The Neural Network Software Market is anticipated to develop at a CAGR of 24.9% from 2024 to 2031, from a value of USD 25.7 Billion in 2023 to USD 152.3 Billion by 2031.
The increasing adoption of Industry 4.0 and its associated technologies is a key driver behind the projected growth of the global neural network software market. Industry 4.0 encompasses the integration of cyber-physical systems, the Internet of Things (IoT), cloud computing, and cognitive computing, among other technologies. These advancements enable businesses to leverage neural network software to analyze vast amounts of data, identify patterns, and make informed decisions. The primary catalyst for the growth of the neural network market is the proliferation of data archiving tools that effectively organize the vast amount of unstructured data generated by various end users. Additionally, there is a surging demand for predictive solutions and a widespread adoption of digital technologies, further fueling the growth of the neural network software market. Several key factors contribute to the increasing demand for predictive solutions, including an exponential surge in data volume, the rapid pace of digitization, stringent regulations, and financial losses resulting from the rise in fraudulent practices. However, the slow rate of digitization in emerging economies, coupled with a lack of technical expertise and operational challenges, pose significant obstacles to market growth.
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Drivers
The development of the worldwide neural network market is aided by developments in the artificial intelligence (AI) industry and the rise of cloud disruption in contemporary business.
The availability of cutting-edge analytical tools and forecasting solutions has a positive effect on the market's expansion.
Artificial intelligence (AI) systems utilize neural networks to facilitate learning, reasoning, and self-correction. Expert systems, speech recognition, and machine vision are some specific uses of AI. The current rise in popularity of AI is due to difficult projects involving cloud computing and large data infrastructure. Prominent companies spanning various industries, such as Software & IT giants like Microsoft, Google, and Amazon, Financial Services leaders like American Express, and Bloomberg, and Automotive pioneers like Tesla and Ford, have recognized the immense potential of Artificial Intelligence (AI) as a pivotal force. Consequently, they have embarked on substantial investments in neural networks to develop cutting-edge systems that promise unparalleled sophistication. Additionally, these leading businesses have helped young startups by offering funding in order to develop fresh, creative neural search platforms. For instance, the open-source firm Jina.ai, based in Berlin, raised $29 million in a series A funding round in 2021. Canaan Partners sponsored a fundraising round for a company that provides information-finding tools for unstructured data.
Restrains
The market's expansion is hampered by a large demand and high reliance on data.
Opportunities
Deep neural network applications are projected to expand, providing lucrative prospects.
Challenges
Slow reaction to operational issues due to a lack of global experience limits market growth
The market growth is being hindered by a lack of global experience, resulting in a slow response to operational issues. This limitation not only affects the efficiency of the business but also hampers its overall progress. To overcome this challenge, it is crucial to enhance our understanding of global markets and develop a more proactive approach to addressing operational issues. By doing so, we can unlock new opportunities for growth and ensure a more successful and sustainable future for our business.
Ukraine has a pool of highly skilled software engineers and data scientists, many of whom contribute to the development of neural network software. If the conflict forces a significant number of these professionals to flee or halt their work, it could lead to talent shortages in the industry. Political instability and armed conflict can create economic uncertainty, which may affect businesses' willingness to invest in research and development, including neural network software. Companies may become more cautious in their spending, which could slow market growth. The Russia-Ukraine conflict may lead to geopolitical tensions that affect international collaborations and partnerships in the tech industry. This could limit cooperation on AI research and software development. Cybersecurity concerns may increase due to the conflict, leading to more significant investments in AI-based security solutions. Neural network software could play a role in enhancing cybersecurity measures. Governments involved in the conflict may increase their investments in AI and neural network software for defense and surveillance purposes. This could create a niche market for specialized software providers. Some companies and organizations may take a stance on humanitarian and ethical grounds and reduce or reconsider their business relationships with entities associated with the conflict.
Impact of Recession
Companies may delay or postpone projects that involve implementing neural network software or artificial intelligence (AI) solutions due to economic uncertainty. This can slow down the growth of the neural network software market. Neural network software startups, which often rely on venture capital and funding, may face difficulties in raising funds during a recession. This can hinder their ability to develop and market innovative software solutions. The impact of a recession on the neural network software market can vary by industry. Some sectors, such as healthcare and finance, may continue to invest in AI and neural network software to improve efficiency and competitiveness, while others may cut back. Companies that continue to invest in technology during a recession may prioritize cost-effective solutions. This could drive demand for neural network software that offers efficiency improvements and cost savings. Government stimulus packages or initiatives to boost economic recovery may include funding for technology and AI projects. This can positively influence the neural network software market in some regions. Recessions can accelerate trends such as remote work and automation. Neural network software that supports remote collaboration, enhances cybersecurity or automates routine tasks may see increased demand.
By Type
Data Mining and Archiving
Analytical Software
Optimization Software
Visualization Software
By Application
Hardware Diagnostics
Financial Forecasting
Image Optimization
Others
By Component
Neural Network Software
Services
Platform and Other Enabling Services
By Vertical
BFSI
Government and Defense
Energy and Utilities
Healthcare
Industrial Manufacturing
Media
Telecom and IT
Transportation and Logistics
Retail and eCommerce
Others
The global neural network software market segment comprises neural network software, services, and platforms. The emergence of neural network software has been a groundbreaking development in the fields of artificial intelligence (AI) and machine learning (ML). Neural networks are a class of algorithms that draw inspiration from the structure and functioning of the human brain, enabling them to learn patterns and relationships from data. Deep learning, a subset of AI that focuses on training multi-layered neural networks, is built upon the foundation of neural networks. Recent advancements in deep learning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have greatly enhanced various AI applications, including computer vision, natural language processing, and speech recognition.
Regional Analysis
The U.S. contributed the most revenue 15% to the global market for neural network software in North America, and it is anticipated that the area would experience a substantial increase over the projected period. The presence of many market participants and the demand for solutions from the automobile industry, particularly from the U.S., are anticipated to propel the regional market's expansion. The United Kingdom, France, and Germany are the European nations with the biggest revenue contributions. China, India, and Japan all have high adoption rates in the region, which is anticipated to fuel the Asia Pacific market for neural network software. Saudi Arabia, the United Arab Emirates (UAE), and South Africa are among the Gulf Cooperation Council (GCC) nations that generate the most money from MEA. The growth of market participants' investments in the region is driving the South American neural network software market. Brazil is the country in the region that contributes the most money to the market for neural network software.
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
The major players in the market are Qualcomm Technologies Inc., IBM, Intel Corporation, Google Inc., Ward Systems Group Inc., Microsoft Corporation, Neuralware, SAP SE, Oracle Corporation, Alyuda Research LLC, Neural Technologies Ltd., Starmind International AG, Slagkryssaren AB, Afiniti, GMDH LLC, and others in the final report.
To expand its leadership in AI and connected intelligent edge, Qualcomm Technologies consolidated its current best-in-class AI software offerings into a single package, the Qualcomm AI Stack, in June 2022. In order to fully exploit the performance of our Qualcomm AI Engine when developing, improving, and deploying their AI applications on Qualcomm Technologies' hardware, this will enable Qualcomm Technologies' OEM clients and developers.
NVIDIA Corporation releases updates to the CUDA-X AI Libraries on February 10, 2021. A set of deep neural network primitives is GPU-accelerated in the new NVIDIA CUDA Deep Neural Network library (cuDNN).
On December 28, 2020, IBM will start offering training for neural networks on smartphones. Large server farms and a lot of energy are needed for neural network training. By reducing the number of bits needed to perform their computations, a new IBM technique suggests that they may be able to significantly cut that.
The Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN), an open-source, performance-improving library that has been heavily abstracted to enable developers to use DL frameworks with improved performance on Intel hardware, will be released by Intel Corporation on January 29, 2020.
| Report Attributes | Details |
| Market Size in 2023 | US$ 25.7 Bn |
| Market Size by 2031 | US$ 152.3 Bn |
| CAGR | CAGR of 24.9% 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 Type (Data Mining and Archiving, Analytical Software, Optimization Software, Visualization Software) • By Application (Fraud Detection, Hardware Diagnostics, Financial Forecasting, Image Optimization, Others) • By Component (Neural Network Software, Services, Platform, and Other Enabling Services) • By Vertical (BFSI, Government and Defense, Energy and Utilities, Healthcare, Industrial Manufacturing, Media, Telecom and IT, Transportation and Logistics, Retail and e-commerce, 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 | Qualcomm Technologies Inc., IBM, Intel Corporation, Google Inc., Ward Systems Group Inc., Microsoft Corporation, Neuralware, SAP SE, Oracle Corporation, Alyuda Research LLC, Neural Technologies Ltd., Starmind International AG, Slagkryssaren AB, Afiniti, GMDH LLC |
| Key Drivers | • The development of the worldwide neural network market is aided by developments in the artificial intelligence (AI) industry and the rise of cloud disruption in contemporary business. • The availability of cutting-edge analytical tools and forecasting solutions has a positive effect on the market's expansion. |
| Market Restraints | • The market's expansion is hampered by a large demand and high reliance on data. |
The CAGR of the Neural Network Software Market for the forecast period 2022-2030 is 32.1%.
The market is expected to grow to USD 173.5 billion by the forecast period of 2030.
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North America region dominates the Neural Network Software Market.
Table of Contents
1. Introduction
1.1 Market 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 Russia-Ukraine War
4.2 Impact of Ongoing Recession
4.2.1 Introduction
4.2.2 Impact on major economies
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. Neural Network Software Market Segmentation, By Type
8.1 Data Mining and Archiving
8.2 Analytical Software
8.3 Optimization Software
8.4 Visualization Software
9. Neural Network Software Market Segmentation, By Application
9.1 Fraud Detection
9.2 Hardware Diagnostics
9.3 Financial Forecasting
9.4 Image Optimization
9.5 Other Applications
10. Neural Network Software Market Segmentation, By Component
10.1 Neural Network Software
10.2 Services
10.3 Platform and Other Enabling Services
11. Neural Network Software Market Segmentation, By Vertical
11.1 BFSI
11.2 Government and Defense
11.3 Energy and Utilities
11.4 Healthcare
11.5 Industrial Manufacturing
11.6 Media
11.7 Telecom and IT
11.8 Transportation and Logistics
11.9 Retail and eCommerce
11.10 Others
12. Regional Analysis
12.1 Introduction
12.2 North America
12.2.1 North America Neural Network Software Market By Country
12.2.2 North America Neural Network Software Market By Type
12.2.3 North America Neural Network Software Market By Application
12.2.4 North America Neural Network Software Market By Component
12.2.5 North America Neural Network Software Market By Vertical
12.2.6 USA
12.2.6.1 USA Neural Network Software Market By Type
12.2.6.2 USA Neural Network Software Market By Application
12.2.6.3 USA Neural Network Software Market By Component
12.2.6.4 USA Neural Network Software Market By Vertical
12.2.7 Canada
12.2.7.1 Canada Neural Network Software Market By Type
12.2.7.2 Canada Neural Network Software Market By Application
12.2.7.3 Canada Neural Network Software Market By Component
12.2.7.4 Canada Neural Network Software Market By Vertical
12.2.8 Mexico
12.2.8.1 Mexico Neural Network Software Market By Type
12.2.8.2 Mexico Neural Network Software Market By Application
12.2.8.3 Mexico Neural Network Software Market By Component
12.2.8.4 Mexico Neural Network Software Market By Vertical
12.3 Europe
12.3.1 Eastern Europe
12.3.1.1 Eastern Europe Neural Network Software Market By Country
12.3.1.2 Eastern Europe Neural Network Software Market By Type
12.3.1.3 Eastern Europe Neural Network Software Market By Application
12.3.1.4 Eastern Europe Neural Network Software Market By Component
12.3.1.5 Eastern Europe Neural Network Software Market By Vertical
12.3.1.6 Poland
12.3.1.6.1 Poland Neural Network Software Market By Type
12.3.1.6.2 Poland Neural Network Software Market By Application
12.3.1.6.3 Poland Neural Network Software Market By Component
12.3.1.6.4 Poland Neural Network Software Market By Vertical
12.3.1.7 Romania
12.3.1.7.1 Romania Neural Network Software Market By Type
12.3.1.7.2 Romania Neural Network Software Market By Application
12.3.1.7.3 Romania Neural Network Software Market By Component
12.3.1.7.4 Romania Neural Network Software Market By Vertical
12.3.1.8 Hungary
12.3.1.8.1 Hungary Neural Network Software Market By Type
12.3.1.8.2 Hungary Neural Network Software Market By Application
12.3.1.8.3 Hungary Neural Network Software Market By Component
12.3.1.8.4 Hungary Neural Network Software Market By Vertical
12.3.1.9 Turkey
12.3.1.9.1 Turkey Neural Network Software Market By Type
12.3.1.9.2 Turkey Neural Network Software Market By Application
12.3.1.9.3 Turkey Neural Network Software Market By Component
12.3.1.9.4 Turkey Neural Network Software Market By Vertical
12.3.1.10 Rest of Eastern Europe
12.3.1.10.1 Rest of Eastern Europe Neural Network Software Market By Type
12.3.1.10.2 Rest of Eastern Europe Neural Network Software Market By Application
12.3.1.10.3 Rest of Eastern Europe Neural Network Software Market By Component
12.3.1.10.4 Rest of Eastern Europe Neural Network Software Market By Vertical
12.3.2 Western Europe
12.3.2.1 Western Europe Neural Network Software Market By Country
12.3.2.2 Western Europe Neural Network Software Market By Type
12.3.2.3 Western Europe Neural Network Software Market By Application
12.3.2.4 Western Europe Neural Network Software Market By Component
12.3.2.5 Western Europe Neural Network Software Market By Vertical
12.3.2.6 Germany
12.3.2.6.1 Germany Neural Network Software Market By Type
12.3.2.6.2 Germany Neural Network Software Market By Application
12.3.2.6.3 Germany Neural Network Software Market By Component
12.3.2.6.4 Germany Neural Network Software Market By Vertical
12.3.2.7 France
12.3.2.7.1 France Neural Network Software Market By Type
12.3.2.7.2 France Neural Network Software Market By Application
12.3.2.7.3 France Neural Network Software Market By Component
12.3.2.7.4 France Neural Network Software Market By Vertical
12.3.2.8 UK
12.3.2.8.1 UK Neural Network Software Market By Type
12.3.2.8.2 UK Neural Network Software Market By Application
12.3.2.8.3 UK Neural Network Software Market By Component
12.3.2.8.4 UK Neural Network Software Market By Vertical
12.3.2.9 Italy
12.3.2.9.1 Italy Neural Network Software Market By Type
12.3.2.9.2 Italy Neural Network Software Market By Application
12.3.2.9.3 Italy Neural Network Software Market By Component
12.3.2.9.4 Italy Neural Network Software Market By Vertical
12.3.2.10 Spain
12.3.2.10.1 Spain Neural Network Software Market By Type
12.3.2.10.2 Spain Neural Network Software Market By Application
12.3.2.10.3 Spain Neural Network Software Market By Component
12.3.2.10.4 Spain Neural Network Software Market By Vertical
12.3.2.11 Netherlands
12.3.2.11.1 Netherlands Neural Network Software Market By Type
12.3.2.11.2 Netherlands Neural Network Software Market By Application
12.3.2.11.3 Netherlands Neural Network Software Market By Component
12.3.2.11.4 Netherlands Neural Network Software Market By Vertical
12.3.2.12 Switzerland
12.3.2.12.1 Switzerland Neural Network Software Market By Type
12.3.2.12.2 Switzerland Neural Network Software Market By Application
12.3.2.12.3 Switzerland Neural Network Software Market By Component
12.3.2.12.4 Switzerland Neural Network Software Market By Vertical
12.3.2.13 Austria
12.3.2.13.1 Austria Neural Network Software Market By Type
12.3.2.13.2 Austria Neural Network Software Market By Application
12.3.2.13.3 Austria Neural Network Software Market By Component
12.3.2.13.4 Austria Neural Network Software Market By Vertical
12.3.2.14 Rest of Western Europe
12.3.2.14.1 Rest of Western Europe Neural Network Software Market By Type
12.3.2.14.2 Rest of Western Europe Neural Network Software Market By Application
12.3.2.14.3 Rest of Western Europe Neural Network Software Market By Component
12.3.2.14.4 Rest of Western Europe Neural Network Software Market By Vertical
12.4 Asia-Pacific
12.4.1 Asia Pacific Neural Network Software Market By Country
12.4.2 Asia Pacific Neural Network Software Market By Type
12.4.3 Asia Pacific Neural Network Software Market By Application
12.4.4 Asia Pacific Neural Network Software Market By Component
12.4.5 Asia Pacific Neural Network Software Market By Vertical
12.4.6 China
12.4.6.1 China Neural Network Software Market By Type
12.4.6.2 China Neural Network Software Market By Application
12.4.6.3 China Neural Network Software Market By Component
12.4.6.4 China Neural Network Software Market By Vertical
12.4.7 India
12.4.7.1 India Neural Network Software Market By Type
12.4.7.2 India Neural Network Software Market By Application
12.4.7.3 India Neural Network Software Market By Component
12.4.7.4 India Neural Network Software Market By Vertical
12.4.8 Japan
12.4.8.1 Japan Neural Network Software Market By Type
12.4.8.2 Japan Neural Network Software Market By Application
12.4.8.3 Japan Neural Network Software Market By Component
12.4.8.4 Japan Neural Network Software Market By Vertical
12.4.9 South Korea
12.4.9.1 South Korea Neural Network Software Market By Type
12.4.9.2 South Korea Neural Network Software Market By Application
12.4.9.3 South Korea Neural Network Software Market By Component
12.4.9.4 South Korea Neural Network Software Market By Vertical
12.4.10 Vietnam
12.4.10.1 Vietnam Neural Network Software Market By Type
12.4.10.2 Vietnam Neural Network Software Market By Application
12.4.10.3 Vietnam Neural Network Software Market By Component
12.4.10.4 Vietnam Neural Network Software Market By Vertical
12.4.11 Singapore
12.4.11.1 Singapore Neural Network Software Market By Type
12.4.11.2 Singapore Neural Network Software Market By Application
12.4.11.3 Singapore Neural Network Software Market By Component
12.4.11.4 Singapore Neural Network Software Market By Vertical
12.4.12 Australia
12.4.12.1 Australia Neural Network Software Market By Type
12.4.12.2 Australia Neural Network Software Market By Application
12.4.12.3 Australia Neural Network Software Market By Component
12.4.12.4 Australia Neural Network Software Market By Vertical
12.4.13 Rest of Asia-Pacific
12.4.13.1 Rest of Asia-Pacific Neural Network Software Market By Type
12.4.13.2 Rest of Asia-Pacific Neural Network Software Market By Application
12.4.13.3 Rest of Asia-Pacific Neural Network Software Market By Component
12.4.13.4 Rest of Asia-Pacific Neural Network Software Market By Vertical
12.5 Middle East & Africa
12.5.1 Middle East
12.5.1.1 Middle East Neural Network Software Market By Country
12.5.1.2 Middle East Neural Network Software Market By Type
12.5.1.3 Middle East Neural Network Software Market By Application
12.5.1.4 Middle East Neural Network Software Market By Component
12.5.1.5 Middle East Neural Network Software Market By Vertical
12.5.1.6 UAE
12.5.1.6.1 UAE Neural Network Software Market By Type
12.5.1.6.2 UAE Neural Network Software Market By Application
12.5.1.6.3 UAE Neural Network Software Market By Component
12.5.1.6.4 UAE Neural Network Software Market By Vertical
12.5.1.7 Egypt
12.5.1.7.1 Egypt Neural Network Software Market By Type
12.5.1.7.2 Egypt Neural Network Software Market By Application
12.5.1.7.3 Egypt Neural Network Software Market By Component
12.5.1.7.4 Egypt Neural Network Software Market By Vertical
12.5.1.8 Saudi Arabia
12.5.1.8.1 Saudi Arabia Neural Network Software Market By Type
12.5.1.8.2 Saudi Arabia Neural Network Software Market By Application
12.5.1.8.3 Saudi Arabia Neural Network Software Market By Component
12.5.1.8.4 Saudi Arabia Neural Network Software Market By Vertical
12.5.1.9 Qatar
12.5.1.9.1 Qatar Neural Network Software Market By Type
12.5.1.9.2 Qatar Neural Network Software Market By Application
12.5.1.9.3 Qatar Neural Network Software Market By Component
12.5.1.9.4 Qatar Neural Network Software Market By Vertical
12.5.1.10 Rest of Middle East
12.5.1.10.1 Rest of Middle East Neural Network Software Market By Type
12.5.1.10.2 Rest of Middle East Neural Network Software Market By Application
12.5.1.10.3 Rest of Middle East Neural Network Software Market By Component
12.5.1.10.4 Rest of Middle East Neural Network Software Market By Vertical
12.5.2. Africa
12.5.2.1 Africa Neural Network Software Market By Country
12.5.2.2 Africa Neural Network Software Market By Type
12.5.2.3 Africa Neural Network Software Market By Application
12.5.2.4 Africa Neural Network Software Market By Component
12.5.2.5 Africa Neural Network Software Market By Vertical
12.5.2.6 Nigeria
12.5.2.6.1 Nigeria Neural Network Software Market By Type
12.5.2.6.2 Nigeria Neural Network Software Market By Application
12.5.2.6.3 Nigeria Neural Network Software Market By Component
12.5.2.6.4 Nigeria Neural Network Software Market By Vertical
12.5.2.7 South Africa
12.5.2.7.1 South Africa Neural Network Software Market By Type
12.5.2.7.2 South Africa Neural Network Software Market By Application
12.5.2.7.3 South Africa Neural Network Software Market By Component
12.5.2.7.4 South Africa Neural Network Software Market By Vertical
12.5.2.8 Rest of Africa
12.5.2.8.1 Rest of Africa Neural Network Software Market By Type
12.5.2.8.2 Rest of Africa Neural Network Software Market By Application
12.5.2.8.3 Rest of Africa Neural Network Software Market By Component
12.5.2.8.4 Rest of Africa Neural Network Software Market By Vertical
12.6. Latin America
12.6.1 Latin America Neural Network Software Market By Country
12.6.2 Latin America Neural Network Software Market By Type
12.6.3 Latin America Neural Network Software Market By Application
12.6.4 Latin America Neural Network Software Market By Component
12.6.5 Latin America Neural Network Software Market By Vertical
12.6.6 Brazil
12.6.6.1 Brazil Neural Network Software Market By Type
12.6.6.2 Brazil Neural Network Software Market By Application
12.6.6.3 Brazil Neural Network Software Market By Component
12.6.6.4 Brazil Neural Network Software Market By Vertical
12.6.7 Argentina
12.6.7.1 Argentina Neural Network Software Market By Type
12.6.7.2 Argentina Neural Network Software Market By Application
12.6.7.3 Argentina Neural Network Software Market By Component
12.6.7.4 Argentina Neural Network Software Market By Vertical
12.6.8 Colombia
12.6.8.1 Colombia Neural Network Software Market By Type
12.6.8.2 Colombia Neural Network Software Market By Application
12.6.8.3 Colombia Neural Network Software Market By Component
12.6.8.4 Colombia Neural Network Software Market By Vertical
12.6.9 Rest of Latin America
12.6.9.1 Rest of Latin America Neural Network Software Market By Type
12.6.9.2 Rest of Latin America Neural Network Software Market By Application
12.6.9.3 Rest of Latin America Neural Network Software Market By Component
12.6.9.4 Rest of Latin America Neural Network Software Market By Vertical
13 Company Profile
13.1 Qualcomm Technologies, Inc.
13.1.1 Company Overview
13.1.2 Financials
13.1.3 Product/Services/Offerings
13.1.4 SWOT Analysis
13.1.5 The SNS View
13.2 IBM.
13.2.1 Company Overview
13.2.2 Financials
13.2.3 Product/Services/Offerings
13.2.4 SWOT Analysis
13.2.5 The SNS View
13.3 Intel Corporation.
13.3.1 Company Overview
13.3.2 Financials
13.3.3 Product/Services/Offerings
13.3.4 SWOT Analysis
13.3.5 The SNS View
13.4 Google Inc.
13.4.1 Company Overview
13.4.2 Financials
13.4.3 Product/Services/Offerings
13.4.4 SWOT Analysis
13.4.5 The SNS View
13.5 Ward Systems Group Inc.
13.5.1 Company Overview
13.5.2 Financials
13.5.3 Product/Services/Offerings
13.5.4 SWOT Analysis
13.5.5 The SNS View
13.6 Microsoft Corporation.
13.6.1 Company Overview
13.6.2 Financials
13.6.3 Product/Services/Offerings
13.6.4 SWOT Analysis
13.6.5 The SNS View
13.7 Neuralware.
13.7.1 Company Overview
13.7.2 Financials
13.7.3 Product/Services/Offerings
13.7.4 SWOT Analysis
13.7.5 The SNS View
13.8 SAP SE.
13.8.1 Company Overview
13.8.2 Financials
13.8.3 Product/Services/Offerings
13.8.4 SWOT Analysis
13.8.5 The SNS View
13.9 Oracle Corporation.
13.9.1 Company Overview
13.9.2 Financials
13.9.3 Product/Services/Offerings
13.9.4 SWOT Analysis
13.9.5 The SNS View
13.10 Neural Technologies Ltd.
13.10.1 Company Overview
13.10.2 Financials
13.10.3 Product/Services/Offerings
13.10.4 SWOT Analysis
13.10.5 The SNS View
14. Competitive Landscape
14.1 Competitive Benchmarking
14.2 Market Share Analysis
14.3 Recent Developments
14.3.1 Industry News
14.3.2 Company News
14.3 Mergers & Acquisitions
15. USE Cases and Best Practices
16. 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.
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.
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.
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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