%. Due to end-use sectors' growing interest in fraud detection, NLP, and image data, the United States and Canada have emerged as profitable locations. A number of businesses have increased their investments in synthetic data, including J.P. Morgan, American Express, Amazon, and Google's Waymo. For example, Amazon launched Amazon Sage Maker Ground Truth in June 2022 to create labeled synthetic image data. These market participants will exhibit a preference for artificial data to train machine learning, payment data to detect fraud, and anti-money laundering behaviours. the growing influence of computer vision will also do well in the prediction for the synthetic data production industry in North America. Physical security, manufacturing, and geospatial imagery have all gained significant interest.

The Asia Pacific region's synthetic data generation market is expanding significantly as a result of the region's rapid digital transformation, tightening data protection laws, expanding use of AI and ML technologies, increased cybersecurity worries, and a strong start-up scene. Synthetic data production is being used by organizations in the area to handle data-driven problems, adhere to regulations, improve the performance of AI and ML models, bolster cybersecurity protocols, and spur innovation. Asia Pacific's synthetic data generation market is well-positioned for continuous growth and prospects given the region's focus on digitalization and the growing demand for data-driven solutions. This is ascribed to an increase in the usage of cloud-based services and improved technology, which both drive the growth of the synthetic data production market in this area.

Synthetic Data Generation Market By Region

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KEY PLAYERS

The major key players in the Synthetic Data Generation Market are Amazon.com, Inc., Microsoft Corporation, Gretel Labs, Mostly AI, NVIDIA Corporation, CVEDIA Inc., Synthesis AI, IBM Corporation, Datagen, Meta and other players.

NVIDIA Corporation-Company Financial Analysis

Company Landscape Analysis

RECENT DEVELOPMENTS

Microsoft:

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

Databricks:

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.

Synthetic Data Generation Market Report Scope:
Report Attributes Details
Market Size in 2023  US$ 0.2 bn
Market Size by 2031  US$ 2.5 bn
CAGR   CAGR of 36.0 % From 2024 to 2031
Base Year  2023
Forecast Period  2024-2031
Historical Data  2020-2022
Report Scope & Coverage Market Size, Segments Analysis, Competitive  Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments • By Data Type (Tabular Data, Text Data, Image and Video Data, Others)
• By Application (AI Training and Development, Test Data Management, Data Sharing and Retention, Data Analytics, Others)
• By Industry Vertical (BFSI, Healthcare and Life Sciences, Transportation, Logistics, Government, Defense, IT, Telecommunication, Manufacturing, Others)
Regional Analysis/Coverage North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America)
Company Profiles Amazon.com, Inc., Microsoft Corporation, Gretel Labs, Mostly AI, NVIDIA Corporation, CVEDIA Inc., Synthesis AI, IBM Corporation, Datagen, Meta 
Key Drivers • The market for synthetic data production has new growth potential due to the rising demand for IoT and connected devices.
• Increasing demand for data security and privacy, rising investment in advanced technologies drive the growth of the market.
Market Restraints • Synthetic data generation often relies on high-end generative models.

 

Frequently Asked Questions

Ans. The Synthetic Data Generation Market  is to grow at a CAGR of 36% over the forecast period 2024-2031.

Ans: The Synthetic Data Generation Market size was valued at US$ 0.2 million in 2023.

Ans. Amazon.com, Inc., CVEDIA Inc., Datagen, IBM Corporation, Meta, Microsoft Corporation, Mostly AI, NVIDIA Corporation Synthesis AI and others.

Ans. Generating Data According to a Known Distribution, Fitting Real Data to a Distribution, Neural Network Techniques, Synthetic Image Generation with Variationally Autoencoders, Synthetic Image Generation with Generative Adversarial Network. These techniques provide different approaches to generating synthetic data that mimics the statistical properties and characteristics of real data.

Ans. The ethical considerations surrounding the use of synthetic data emphasize the importance of fairness, privacy, transparency, and accountability. It is essential to address these considerations throughout the entire lifecycle of synthetic data, from its creation to its use in various applications.

TABLE OF CONTENTS

1. Introduction
1.1 Company Definition
1.2 Scope
1.3 Research Assumptions

2. Research Methodology

3. Market Dynamics
3.1 Drivers
3.2 Restraints
3.3 Opportunities
3.4 Challenges

4. Impact Analysis
4.1 Impact of Ukraine- Russia War
4.2 Impact of Recession
4.2.2.1 US
4.2.2.2 Canada
4.2.2.3 Germany
4.2.2.4 France
4.2.2.5 United Kingdom
4.2.2.6 China
4.2.2.7 Japan
4.2.2.8 South Korea
4.2.2.9 Rest of the World

5. Value Chain Analysis

6. Porter’s 5 forces model

7. PEST Analysis

8. Synthetic Data Generation Market, by Data Type
8.1 Tabular Data
8.2 Text Data
8.3 Image and Video Data
8.4 Others

9. Synthetic Data Generation Market, by Application
9.1 AI Training and Development
9.2 Test Data Management
9.3 Data Sharing and Retention
9.4 Data Analytics
9.5 Others

10. Synthetic Data Generation Market, by Industry Vertical
10.1 BFSI
10.2 Healthcare
10.3 Life Sciences
10.4 Transportation
10.5 Logistic
10.6 Government
10.7 Defense
10.8 IT
10.9 Telecommunication
10.10 Manufacturing
10.11 others

11. Regional Analysis
11.1 Introduction
11.2 North America
11.2.1North America Synthetic Data Generation Market by Country
11.2.2North America Synthetic Data Generation Market by Data Type
11.2.3 North America Synthetic Data Generation Market by Application
11.2.4 North America Synthetic Data Generation Market by Industry Vertical
11.2.5 USA
11.2.5.1 USA  Synthetic Data Generation Market By Data Type
11.2.5.2 USA  Synthetic Data Generation Market By Application
11.2.5.3 USA Synthetic Data Generation Market by Industry Vertical
11.2.6 Canada
11.2.6.1 Canada Synthetic Data Generation Market By Data Type
11.2.6.2 Canada Synthetic Data Generation Market By Application
11.2.6.3Canada Synthetic Data Generation Market by Industry Vertical
11.2.7 Mexico
11.2.7.1 Mexico Synthetic Data Generation Market By Data Type
11.2.7.2 Mexico Synthetic Data Generation Market By Application
11.2.7.3 Mexico Synthetic Data Generation Market by Industry Vertical
11.3 Europe
11.3.1 Europe Synthetic Data Generation Market by Country
1.3.3.2 Europe Synthetic Data Generation Market By Data Type
11.3.3 Europe Synthetic Data Generation Market By Application
11.3.4 Europe Synthetic Data Generation Market by Industry Vertical
11.3.5 Germany
11.3.5.1 Germany Synthetic Data Generation Market By Data Type
11.3.5.2 Germany Synthetic Data Generation Market By Application
11.3.5.3 Germany Synthetic Data Generation Market by Industry Vertical
11.3.6 UK
11.3.6.1 UK Synthetic Data Generation Market By Data Type
11.3.6.2 UK Synthetic Data Generation Market By Application
11.3.6.3 UK Synthetic Data Generation Market by Industry Vertical
11.3.7 France
11.3.7.1 France Synthetic Data Generation Market By Data Type
11.3.7.2 France Synthetic Data Generation Market By Application
11.3.7.3 France Synthetic Data Generation Market by Industry Vertical
11.3.8 Italy
11.3.8.1 Italy Synthetic Data Generation Market By Data Type
11.3.8.2 Italy Synthetic Data Generation Market By Application
11.3.8.3 Italy Synthetic Data Generation Market by Industry Vertical
11.3.9 Spain
11.3.9.1 Spain Synthetic Data Generation Market By Data Type
11.3.9.2 Spain Synthetic Data Generation Market By Application
11.3.9.3 Spain Synthetic Data Generation Market by Industry Vertical
11.3.10 The Netherlands
11.3.10.1 Netherlands Synthetic Data Generation Market By Data Type
11.3.10.2 Netherlands Synthetic Data Generation Market By Application
11.3.10.3 Netherlands Synthetic Data Generation Market by Industry Vertical
11.3.11 Rest of Europe
11.3.11.1 Rest of Europe Synthetic Data Generation Market By Data Type
11.3.11.2 Rest of Europe Synthetic Data Generation Market By Application
11.3.11.3 Rest of Europe Synthetic Data Generation Market by Industry Vertical
11.4 Asia-Pacific
11.4.1 Asia Pacific Synthetic Data Generation Market by Country
11.4.2 Asia Pacific Synthetic Data Generation Market By Data Type
11.4.3 Asia Pacific Synthetic Data Generation Market By Application
11.4.4 Asia Pacific Synthetic Data Generation Market by Industry Vertical
11.4.5 Japan
11.4.5.1 Japan Synthetic Data Generation Market By Data Type
11.4.5.2 Japan Synthetic Data Generation Market By Application
11.4.5.3 Japan Synthetic Data Generation Market by Industry Vertical
11.4.6 South Korea
11.4.6.1 South Korea Synthetic Data Generation Market By Data Type
11.4.6.2 South Korea Synthetic Data Generation Market By Application
11.4.6.3 South Korea Synthetic Data Generation Market by Industry Vertical
11.4.7 China
11.4.7.1 China Synthetic Data Generation Market By Data Type
11.4.7.2 China Synthetic Data Generation Market By Application
11.4.7.3 China Synthetic Data Generation Market by Industry Vertical
11.4.8 India
11.4.8.1 India Synthetic Data Generation Market By Data Type
11.4.8.2 India Synthetic Data Generation Market By Application
11.4.8.3 India Synthetic Data Generation Market by Industry Vertical
11.4.9 Australia
11.4.9.1 Australia Synthetic Data Generation Market By Data Type
11.4.9.2 Australia Synthetic Data Generation Market By Application
11.4.9.3 Australia Synthetic Data Generation Market by Industry Vertical
11.4.10 Rest of Asia-Pacific
11.4.10.1 APAC Synthetic Data Generation Market By Data Type
11.4.10.2 APAC Synthetic Data Generation Market By Application
11.4.10.3 APAC Synthetic Data Generation Market by Industry Vertical
11.5 The Middle East & Africa
11.5.1 The Middle East & Africa Synthetic Data Generation Market by Country
11.5.2 The Middle East & Africa Synthetic Data Generation Market By Data Type
11.5.3 The Middle East & Africa Synthetic Data Generation Market By Application
11.5.4 The Middle East & Africa Synthetic Data Generation Market by Industry Vertical
11.5.6 Israel
11.5.6.1 Israel Synthetic Data Generation Market By Data Type
11.5.6.2 Israel Synthetic Data Generation Market By Application
11.5.6.3 Israel Synthetic Data Generation Market by Industry Vertical
11.5.6 UAE
11.5.6.1 UAE Synthetic Data Generation Market By Data Type
11.5.6.2 UAE Synthetic Data Generation Market By Application
11.5.6.3 UAE Synthetic Data Generation Market by Industry Vertical
11.5.7 South Africa
11.5.7.1 South Africa Synthetic Data Generation Market By Data Type
11.5.7.2 South Africa Synthetic Data Generation Market By Application
11.5.7.3 South Africa Synthetic Data Generation Market by Industry Vertical
11.5.8 Rest of Middle East & Africa
11.5.8.1 Rest of Middle East & Asia Synthetic Data Generation Market By Data Type
11.5.8.2 Rest of Middle East & Asia Synthetic Data Generation Market By Application
11.5.8.3 Rest of Middle East & Asia Synthetic Data Generation Market by Industry Vertical
11.6 Latin America
11.6.1 Latin America Synthetic Data Generation Market by Country
11.6.2 Latin America Synthetic Data Generation Market By Data Type
11.6.3 Latin America Synthetic Data Generation Market By Application
11.6.4 Latin America Synthetic Data Generation Market by Industry Vertical
11.6.5 Brazil
11.6.5.1 Brazil Synthetic Data Generation Market By Data Type
11.6.5.2Brazil Synthetic Data Generation Market By Application
11.6.5.3 Brazil Synthetic Data Generation Market by Industry Vertical
11.6.6 Argentina
11.6.6.1 Argentina Synthetic Data Generation Market By Data Type
11.6.6.2 Argentina Synthetic Data Generation Market By Application
11.6.6.3 Argentina Synthetic Data Generation Market by Industry Vertical
11.6.7 Rest of Latin America
11.6.7.1 Rest of Latin America Synthetic Data Generation Market By Data Type
11.6.7.2 Rest of Latin America Synthetic Data Generation Market By Application
11.6.7.3 Rest of Latin America Synthetic Data Generation Market by Industry Vertical

12. Company profile
12.1 Amazon.com, Inc.
12.1.1 Company Overview
12.1.2 Financials
12.1.3Product/Services/Offerings
12.1.4 SWOT Analysis
12.1.5 The SNS View
12.2 Microsoft Corporation
12.2.1 Company Overview
12.2.2 Financials
12.2.3Product/Services/Offerings
12.2.4 SWOT Analysis
12.2.5 The SNS View
12.3 Gretel Labs
12.3.1 Company Overview
12.3.2 Financials
12.3.3Product/Services/Offerings
12.3.4 SWOT Analysis
12.3.5 The SNS View
12.4 Mostly AI
12.4.1 Company Overview
12.4.2 Financials
12.4.3Product/Services/Offerings
12.4.4 SWOT Analysis
12.4.5 The SNS View
12.5 NVIDIA Corporation
12.5.1 Company Overview
12.5.2 Financials
12.5.3Product/Services/Offerings
12.5.4 SWOT Analysis
12.5.5 The SNS View
12.6 CVEDIA Inc.
12.6.1 Company Overview
12.6.2 Financials
12.6.3Product/Services/Offerings
12.6.4 SWOT Analysis
12.6.5 The SNS View
12.7 Synthesis AI
12.7.1 Company Overview
12.7.2 Financials
12.7.3Product/Services/Offerings
12.7.4 SWOT Analysis
12.7.5 The SNS View
12.8. IBM Corporation
12.8.1 Company Overview
12.8.2 Financial
12.8.3Product/Services/Offerings
12.8.4 SWOT Analysis
12.8.5 The SNS View
12.9. Datagen
12.9.1 Company Overview
12.9.2 Financials
12.9.3 Product/Service/Offerings
12.9.4 SWOT Analysis
12.9.5 The SNS View
12.10 Meta
12.10.1 Company Overview
12.10.2 Financials
12.10.3 Product/Service/Offerings
12.10.4 SWOT Analysis
12.10.5 The SNS View

13. Competitive Landscape
13.1 Competitive Bench marking
13.2 Market Share Analysis
13.3 Recent Developments
13.3.1 Industry News
13.3.2 Company News
13.3.3 Mergers & Acquisitions

14. Use Case and Best Practices

15. Conclusion

An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.

Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.

 

The 5 steps process:

Step 1: Secondary Research:

Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.

Secondary Research

Step 2: Primary Research

When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data.  This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.

We at SNS Insider have divided Primary Research into 2 parts.

Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.

This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.

Primary Research

Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.

Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.

Step 3: Data Bank Validation

Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.

Data Bank Validation

Step 4: QA/QC Process

After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.

Step 5: Final QC/QA Process:

This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.

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