The average for 2023 based on 168 countries was 26.27 percent. The highest value was in Libya: 77.34 percent and the lowest value was in Sao Tome and Principe: 2.82 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

With a market capitalization of 3.12 trillion U.S. dollars as of May 2024, Microsoft was the world’s largest company that year. Rounding out the top five were some of the world’s most recognizable brands: Apple, NVIDIA, Google’s parent company Alphabet, and Amazon. Saudi Aramco led the ranking of the world's most profitable companies in 2023, with a pre-tax income of nearly 250 billion U.S. dollars. How are market value and market capitalization determined? Market value and market capitalization are two terms frequently used – and confused - when discussing the profitability and viability of companies. Strictly speaking, market capitalization (or market cap) is the worth of a company based on the total value of all their shares; an important metric when determining the comparative value of companies for trading opportunities. Accordingly, many stock exchanges such as the New York or London Stock Exchange release market capitalization data on their listed companies. On the other hand, market value technically refers to what a company is worth in a much broader context. It is determined by multiple factors, including profitability, corporate debt, and the market environment as a whole. In this sense it aims to estimate the overall value of a company, with share price only being one element. Market value is therefore useful for determining whether a company’s shares are over- or undervalued, and in arriving at a price if the company is to be sold. Such valuations are generally made on a case-by-case basis though, and not regularly reported. For this reason, market capitalization is often reported as market value. What are the top companies in the world? The answer to this question depends on the metric used. Although the largest company by market capitalization, Microsoft's global revenue did not manage to crack the top 20 companies. Rather, American multinational retailer Walmart was ranked as the largest company in the world by revenue. Walmart also had the highest number of employees in the world.

Financial data service providers offer financial market data and related services, primarily real-time feeds, portfolio analytics, research, pricing and valuation data, to financial institutions, traders and investors. Companies aggregate data and content from stock exchange feeds, broker and dealer desks and regulatory filings to distribute financial news and business information to the investment community. Recent globalization of the world capital market has benefited the financial sector and increased trading speed. Businesses rely on real-time data more than ever to help them make informed decisions. When considering a data service provider, an easy-to-use interface that shows customized, relevant information is vital for clients. During times of economic uncertainty, this information becomes more crucial than ever. Clients want information as soon and as frequently as possible, causing providers to prioritize efficiency and delivery. This was evident during the pandemic, the high interest rate environment in the latter part of the period and as the Fed cuts rates in 2024. Increased automation has helped industry players process large volumes of financial data, reducing analysis and reporting times. In addition, automation has reduced operational costs and reduced human data errors. These trends have resulted in growing revenue, which has risen at a CAGR of 3.2% to $21.9 billion over the past five years, including a 3.5% uptick in 2024 alone. Corporate profit will continue to expand as inflationary concerns begin to wane slowly. This will lead many companies to take on new clients as financial data helps them gain insight into operating their business amid ongoing trends and economic shakeups. With technology constantly advancing, service providers will continue investing in research and development to improve their products and services and best serve their clients. As technological advances continue, smaller players will be able to better compete with larger industry players. While this may lead to new companies joining the industry, larger providers will resume consolidation activity to expand their customer base. Overall, revenue is expected to swell at a CAGR of 2.7% to $25.0 billion by the end of 2029.

The Information sector creates and distributes media content to US consumers and businesses. The Information sector responds to trends in household formation, which influences subscription volumes to communications services advertising expenditure, which generates nearly one-fourth of sector revenue, as well as consumer incomes and spending habits, which influence the extent to which households purchase discretionary entertainment products. The Information sector also sells some products and services directly to businesses and is influenced to a lesser extent by trends in corporate profit and business sentiment. The accelerated pace of digital transformation has fueled industry growth. As remote work and online learning became the norm, the demand for robust digital infrastructure and cloud services skyrocketed. This shift wasn't limited to cloud services alone, internet providers flourished spurred by the advent of 5G technology. Through the end of 2024, sector revenue will expand at a CAGR of 2.7% to reach $2.4 trillion, including a boost of 1.9% in 2024. Although consumer demand for media is generally steady and the Information sector has expanded consistently, revenue flows within the sector are uneven and determined by technology trends. Substantial expansion through the end of 2024 has stemmed from a proliferation of new consumer devices. However, most of the expansion has been concentrated on online publishing and data processing at the expense of more traditional information subsectors. For example, new digital channels have detracted from print advertising expenditure, which has dipped during the current period and curtailed print publishing. An expansion in mobile devices and the emergence of online streaming services have made consumers less reliant on more traditional communication services like wired voice, broadband internet and cable TV. Looking ahead, the information sector is poised for sustained growth over the next five years, fueled by rising consumer spending and private investment. As the economy recovers and interest rates stabilize, disposable incomes are poised to climb, allowing households to avail themselves of more digital subscriptions and services. The rollout of 5G will further augment mobile internet usage, potentially challenging wired broadband alternatives. Traditional media companies will continue to pivot to online platforms and streaming services, aiming to retain and expand their audience. Through the end of 2029, the Information sector revenue will strengthen at a CAGR of 2.2% to reach $2.7 trillion.

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

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➡️ Multiple APIs designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing our APIs.

Coresignal's employee data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including HR and sales technology and investment.

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✅ Source best-fit talent for your recruitment needs

Coresignal's Employee Data can help source the best-fit talent for your recruitment needs by providing the most up-to-date information on qualified candidates globally.

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Enhance lead generation with 712M+ up-to-date employee records from the largest professional network. Our Employee Data can help you develop a qualified list of potential clients and enrich your own database.

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Employee Data can help you generate actionable signals and identify new investment opportunities earlier than competitors or perform deeper analysis of companies you're interested in.

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The Global Investment Report 2023 revealed that after a sharp decline in 2020 and a strong rebound in 2021, global foreign direct investment (FDI) declined by 12 percent to $1.3 trillion in 2022. However, in developing countries, FDI increased by 4% to $916 billion, a record share of more than 70% of global flows. The number of greenfield investment projects in developing countries increased by 37 percent and international project finance transactions by 5 percent. Foreign investment from China, the second largest recipient of foreign investment globally, increased by 5 percent. The service industry has become the mainstream industry in the global FDI structure. The global industry is accelerating its transformation to a "service-based economy," international FDI in productive service industries has become an essential means of industrial transfer in developed countries and a meaningful way to upgrade the industrial structure and high-quality development in emerging economies. As a representative province in central China, Hubei Province has unique advantages in human capital, factor cost, and market potential, which provide preferential conditions to attract foreign investment. This paper first introduced the concept of the productive service industry, based on the relevant statistical data from 2011 to 2022, focused on the current situation of foreign investment utilization in five major sub-sectors of the productive service industry in Hubei Province in the past ten years, and empirically investigated the impact of foreign investment utilization in five major sub-sectors of the productive service industry on the economic growth of Hubei Province, and obtained that the level of foreign investment attraction varied significantly among the regions in Hubei Province. The three productive service industries, namely transportation, storage and postal services, information transmission, software and information technology services, and financial services, played a significant role in the active attraction and optimal utilization of foreign capital and the economic development of Hubei Province. Based on this, it was proposed to build a market-oriented rule of law and internationalized business environment, improve the infrastructure construction in different regions of the province, focus on the training of professional talents for the development of productive service industries, and pay attention to the improvement of independent innovation capacity.

The Data Warehousing Market report segments the industry into By Type Of Deployment (On-Premise, Cloud, Hybrid), By Size Of Enterprise (Small And Medium-Sized Enterprises, Large Enterprises), By Industry Vertical (BFSI, Manufacturing, Healthcare, Retail, Other Industry Verticals), and Geography (North America, Europe, Asia-Pacific, Rest Of The World). Get five years of historical data and five-year market forecasts.

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

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 Data Broker Services market size is USD 268154.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 8.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 107261.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 80446.26 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 61675.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 13407.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Middle East and Africa held the major market ofaround 2% of the global revenue with a market size of USD 5363.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2024 to 2031.
The Subscription Paid held the highest Data Broker Services market revenue share in 2024.

Market Dynamics of Data Broker Services Market

Key Drivers of Data Broker Services Market

Increasing Demand for Personalized Marketing Solutions to boost the demand globally

The Data Broker Services Market is being driven by the increasing demand for personalized marketing solutions. Companies across various industries are leveraging data broker services to access valuable consumer insights and enhance their marketing strategies. Data brokers offer a wide range of data sets, including demographic, behavioral, and transactional data, which can be used to create targeted marketing campaigns. By utilizing data broker services, companies can tailor their marketing messages to specific consumer segments, leading to higher engagement and conversion rates. This trend is expected to continue driving the growth of the Data Broker Services Market as businesses increasingly prioritize personalized marketing approaches to remain competitive in the digital age.

Growing Focus on Data Monetization to Propel Market Growth

Another key driver of the Data Broker Services Market is the growing focus on data monetization. Organizations are realizing the value of their data assets and are looking for ways to monetize them. Data broker services enable companies to sell their data to third parties, such as marketers, researchers, and other businesses, generating additional revenue streams. This trend is particularly prevalent in industries with large amounts of consumer data, such as retail, finance, and healthcare. By monetizing their data, companies can unlock new revenue opportunities and offset the costs associated with data collection and management. As the demand for data-driven insights continues to grow, the Data Broker Services Market is expected to expand, driven by the increasing number of organizations looking to capitalize on their data assets.

Restraint Factors Of Data Broker Services Market

Regulatory Challenges and Data Privacy Concerns to Limit the Sales

One of the key restraints in the Data Broker Services Market is the increasing regulatory challenges and data privacy concerns. With the implementation of regulations such as the GDPR in Europe and the CCPA in California, data brokers are facing stricter requirements for data collection, processing, and sharing. Compliance with these regulations requires significant resources and can limit the ability of data brokers to collect and monetize data. Additionally, concerns about data privacy and security among consumers are leading to greater scrutiny of data broker practices, further complicating the operating environment for these companies. As regulatory pressures continue to increase, data brokers may face challenges in expanding their operations and maintaining profitability.

Opportunity for the Data Broker Services Market

The Data Broker Service Market is poised to benefit significantly from the integration of blockchain technology.

By leveraging blockchain's decentralized and immutable nature, data brokers can ensure tamper-proof data exchange, enable secure data sharing, and provide auditable trails. This can increase trust and confidence in data exchange, driving growth in the data broker...

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Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

What will be the Size of the Data Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offeri

The US data processing and hosting services industry is navigating a dynamic environment marked by rising demands and revolutionary trends. As digitalization accelerates, data centers have evolved from simple infrastructure to essential strategic assets. These hubs now power services ranging from cloud computing to advanced data analytics. In 2025, the data processing and hosting service market includes giants like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). Industry revenue currently sits at $383.8 billion, growing robustly at a CAGR of 9.2% over the past five years, including a 6.2% surge in 2025 alone. Alongside leading tech firms, smaller specialized providers cater to sectors like healthcare, financial services and government agencies with precision-placed data storage solutions. Emerging trends significantly influence the evolution of the US data processing and hosting services industry. Prominent among these is edge computing, a decentralized approach that locates data centers closer to end-user devices. Along with AI and modern data centers, these innovations aim to reduce latency and enhance application performance by minimizing resource usage in data transmission, thereby promoting broader adoption of cloud computing. Despite this transformative growth, the US data processing and hosting services industry faces significant hurdles, including a skill gap, escalating energy costs and escalating cybersecurity threats. This scarcity has heightened the focus on software automation, leading many facilities to implement AI solutions. Though offshoring trends lead to lost business for many participants, this activity is limited and the industry still benefits from strong demand, leading to rising profit. The industry is projected to grow at a CAGR of 2.4% to $431.4 billion by 2030. The future holds a mix of challenges and opportunities for the industry. Strategic investments in human capital and advanced technologies will distinguish industry leaders from laggards. Compliance with evolving data sovereignty and privacy regulations will determine local market competitiveness. Continuous innovation is expected to drive this progress, solidifying data centers' roles as pivotal components shaping the digital landscape ahead.

China boasts the fastest growing GDP of all developed nations. Neighboring regions will have the largest middle class in history. China is building transport infrastructure to take advantage. Companies that capture market share in this region will be the largest and best performing over the next decade.

Macro Tailwinds

1) China GDP is the fastest growing of any major country with expected 5-6% over the next decade. If businesses (Alibaba, Tencent, etc..) maintain flat market share, that alone will drive 5-6% over the next decade. This is already higher than JP Morgans expectation (from their 13f filings) that the US market will perform between -5% and +5% over this coming decade.

2) The Southeast Asia Region contains about 5 billion people. China is constructing the One Best One Road which will be completed by 2030. This will grant their businesses access to the fastest and largest growing middle class in human history. Over the next 10+ years this region will be home to the largest middle class in history, potentially over 10x that of North America and Europe, based on stock price in Google Sheets.

Increasing average Chinese income.

Chinese average income has more than doubled over the last decade. Having sustained the least economic damage from the virus, this trend is expected to continue. At this pace the average Chinese citizen salary will be at 50% of the average US by 2030 (with stock price in Excel provided by Finsheet via Finnhub Stock Api), with the difference being there are 4x more Chinese. Thus a market potential of almost 2x the US over the next decade.

The Southeast Asia Region now contains the largest total number of billionaires, this number is expected to increase at an increasing rate as the region continues to develop. Over the next 10 years the largest trading route ever assembled will be completed, and China will be the primary provider of goods to 5b+ people

2013 North America was home to the largest number of billionaires. This reversed with Asia over the following 5 years. This separation is expected to continue at an increasing rate. Why does this matter? Over the next 10 years the largest trading route ever assembled will be completed, and China will be the primary provider of goods to 5b+ people

Companies that can easily access all customers in the world will perform best. This is good news for Apple, Microsoft, and Disney. Disney stock price in Excel right now is $70. But not for Amazon or Google which at first may sound contrary as the expectation is that Amazon "will take over the world". However one cannot do that without first conquering China. Firms like Alibaba and Tencent will have easy access to the global infrastructure being built by China in an attempt to speed up and ease trade in that region. The following guide shows how to get stock price in Excel.

We will explore companies using a:

1) Past

2) Present (including financial statements)

3) Future

4) Story/Tailwind

Method to find investing ideas in these regions. The tailwind is currently largest in the Asia region with 6%+ GDP growth according to the latest SEC form 4 from Edgar Company Search. This is relevant as investments in this region have a greater margin of safety; investing in a company that maintains flat market share should increase about 6% per year as the market growth size is so significant. The next article I will explore Alibaba (NYSE: BABA), and why I recently purchased a large position during the recent Ant Financial Crisis.

The global big data analytics market size was valued at $307.52 billion in 2023 & is projected to grow from $348.21 billion in 2024 to $961.89 billion by 2032

The global market size of IT Data collectors is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global IT Data collectors Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global IT Data collectors industry. The key insights of the report:
1.The report provides key statistics on the market status of the IT Data collectors manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of IT Data collectors industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of IT Data collectors Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of IT Data collectors as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of IT Data collectors market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY

The global market size of Internet Data Centers is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.

Global Internet Data Centers Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Internet Data Centers industry. The key insights of the report:

1.The report provides key statistics on the market status of the Internet Data Centers manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.

2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.

3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.

4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.

5.The report estimates 2019-2024 market development trends of Internet Data Centers industry.

6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out

7.The report makes some important proposals for a new project of Internet Data Centers Industry before evaluating its feasibility.

There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.

For competitor segment, the report includes global key players of Internet Data Centers as well as some small players. At least 8 companies are included:

* 21Vianet Group

* Amazon.com

* Apple

* AT&T Inc.

* BT Global Services plc

* CenturyLink,lnc.

For complete companies list, please ask for sample pages.

The information for each competitor includes:

* Company Profile

* Main Business Information

* SWOT Analysis

* Sales, Revenue, Price and Gross Margin

* Market Share



For product type segment, this report listed main product type of Internet Data Centers market

* Cloud Data Center

* Traditional Data Center

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.

* Application I

* Application II

* Application III



For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:

* North America

* South America

* Asia & Pacific

* Europe

* MEA (Middle East and Africa)

The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.



Reasons to Purchase this Report:

* Analyzing the outlook of the market with the recent trends and SWOT analysis

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* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects

* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the Internet Data Centers Market.

* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment

* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years

* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players

* 1-year analyst support, along

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Uruguay in 2006, 2010 and 2017. The Enterprise Survey is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for 2006, 2010 and 2017 Uruguay Enterprise Surveys were selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in Honduras ES: industry, establishment size, and region.

In 2006 ES, industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews.

In 2010 ES, industry stratification was designed in the way that follows: the universe was stratified into 3 manufacturing industries, 1 service industry -retail -, and 1 residual sector as defined in the sampling manual. All sectors had a target of 120 interviews. Regional stratification was defined in two regions (city and the surrounding business area): Montevideo and Canelones.

In 2017 ES, industry stratification was designed as follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15-37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72). For the Uruguay ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification was done across two regions: Montevideo and Canelones.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Indonesia Gross Output: Large-Medium Manufacturing: 13930: Carpet and rug industries data was reported at 3,079,023,309.000 IDR th in 2015. This records an increase from the previous number of 2,943,246,394.000 IDR th for 2014. Indonesia Gross Output: Large-Medium Manufacturing: 13930: Carpet and rug industries data is updated yearly, averaging 1,914,928,692.000 IDR th from Dec 2010 (Median) to 2015, with 5 observations. The data reached an all-time high of 3,079,023,309.000 IDR th in 2015 and a record low of 1,238,524,413.000 IDR th in 2010. Indonesia Gross Output: Large-Medium Manufacturing: 13930: Carpet and rug industries data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Mining and Manufacturing Sector – Table ID.BAG002: Manufacturing: ISIC Rev.4: Large and Medium Manufacturing: Gross Output.