The dataset contains information about world's biggest companies.

Among them you can find companies founded in the US, the UK, Europe, Asia, South America, South Africa, Australia.

The dataset contains information about the year the company was founded, its' revenue and net income in years 2018 - 2020, and the industry.

I have included 2 csv files: the raw csv file if you want to practice cleaning the data, and the clean csv ready to be analyzed.

The third dataset includes the name of all the companies included in the previous datasets and 2 additional columns: number of employees and name of the founder.

In addition there's tesla.csv file containing shares prices for Tesla.

Analysis of ‘Brazil Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/brazil-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Assets ($billion), Sales ($billion), technical information and other features such as: - Profits ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Assets ($billion) in relation to Sales ($billion)
• Study the influence of Profits ($billion) on Market Value ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

CompanyData.com, powered by BoldData, is your global partner for high-quality, verified B2B company information. Our Japan company database includes 6,535,597 verified business records, sourced directly from official trade registers and government data, offering unmatched coverage of one of the world’s largest economies.

Each Japanese company profile contains comprehensive firmographic and structural data, including company name, registration number, corporate ID, legal form, business type (NACE or JSIC classification), size, revenue estimates, and employee count. Many records also feature contact information, such as emails, phone numbers, and names of key decision-makers, enabling precise and effective outreach.

Our Japan dataset is ideal for a wide range of use cases — from compliance, KYC, and AML checks, to B2B marketing, sales prospecting, market research, CRM data enrichment, and AI model training. Whether you're targeting tech firms in Tokyo or manufacturers in Osaka, our database supports smart, targeted growth.

We provide flexible data delivery options to fit your workflow — including custom-built lists, full database exports in Excel or CSV, real-time API access, and a convenient self-service platform. Need help optimizing your own data? Our enrichment and cleansing services can update and enhance your existing records using verified data from Japan.

With access to 6,535,597 verified companies worldwide, CompanyData.com helps you scale locally in Japan and globally across borders. Leverage our deep local data and global expertise to make confident, data-driven decisions that move your business forward.

CompanyData.com powered by BoldData is your gateway to verified, high-quality business data from around the world. We specialize in delivering structured company information sourced directly from official trade registers, giving you reliable data to fuel smarter business decisions.

Our USA company database includes over 69,853,300 verified business records, making it one of the most comprehensive sources of company information available. Each record contains detailed firmographics such as industry classification, company size and revenue, corporate hierarchies and verified contact details including decision-maker names, email addresses, direct dials and mobile numbers.

This rich dataset supports a wide range of use cases including - Regulatory compliance and KYC verification - Sales prospecting and lead generation - B2B marketing and audience segmentation - CRM enrichment and data cleansing - Training data for AI and machine learning models

We offer flexible delivery options tailored to your workflow - Tailored company lists filtered by location, size, industry and more - Full USA company database exports in Excel or CSV - Real-time API access for seamless data integration - Data enrichment services to enhance your internal records

The United States is a key part of our global database of over 69,853,300 verified companies across more than 200 countries. Whether you are expanding into the US market or enriching global CRM systems, we deliver the accuracy, scale and flexibility your business demands.

Partner with CompanyData.com to unlock actionable company intelligence in the USA delivered how you need it, when you need it, with the precision your business deserves.

Business-critical Data Types We offer access to robust datasets sourced from over 13M job ads daily. Track companies’ growth, market focus, technological shifts, planned geographic expansion, and more: - Identify new business opportunities - Identify and forecast industry & technological trends - Help identify the jobs, teams, and business units that have the highest impact on corporate goals - Identify most in-demand skills and qualifications for key positions.

Fresh Datasets We regularly update our datasets, assuring you access to the latest data and allowing for timely analysis of rapidly evolving markets & dynamic businesses.

Historical Datasets We maintain at your disposal historical datasets, allowing for comprehensive, reliable, and statistically sound historical analysis, trend identification, and forecasting.

Easy Access and Retrieval Our job listing datasets are available in industry-standard, convenient JSON and CSV formats. These structured formats make our datasets compatible with machine learning, artificial intelligence training, and similar applications. The historical data retrieval process is quick and reliable thanks to our robust, easy-to-implement API integration.

Datasets for investors Investment firms and hedge funds use our datasets to better inform their investment decisions by gaining up-to-date, reliable insights into workforce growth, geographic expansion, market focus, technology shifts, and other factors of start-ups and established companies.

Datasets for businesses Our datasets are used by retailers, manufacturers, real estate agents, and many other types of B2B & B2C businesses to stay ahead of the curve. They can gain insights into the competitive landscape, technology, and product adoption trends as well as power their lead generation processes with data-driven decision-making.

Our Web Scraping dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For lead generation

With millions of companies from around the globe, this scraped data enables you to filter potential clients based on specific criteria and hasten the conversion process.

Use cases

1. Filter potential clients according to location, size, and other criteria
2. Enrich your existing database
3. Improve conversion rates
4. Use predictive models to identify potential leads
5. Group your leads in segments for more accurate targeting

For market and business analysis

Our Web Scraping Data on companies gives information about millions of businesses, allowing you to evaluate your competitors.

Use cases

1. Know your competitors
2. See your competitors' size, headcount, and revenue
3. Come up with a data-driven strategy for the next quarter

For Investors

We recommend Web Scraping Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global Web Scraping Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies looking for the next investment
3. Check if a startup is about to reach its maturity
4. Predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

Web Scraping Data saves time your employees would otherwise use it to find potential clients and choose the best prospects manually.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
3. Based on the revenue, determine which companies are ready to convert
4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

Dataset Card for 100 Richest People In World

  Dataset Summary

This dataset contains the list of Top 100 Richest People in the World Column Information:-

Name - Person Name NetWorth - His/Her Networth Age - Person Age Country - The country person belongs to Source - Information Source Industry - Expertise Domain

  Join our Community









  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/100-richest-people-in-world.

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.

Analysis of ‘Germany Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/germany-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Profits ($billion), Assets ($billion), technical information and other features such as: - Sales ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Global Rank in relation to Profits ($billion)
• Study the influence of Assets ($billion) on Sales ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by sriswaroopkoundinya

Contents

This dataset provides an overview of net zero targets for the 2,000 largest publicly-traded companies in the world by revenue. Data originally from Net Zero Tracker.

Context

From GLOBAL 2000

Forbes’ 18th annual ranking of the world’s 2,000 largest public companies illustrates the magnitude of the global shutdowns and serves as a warning for more trouble ahead in the coming months.

Acknowledgements

Most companies on this year’s Global 2000 list have seen their market values drop considerably since last year, and woeful first-quarter earnings provide a painful insight into the impact of the Great Cessation. The past few months have been especially brutal for the airlines, which saw demand drop lower than after 9/11. American Airlines, for instance, fell from No. 372 on the list to 967th after losing a staggering $2.2 billion in its first quarter.

Not all companies are being negatively affected by the pandemic, however. The biggest players in e-commerce —including Amazon, Alibaba and Walmart—have all experienced growth thanks to the rise in online shopping. All three moved up on this year’s list.

On the financial front, the Industrial and Commercial Bank of China remained in the top spot for the eighth straight year with more than $4.3 trillion in assets. China’s “big four” state-owned banks all wound up in this year’s top 10. JPMorgan Chase is the largest U.S. company at No. 3, falling one spot from last year.

In other bright spots, the largest IPO of 2019, Saudi Aramco, debuted at No. 5 on the list, while Zoom and Slack (which both IPOed last year) have also been instant beneficiaries of the new work-from-home realities. Both companies made an inaugural appearance on the Global 2000—virtually overnight.

CompanyKG is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.

Edges: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.

Nodes: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "Klarna is a fintech company that provides support for direct and post-purchase payments ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: mSBERT (multilingual Sentence BERT), ADA2, SimCSE (fine-tuned on the raw company descriptions) and PAUSE.

Evaluation Tasks. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:

Similarity Prediction (SP). To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.

Competitor Retrieval (CR). Each sample contains one target company and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with N direct competitors in this CR evaluation set, we expect a competent method to retrieve all N competitors when searching for similar companies to A.

Similarity Ranking (SR) is designed to assess the ability of any method to rank candidate companies (numbered 0 and 1) based on their similarity to a query company. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development.

Edge Prediction (EP) evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).

Background and Motivation

In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as company similarity quantification, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.

While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment.

In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.

However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a Knowledge Graph (KG). Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.

Source Code and Tutorial:https://github.com/llcresearch/CompanyKG2

Paper: to be published

Graph Database Market Outlook

The global graph database market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 8.5 billion by 2032, growing at a CAGR of 21.2% from 2024 to 2032. The substantial growth of this market is driven primarily by increasing data complexity, advancements in data analytics technologies, and the rising need for more efficient database management systems.

One of the primary growth factors for the graph database market is the exponential increase in data generation. As organizations generate vast amounts of data from various sources such as social media, e-commerce platforms, and IoT devices, the need for sophisticated data management and analysis tools becomes paramount. Traditional relational databases struggle to handle the complexity and interconnectivity of this data, leading to a shift towards graph databases which excel in managing such intricate relationships.

Another significant driver is the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies. These technologies rely heavily on connected data for predictive analytics and decision-making processes. Graph databases, with their inherent ability to model relationships between data points effectively, provide a robust foundation for AI and ML applications. This synergy between AI/ML and graph databases further accelerates market growth.

Additionally, the increasing prevalence of personalized customer experiences across industries like retail, finance, and healthcare is fueling demand for graph databases. Businesses are leveraging graph databases to analyze customer behaviors, preferences, and interactions in real-time, enabling them to offer tailored recommendations and services. This enhanced customer experience translates to higher customer satisfaction and retention, driving further adoption of graph databases.

From a regional perspective, North America currently holds the largest market share due to early adoption of advanced technologies and the presence of key market players. However, significant growth is also anticipated in the Asia-Pacific region, driven by rapid digital transformation, increasing investments in IT infrastructure, and growing awareness of the benefits of graph databases. Europe is also expected to witness steady growth, supported by stringent data management regulations and a strong focus on data privacy and security.

Component Analysis

The graph database market can be segmented into two primary components: software and services. The software segment holds the largest market share, driven by extensive adoption across various industries. Graph database software is designed to create, manage, and query graph databases, offering features such as scalability, high performance, and efficient handling of complex data relationships. The growth in this segment is propelled by continuous advancements and innovations in graph database technologies. Companies are increasingly investing in research and development to enhance the capabilities of their graph database software products, catering to the evolving needs of their customers.

On the other hand, the services segment is also witnessing substantial growth. This segment includes consulting, implementation, and support services provided by vendors to help organizations effectively deploy and manage graph databases. As businesses recognize the benefits of graph databases, the demand for expert services to ensure successful implementation and integration into existing systems is rising. Additionally, ongoing support and maintenance services are crucial for the smooth operation of graph databases, driving further growth in this segment.

The increasing complexity of data and the need for specialized expertise to manage and analyze it effectively are key factors contributing to the growth of the services segment. Organizations often lack the in-house skills required to harness the full potential of graph databases, prompting them to seek external assistance. This trend is particularly evident in large enterprises, where the scale and complexity of data necessitate robust support services.

Moreover, the services segment is benefiting from the growing trend of outsourcing IT functions. Many organizations are opting to outsource their database management needs to specialized service providers, allowing them to focus on their core business activities. This shift towards outsourcing is further bolstering the demand for graph database services, driving market growth.

Video Games Sales Dataset

About Dataset

This Dataset provides up-to-date information on the sales performance and popularity of various video games worldwide. The data includes the name, platform, year of release, genre, publisher, and sales in North America, Europe, Japan, and other regions. It also features scores and ratings from both critics and users, including average critic score, number of critics reviewed, average user score, number of users reviewed, developer, and rating. This comprehensive and essential dataset offers valuable insights into the global video game market and is a must-have tool for gamers, industry professionals, and market researchers. by source

More Datasets

For more datasets, click here.

Columns

Research Ideas / Data Use

• Market Analysis: The video game sales data can be used to analyze market trends and identify popular genres, platforms, and publishers. This can be useful for industry professionals to make informed decisions about game development and marketing strategies.
• Sales Forecasting: The sales data can be used to forecast future trends and predict the success of upcoming games.
• Consumer Insights: The data can be analyzed to gain insights into consumer preferences and buying habits, which can be used to tailor marketing strategies and improve customer satisfaction.
• Comparison of Competitors: The data can be used to compare the sales performance of competing video games and identify market leaders.
• Gaming Industry Performance: The data can be used to evaluate the overall performance of the gaming industry and track its growth over time.
• Gaming Popularity by Region: The data can be analyzed to determine which regions are the largest markets for video games and which genres are most popular in each region.
• Impact of Reviews: The data can be used to study the impact of critic and user reviews on sales and the relationship between scores and sales performance.
• Gaming Trends over Time: The data can be used to identify trends in the gaming industry over time and to track the evolution of the market.
• Gaming Demographics: The data can be used to analyze the demographic makeup of the gaming audience, including age, gender, and income.
• Impact of Gaming Industry on the Economy: The data can be used to evaluate the impact of the gaming industry on the economy and to assess its contribution to job creation and economic growth.

Acknowledgements

if this dataset was used in your work or studies, please credit the original source Please Credit ↑ ⠀⠀⠀

OLAP Database Systems Market Outlook

The OLAP (Online Analytical Processing) Database Systems market size is projected to grow significantly from $10.3 billion in 2023 to a remarkable $21.6 billion by 2032, at an impressive CAGR of 8.4%. This growth is primarily driven by the increasing need for sophisticated data analytics to support business intelligence and decision-making processes. Organizations across various sectors are increasingly recognizing the value of OLAP systems in transforming vast amounts of raw data into actionable insights, thereby fueling the market’s expansion.

One of the major growth factors for the OLAP Database Systems market is the increasing volume of data being generated globally. With the rise of IoT devices, social media, and digital transactions, the amount of data being produced is growing exponentially. Businesses need robust systems to analyze this data efficiently and derive meaningful insights. OLAP systems provide the required analytical capabilities to handle large datasets, making them indispensable in today’s data-driven world. Additionally, advancements in machine learning and AI are enhancing the capabilities of OLAP systems, further driving their adoption.

Another key driver is the growing importance of business intelligence and data-driven decision-making in organizations. In a competitive business environment, companies are leveraging OLAP systems to gain a comprehensive understanding of their operations, customer behavior, and market trends. These insights help in strategic planning, identifying new opportunities, and optimizing operations. As a result, the demand for OLAP systems is witnessing a substantial increase across various industry verticals, including BFSI, healthcare, retail, and manufacturing.

Moreover, the shift towards cloud-based solutions is significantly contributing to the market growth. Cloud-based OLAP systems offer several advantages, such as scalability, cost-effectiveness, and ease of deployment. They eliminate the need for significant upfront investments in hardware and infrastructure, making advanced analytics accessible to small and medium enterprises (SMEs) as well. The flexibility and scalability offered by cloud-based OLAP systems are encouraging more organizations to migrate their analytics operations to the cloud, thereby driving market growth.

Regionally, North America is expected to dominate the OLAP Database Systems market during the forecast period, followed by Europe and Asia Pacific. The presence of major technology companies and high adoption rates of advanced analytics solutions are the key factors contributing to the market's growth in North America. In contrast, the Asia Pacific region is anticipated to exhibit the highest growth rate due to rapid digitalization, increasing internet penetration, and the growing adoption of emerging technologies in countries like China, India, and Japan.

Component Analysis

The OLAP Database Systems market can be segmented by component into software, hardware, and services. The software segment holds the largest market share due to the extensive use of OLAP software for data modeling, reporting, and analysis. OLAP software solutions are crucial for businesses to extract meaningful insights from their data and support decision-making processes. These solutions are continuously evolving with the integration of advanced features like real-time analytics, predictive modeling, and AI-driven insights, making them indispensable tools for modern enterprises.

The hardware segment, although smaller compared to software, is also significant. It includes servers, storage devices, and networking equipment essential for the deployment of OLAP systems. With the growing adoption of big data and analytics, there is an increasing demand for robust hardware infrastructure to support these complex analytical processes. Innovations in hardware technology, such as high-performance computing and the development of more efficient storage systems, are also contributing to the growth of this segment.

The services segment is expected to witness substantial growth during the forecast period. This segment includes consulting, implementation, and maintenance services. As organizations adopt OLAP systems, they require expertise for smooth implementation and integration with their existing IT infrastructure. Consulting services help businesses identify their specific needs and choose the right OLAP solutions, while implementation services ensure the successful deployment of these systems. Ongoing maintenance and support services

Success.ai’s Manufacturing Company Data and B2B Contact Data for Global Manufacturing Professionals empowers businesses to connect with key decision-makers in the manufacturing industry worldwide. With access to over 170 million verified professional profiles, this dataset includes critical contact information for executives, managers, engineers, and other professionals in manufacturing, supply chain, and production roles. Whether you're targeting plant managers, operations executives, or procurement officers, Success.ai ensures accurate and effective outreach.

Why Choose Success.ai’s Manufacturing Professionals Data?

1. Comprehensive Contact Information:
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for manufacturing leaders globally.

3. AI-validated data ensures 99% accuracy and up-to-date contact details for your outreach.

4. Global Reach Across Manufacturing Functions:

5. Includes profiles of manufacturing executives, plant managers, procurement specialists, engineers, and more.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets:

8. Real-time data updates guarantee you always have the latest information about manufacturing professionals.

9. Ethical and Compliant:

10. Adheres to GDPR, CCPA, and other global privacy regulations for ethical use of data.

Data Highlights: - 170M+ Verified Professional Profiles: Includes manufacturing professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.

Key Features of the Dataset:

1. Manufacturing Decision-Maker Profiles: Identify and connect with top-tier manufacturing professionals including operations leaders, plant managers, procurement officers, and senior executives.

2. Advanced Filters for Precision Targeting: Filter data by industry, company size, location, and specific job roles for optimal outreach.

3. AI-Driven Enrichment: Profiles enriched with actionable data to facilitate personalized engagement and higher success rates in campaigns.

Strategic Use Cases:

1. Supply Chain and Procurement Outreach:
2. Reach professionals involved in purchasing, supply chain management, and vendor relations.

3. Perfect for suppliers of equipment, materials, and logistics services to target key decision-makers in manufacturing.

4. Lead Generation for Manufacturing Solutions:

5. Promote manufacturing software, automation tools, and process optimization solutions.

6. Connect with professionals in charge of manufacturing operations to present cost-saving and efficiency-driving solutions.

7. Market Research and Industry Insights:

8. Gather data for industry trends, connect with thought leaders, and conduct targeted research in the global manufacturing sector.

9. Engage with professionals to build relationships and gain insights into evolving manufacturing practices.

10. Targeted Marketing Campaigns:

11. Design email marketing campaigns or direct outreach strategies targeting manufacturing decision-makers.

12. Utilize accurate contact data to drive higher engagement and conversion rates in your campaigns.

Why Choose Success.ai?

1. Best Price Guarantee: Enjoy the highest quality datasets at the most competitive pricing.

2. Seamless Integration: Easily integrate data into your CRM systems using APIs or download in the preferred format.

3. Data Accuracy with AI Validation: All profiles in this dataset are verified for 99% accuracy, ensuring confidence in the data for marketing, outreach, and decision-making.

4. Customizable and Scalable Solutions: Tailor the dataset to specific manufacturing sectors or job functions for more targeted outreach.

APIs for Enhanced Functionality:

1. Data Enrichment API: Enhance your existing records with verified manufacturing contact data to improve engagement and targeting.

2. Lead Generation API: Automate the lead generation process for manufacturing-specific campaigns to increase efficiency and scale.

Leverage Success.ai’s B2B Contact Data for Manufacturing Professionals to connect with key decision-makers in the global manufacturing industry. With verified emails, phone numbers, and continuously updated profiles, this data ensures that your outreach and communication efforts are impactful and precise.

Contact Success.ai now to elevate your manufacturing industry strategies with verified, AI-validated contact data. And remember—no one beats us on price. Period.

Analysis of ‘Greece Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/greece-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Market Value ($billion), Assets ($billion), technical information and other features such as: - Profits ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Assets ($billion) in relation to Profits ($billion)
• Study the influence of Market Value ($billion) on Assets ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

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--- Original source retains full ownership of the source dataset ---

The size of the US Data Center Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.00% during the forecast period.A data center is a facility that keeps computer systems and networking equipment housed, processing, and transmitting data. It represents the infrastructure on which organizations carry out their IT operations and host websites, email servers, and database servers. Data centers, therefore, are imperative to any size business: small start-ups or large enterprise since they enable digital transformation, thus making business applications available.The US data center industry is one of the largest and most developed in the world. The country boasts robust digital infrastructure, abundant energy resources, and a highly skilled workforce, making it an attractive destination for data center operators. Some of the drivers of the US data center market are the growing trend of cloud computing, internet of things (IoT), and high-performance computing requirements.Top-of-the-line technology companies along with cloud service providers set up major data center footprints in the US, mostly in key regions such as Silicon Valley and Northern Virginia, Dallas, for example. These data centers support applications such as e-commerce-a manner of accessing streaming services-whose development depends on its artificial intelligence financial service type. As demand increases concerning data center capacity, therefore, the US data centre industry will continue to prosper as the world's hub for reliable and scalable solutions. Recent developments include: February 2023: The expansion of Souther Telecom to its data center in Atlanta, Georgia, at 345 Courtland Street, was announced by H5 Data Centers, a colocation and wholesale data center operator. One of the top communication service providers in the southeast is Southern Telecom. Customers in Alabama, Georgia, Florida, and Mississippi will receive better service due to the expansion of this low-latency fiber optic network.December 2022: DigitalBridge Group, Inc. and IFM Investors announced completing their previously announced transaction in which funds affiliated with the investment management platform of DigitalBridge and an affiliate of IFM Investors acquired all outstanding common shares of Switch, Inc. for USD approximately USD 11 billion, including the repayment of outstanding debt.October 2022: Three additional data centers in Charlotte, Nashville, and Louisville have been made available to Flexential's cloud customers, according to the supplier of data center colocation, cloud computing, and connectivity. By the end of the year, clients will have access to more than 220MW of hybrid IT capacity spread across 40 data centers in 19 markets, which is well aligned with Flexential's 2022 ambition to add 33MW of new, sustainable data center development projects.. Key drivers for this market are: , High Mobile penetration, Low Tariff, and Mature Regulatory Authority; Successful Privatization and Liberalization Initiatives. Potential restraints include: , Difficulties in Customization According to Business Needs. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

Analysis of ‘China Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/china-largest-companiese on 28 January 2022.

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About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 100 samples along with Profits ($billion), Market Value ($billion), technical information and other features such as: - Sales ($billion) - Assets ($billion) - and more.

How to use this dataset

• Analyze Global Rank in relation to Profits ($billion)
• Study the influence of Market Value ($billion) on Sales ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

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--- Original source retains full ownership of the source dataset ---