Survey of innovation, logging and manufacturing industries, percentage of the plant's total revenue that came from the most important customer or client who was not a part of the firm by type of plant, percentage range of the plant's total revenue and the North American Industry Classification System (NAICS) for Canada, provinces and territories in 2005. (Terminated)

According to Cognitive Market Research, the global GPU Database market size will be USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. Market Dynamics of GPU Database Market Key Drivers for GPU Database Market Growing Demand for High-Performance Computing in Various Data-Intensive Industries- One of the main reasons the GPU Database market is growing demand for high-performance computing (HPC) across various data-intensive industries. These industries, including finance, healthcare, and telecommunications, require rapid data processing and real-time analytics, which GPU databases excel at providing. Unlike traditional CPU databases, GPU databases leverage the parallel processing power of GPUs to handle complex queries and large datasets more efficiently. This capability is crucial for applications such as machine learning, artificial intelligence, and big data analytics. The expansion of data and the increasing need for speed and scalability in processing are pushing enterprises to adopt GPU databases. Consequently, the market is poised for robust growth as organizations continue to seek solutions that offer enhanced performance, reduced latency, and greater computational power to meet their evolving data management needs. The increasing demand for gaining insights from large volumes of data generated across verticals to drive the GPU Database market's expansion in the years ahead. Key Restraints for GPU Database Market Lack of efficient training professionals poses a serious threat to the GPU Database industry. The market also faces significant difficulties related to insufficient security options. Introduction of the GPU Database Market The GPU database market is experiencing rapid growth due to the increasing demand for high-performance data processing and analytics. GPUs, or Graphics Processing Units, excel in parallel processing, making them ideal for handling large-scale, complex data sets with unprecedented speed and efficiency. This market is driven by the proliferation of big data, advancements in AI and machine learning, and the need for real-time analytics across industries such as finance, healthcare, and retail. Companies are increasingly adopting GPU-accelerated databases to enhance data visualization, predictive analytics, and computational workloads. Key players in this market include established tech giants and specialized startups, all contributing to a competitive landscape marked by innovation and strategic partnerships. As organizations continue to seek faster and more efficient ways to harness their data, the GPU database market is poised for substantial growth, reshaping the future of data management and analytics.< /p>

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

The dataset provides information about India's largest companies ranked by their size, revenue, or any other relevant metric. It offers insights into the business landscape and highlights the leading companies that contribute significantly to the Indian economy.

Key features of the dataset may include:

Company Name: The names of the largest companies in India based on their ranking. Rank: The ranking assigned to each company based on their size or other criteria.

Industry Sector: The sector or industry to which the company belongs, such as technology, finance, healthcare, energy, etc.

Revenue: The company's annual revenue or turnover, indicating its financial performance.

Market Capitalization: The market value of the company's outstanding shares, providing insights into its overall worth.

Headquarters: The location of the company's main headquarters or registered office.

Brief Description: A concise overview or description of the company's business activities, products, or services.

This dataset can be useful for various purposes, including market analysis, investment research, economic studies, and understanding the dynamics of India's corporate sector. Researchers, analysts, investors, and policymakers can leverage this dataset to gain insights into the performance and growth of India's largest companies, track industry trends, identify potential investment opportunities, and assess the overall health of the Indian business ecosystem.

According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
Data Annotation category is the fastest growing segment of the AI Training Dataset Market

Market Dynamics of AI Training Dataset Market

Key Drivers for AI Training Dataset Market

Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

Restraint Factor for the AI Training Dataset Market

Data Privacy Regulations Impeding AI Training Dataset Market Growth

Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

Success.ai’s Firmographic Data API empowers organizations to make data-driven decisions with on-demand access to detailed insights on over 70 million companies worldwide. Covering key firmographic attributes like industry classifications, revenue size, and employee count, this API ensures your market analysis, strategic planning, and competitive benchmarking efforts are backed by continuously updated, AI-validated information.

Whether you’re exploring new markets, refining your product offerings, or optimizing partner relationships, Success.ai’s Firmographic Data API delivers the intelligence you need. Supported by our Best Price Guarantee, this solution helps you confidently navigate the global business landscape.

Why Choose Success.ai’s Firmographic Data API?

1. Detailed, Verified Firmographic Data

   • Access comprehensive company attributes including industries, revenue ranges, and headcount.
   • AI-driven validation ensures 99% accuracy, minimizing errors and fostering informed decision-making.

2. Extensive Global Coverage

   • Includes profiles of companies from North America, Europe, Asia-Pacific, and beyond.
   • Scale your strategies by tapping into emerging markets, niche sectors, and diverse geographies.

3. Continuous Data Updates

   • Receive real-time updates to keep pace with changing organizational structures, market expansions, and acquisitions.
   • Always rely on current data to guide product roadmaps, growth plans, and strategic partnerships.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible, lawful data usage for every query.

Data Highlights:

• 70M+ Verified Company Profiles: Gain clarity on businesses spanning all major industries and regions.
• Industry Classifications: Filter companies by their sector focus, from manufacturing to technology.
• Revenue and Employee Counts: Understand company sizes, growth potential, and market reach.
• Global Market Insights: Use firmographic data to inform product launches, expansions, and strategic alliances.

Key Features of the Firmographic Data API:

1. Real-Time Company Enrichment

   • Enhance your CRM or analytics platforms with verified firmographic data, eliminating guesswork and manual data imports.
   • Update records automatically as companies grow, diversify, or shift their market focus.

2. Advanced Filtering and Query Capabilities

   • Query the API for specific parameters like industry vertical, company size, or geographic location.
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3. Scalability and Flexibility

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4. AI-Validated Accuracy and Reliability

   • Rely on an AI-powered validation process that continually verifies data integrity.
   • Increase confidence in strategic decisions backed by accurate, current, and relevant information.

Strategic Use Cases:

1. Market Analysis and Competitive Benchmarking

   • Identify industries poised for growth, evaluate emerging markets, and benchmark against competitor profiles.
   • Refine go-to-market strategies and product launches based on solid data rather than assumptions.

2. Strategic Partnering and M&A Efforts

   • Explore potential partners, suppliers, or acquisition targets that match your criteria, from revenue tiers to geographic presence.
   • Shorten due diligence cycles with reliable, on-demand firmographic insights.

3. Sales and Account-Based Marketing

   • Segregate target accounts by industry, size, and region to tailor outreach and messaging.
   • Personalize campaigns, improve lead quality, and increase win rates through better audience alignment.

4. Product Roadmapping and Portfolio Management

   • Inform product development by identifying high-growth verticals or underpenetrated regions.
   • Allocate resources effectively and prioritize product enhancements based on firmographic-driven insights.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality firmographic data at competitive prices, ensuring optimal ROI for your research and strategic planning.

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3. Data Accuracy with AI Validation

   • Depend on 99% accuracy to guide data-driven decisions, refine targeting, and boost strategic outcomes.

4. Customizable and Scalable Solutions

   • Tailor the dataset to specific industries, regions, or revenue segments as your business evolves and market conditions shift.

Additional APIs for Enhanced Functionality:

1. Data Enrichment API...

🌍 Europe B2B Company Dataset | 30M+ Verified Records | Firmographics & API Access Power your sales, marketing, and investment strategies with the most comprehensive global B2B company data—verified, AI-driven, and updated bi-weekly.

The Forager.ai Global Company Dataset delivers 30M+ high-quality firmographic records, covering public and private companies worldwide. Leveraging AI-powered validation and bi-weekly updates, our dataset ensures accuracy, freshness, and depth—making it ideal for sales intelligence, market analysis, and CRM enrichment.

📊 Key Features & Coverage ✅ 30M+ Company Records – The largest, most reliable B2B firmographic dataset available. ✅ Bi-Weekly Updates – Stay ahead with refreshed data every two weeks. ✅ AI-Driven Accuracy – Sophisticated algorithms verify and enrich every record. ✅ Global Coverage – Companies across North America, Europe, APAC, and emerging markets.

📋 Core Data Fields: ✔ Company Name, LinkedIn URL, & Domain ✔ Industries ✔ Job postings, Revenue, Employee Size, Funding Status ✔ Location (HQ + Regional Offices) ✔ Tech Stack & Firmographic Signals ✔ LinkedIn Profile details

🎯 Top Use Cases 🔹 Sales & Lead Generation

Build targeted prospect lists using firmographics (size, industry, revenue).

Enhance lead scoring with technographic insights.

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Track company growth, expansions, and trends.

Benchmark competitors using real-time private company data.

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Discover investment opportunities with granular sector-level insights.

Monitor portfolio companies and industry shifts.

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Enrich CRM data for hyper-targeted campaigns.

Power intent data and predictive analytics.

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Tags: B2B Company Data |LinkedIn Job Postings | Firmographics | Global Business Intelligence | Sales Leads | VC & PE Data | Technographics | CRM Enrichment | API Access | AI-Validated Data

This statistic shows the ranking of the global top 10 biotech and pharmaceutical companies worldwide, based on revenue. The values are based on a 2025 database. U.S. pharmaceutical company Pfizer was ranked first, with a total revenue of around ** billion U.S. dollars. Biotech and pharmaceutical companiesPharmaceutical companies are best known for manufacturing pharmaceutical drugs. These drugs have the aim to diagnose, to cure, to treat, or to prevent diseases. The pharmaceutical sector represents a huge industry, with the global pharmaceutical market being worth around *** trillion U.S. dollars. The best known top global pharmaceutical players are Pfizer, Merck, and Johnson & Johnson from the U.S., Novartis and Roche from Switzerland, Sanofi from France, etc. Most of these companies are involved not only in pure pharmaceutical business, but also manufacture medical technology and consumer health products, vaccines, etc. There are both pure play biotechnology companies and pharmaceutical companies which among other products also produce biotech products within their biotechnological divisions. Most of the leading global pharmaceutical companies have biopharmaceutical divisions. Although not a pure play biotech firm, Roche from Switzerland is among the companies with the largest revenues from biotechnology products worldwide. In contrast, California-based company Amgen was one of the world’s first large pure play biotech companies. Biotech companies use biotechnology to generate their products, most often medical drugs or agricultural genetic engineering. The latter segment is dominated by companies like Bayer CropScience and Syngenta. The United Nations Convention on Biological Diversity defines biotechnology as follows: "Any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use." In fact, biotechnology is thousands of years old, used in agriculture, food manufacturing and medicine.

As per Cognitive Market Research's latest published report, the Global Data Collection and Labeling market size was USD 2.41 Billion in 2022 and it is forecasted to reach USD 18.60 Billion by 2030. Data Collection and Labeling Industry's Compound Annual Growth Rate will be 29.1% from 2023 to 2030. What are the key driving factors for the Data Collection and Labeling Market?

As machine learning and artificial intelligence become more prevalent, the demand for high-quality training data is increasing. This is because algorithms need accurate and well-labeled data to learn and make accurate predictions. This factor is accelerating the growth of the Data Collection and Labeling Market. Moreover, the advancement in technology is one of the major factors contributing to the market growth. Technological advancements have made data collection and labeling more efficient and accurate. For example, computer vision algorithms can now label images and videos automatically, reducing the need for manual labeling. Similarly, the growing need for data in various industries and data collection and labeling is critical in industries such as healthcare, finance, retail, and automotive. As these industries become more data-driven, the need for accurate and well-labeled data is increasing, which is driving the market’s growth.

Growing use of AI and machine learning is creating demand for high-quality labelled data sets across sectors.

High-quality labelled data sets across sectors are needed due to growing use of AI and machine learning. More companies are now seeking to train AI models to do things like autonomous cars, medical diagnosis or natural language processing, and data annotation is getting in the way. Automated and AI-based data labelling technologies have streamlined the process, which in turn has minimized manual labelling cost and time. Concurrently, the accelerated expansion of e-commerce, social media, and customer analytics industries is also fueling an unquenchable thirst for copious amounts of labelled data. Cloud-based platforms enabled organizations to embrace scalable solutions for real-time data labelling, which will support faster market growth.

Key Restraint of Market.

Data privacy laws, high expense, and inefficient manual labelling can restraint the market.

While it is slowly being adopted, we are inevitably going to encounter non-trivial issues with data collection, data labelling, data privacy, data security, and compliance. Laws such as GDPR and CCPA have a genuine effect on what you can do with user data, and the amount of usable high-quality datasets available is few and far between. While manual tagging has proven to be time-consuming and error-filled, reducing accuracy and scalability. High costs of skilled annotators and advanced AI-powered tagging technologies may be unaffordable for small-to-mid-sized entities. Bias data and its impact on the AI decision-making process is another ethical problem that significantly holds back the digital workforce, which compels entities to follow transparent data labelling practices properly, according to the information they want.

Key Opportunity of Market.

AI-powered automation and self-supervised learning improve scalability and precision in data labeling.

The increasing penetration of AI-powered automation in data labeling, along with the vast scale, provides profitable growth opportunities in the market. The latency will decrease, and the costs will be less due to the integration of AI-powered annotation tools with a human-in-the-loop model that offers a trade-off between the accuracy and costs. Self-supervised and semi-supervised learning expands the potential of an AI model to tag data with minimal or no human intervention but offers robust scalability. New uses in healthcare, robotics, and autonomous systems open up new use cases by the day. Additionally, increased growth in edge computing and IoT devices organically generates large amounts of unstructured data, providing a pathway for AI-based data-labeling solutions to help improve real-time processing and analysis. What is Data Collection and Labeling?

Data collection and labeling is the process of gathering and organizing data and adding metadata to it for better analysis and understanding. This process is critical in machine learning and artificial intelligence, as it provides the found...

Key Table Information.Table Title.Island Areas: Share of Sales, Value of Shipments, or Revenue Accounted for by the 4, 8, 20, and 50 Largest Establishments for Puerto Rico: 2022.Table ID.ISLANDAREASIND2022.IA2200SIZE03.Survey/Program.Economic Census of Island Areas.Year.2022.Dataset.ECNIA Economic Census of Island Areas.Source.U.S. Census Bureau, 2022 Economic Census of Island Areas, Core Statistics.Release Date.2024-12-19.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.2022 Economic Census of Island Areas tables are released on a flow basis from June through December 2024.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe. The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in Puerto Rico, have paid employees, and are classified in one of eighteen in-scope sectors defined by the 2022 NAICS..Sponsor.U.S. Department of Commerce.Methodology.Data Items and Other Identifying Records.Number of establishmentsSales, value of shipments, or revenue ($1,000)Sales, value of shipments, or revenue of largest establishments as percent of total sales, value of shipments, or revenue (%)Herfindahl-Hirschman index for the 50 largest establishmentsRange indicating percent of total sales, value of shipments, or revenue imputedEach record includes a CONCENES code, which represents a specific concentration ratio category for establishments.The data are shown for concentration ratio ranges.Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the Economic Census of Island Areas are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed..Geography Coverage.The data are shown for employer establishments and firms that vary by industry:At the Territory level for Puerto RicoFor information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 4-digit 2022 NAICS code levels for selected economic census sectors and geographies.For information about NAICS, see Economic Census Code Lists..Sampling.The Economic Census of Island Areas is a complete enumeration of establishments located in the islands (i.e., all establishments on the sampling frame are included in the sample). Therefore, the accuracy of tabulations is not affected by sampling error..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0044).The primary method of disclosure avoidance protection is noise infusion. Under this method, the quantitative data values such as sales or payroll for each establishment are perturbed prior to tabulation by applying a random noise multiplier (i.e., factor). Each establishment is assigned a single noise factor, which is applied to all its quantitative data value. Using this method, most published cell totals are perturbed by at most a few percentage points.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. For more information on disclosure avoidance, see Methodology for the 2022 Economic Census- Island Areas..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, see Methodology for the 2022 Economic Census- Island Areas.For more information about survey questionnaires, Primary Business Activity/NAICS codes, and NAPCS codes, see Economic Census Technical Documentation..Weights.Because the Economic Census of Island Areas is a complete enumeration, there is no sample weighting..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data ...

Key Table Information.Table Title.Selected Sectors: Concentration of Largest Firms for the U.S.: 2022.Table ID.ECNSIZE2022.EC2200SIZECONCEN.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Economic Census: Establishment and Firm Size Statistics for the U.S..Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2025-04-24.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesSales, value of shipments, or revenue of largest firms as percent of total sales, value of shipments, or revenue(%)Herfindahl-Hirschman Index (based on sales, value of shipments, or revenue)(%)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. levels only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for all economic census sectors except Agriculture (11) and Management of Companies and Enterprises (55). For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unp...

According to Cognitive Market Research, the global Artificial Intelligence in Retail market size is USD 4951.2 million in 2023and will expand at a compound annual growth rate (CAGR) of 39.50% from 2023 to 2030.

Enhanced customer personalization to provide viable market output
Demand for online remains higher in Artificial Intelligence in the Retail market.
The machine learning and deep learning category held the highest Artificial Intelligence in Retail market revenue share in 2023.
North American Artificial Intelligence In Retail will continue to lead, whereas the Asia-Pacific Artificial Intelligence In Retail market will experience the most substantial growth until 2030.

Market Dynamics of the Artificial Intelligence in the Retail Market

Key Drivers for Artificial Intelligence in Retail Market

Enhanced Customer Personalization to Provide Viable Market Output

A primary driver of Artificial Intelligence in the Retail market is the pursuit of enhanced customer personalization. A.I. algorithms analyze vast datasets of customer behaviors, preferences, and purchase history to deliver highly personalized shopping experiences. Retailers leverage this insight to offer tailored product recommendations, targeted marketing campaigns, and personalized promotions. The drive for superior customer personalization not only enhances customer satisfaction but also increases engagement and boosts sales. This focus on individualized interactions through A.I. applications is a key driver shaping the dynamic landscape of A.I. in the retail market.

January 2023 - Microsoft and digital start-up AiFi worked together to offer Smart Store Analytics. It is a cloud-based tracking solution that helps merchants with operational and shopper insights for intelligent, cashierless stores.

Source-techcrunch.com/2023/01/10/aifi-microsoft-smart-store-analytics/

Improved Operational Efficiency to Propel Market Growth

Another pivotal driver is the quest for improved operational efficiency within the retail sector. A.I. technologies streamline various aspects of retail operations, from inventory management and demand forecasting to supply chain optimization and cashier-less checkout systems. By automating routine tasks and leveraging predictive analytics, retailers can enhance efficiency, reduce costs, and minimize errors. The pursuit of improved operational efficiency is a key motivator for retailers to invest in AI solutions, enabling them to stay competitive, adapt to dynamic market conditions, and meet the evolving demands of modern consumers in the highly competitive artificial intelligence (AI) retail market.

January 2023 - The EY Retail Intelligence solution, which is based on Microsoft Cloud, was introduced by the Fintech business EY to give customers a safe and efficient shopping experience. In order to deliver insightful information, this solution makes use of Microsoft Cloud for Retail and its technologies, which include image recognition, analytics, and artificial intelligence (A.I.).

Source-www.ey.com/en_gl/news/2023/01/ey-announces-launch-of-retail-solution-that-builds-on-the-microsoft-cloud-to-help-achieve-seamless-consumer-shopping-experiences

Key Restraints for Artificial Intelligence in Retail Market

Data Security Concerns to Restrict Market Growth

A prominent restraint in Artificial Intelligence in the Retail market is the pervasive concern over data security. As retailers increasingly rely on A.I. to process vast amounts of customer data for personalized experiences, there is a growing apprehension regarding the protection of sensitive information. The potential for data breaches and cyberattacks poses a significant challenge, as retailers must navigate the delicate balance between utilizing customer data for AI-driven initiatives and safeguarding it against potential security threats. Addressing these concerns is crucial to building and maintaining consumer trust in A.I. applications within the retail sector.

Key Trends for Artificial Intelligence in Retail Market

Surge in Voice-Enabled Shopping Interfaces Reshaping Retail Experiences

Voice-enabled A.I. assistants such as Amazon Alexa and Google Assistant are revolutionizing the way consumers engage with retail platforms. Shoppers can now utilize voice commands to search, compare, and purchase products, thereby streamlining and accelerating the buying process. Retailers...

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Release Date: 2018-08-10.[NOTE: Includes firms with payroll at any time during 2016. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2016 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, or veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for U.S. Employer Firms by Duration of Business Banking Relationship by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016. ..Release Schedule. . This file was released in August 2018.. ..Key Table Information. . These data are related to all other 2016 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2016 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2016 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms by Duration of Business Banking Relationship by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of respondent firms. . All firms. Female-owned. Male-owned. Equally male-/female-owned. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. All firms classifiable by gender, ethnicity, race, and veteran status. Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in busines...

Release Date: 2018-08-10.[NOTE: Includes firms with payroll at any time during 2016. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2016 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, or veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for U.S. Employer Firms by Reasons That a Business Ceased Operations by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016. ..Release Schedule. . This file was released in August 2018.. ..Key Table Information. . These data are related to all other 2016 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2016 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2016 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms by Reasons That a Business Ceased Operations by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of respondent firms. . All firms. Female-owned. Male-owned. Equally male-/female-owned. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. All firms classifiable by gender, ethnicity, race, and veteran status. Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in busines...

Release Date: 2017-07-13.[NOTE: Includes firms with payroll at any time during 2015. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2015 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, or veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for U.S. Employer Firms That Had E-Commerce Sales by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015. ..Release Schedule. . This file was released in July 2017.. ..Key Table Information. . These data are related to all other 2015 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2015 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2015 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms That Had E-Commerce Sales by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of respondent firms. . All firms. Female-owned. Male-owned. Equally male-/female-owned. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. All firms classifiable by gender, ethnicity, race, and veteran status. Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more year...