The data allow to investigate the relationship between export sophistication and economic performance for 64 countries over 2005-2015 period, based on Hausmann, Hwang and Rodrik (2007). PRODY and EXPY measures are computed using domestic value-added exports available from TiVA dataset instead of gross exports. TiVA dataset covers 35 sectors including 21 manufacturing and 14 services sectors, which allows to measure the impact of goods and services on income, alike. Other variables are gathered from different datasets. A dynamic panel GMM approach is followed. Income ratio defined as lnGDPpc/lnEXPY is employed as the dependent variable. Explaining variables include economic structure, technological content of exports, and TiVA new variables including backward and forward linkages variables. Strong evidence of the positive effect of manufacturing sector on countries’ economic performance is found. Weak evidence has been provided in favor of exports led growth hypothesis when taking conventional exports data into account, with the exception of high tech. and ICT exported goods, which have strong positive and significant effect on income. Relying on TiVA new indicators give new insights into countries GVCs participation gains. Thus, backward linkages seem to have an important role given their positive and significant effect on income, either sourced from commodities or services activities. Forward linkages seem to have mixed effects, depending on the end use of the exported domestic value-added, playing a prominent income role when domestic value-added is reimported, embodied in foreign final demand or when re-exporting intermediate imports as share of intermediate imports, suggesting that countries should not take GVCs’ benefits for granted. Some results and correlations matrix are available.

Techsalerator’s Business Technographic Data for Vietnam: Unlocking Insights into Vietnam's Technology Landscape

Techsalerator’s Business Technographic Data for Vietnam provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Vietnam. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Vietnam, enabling technology vendors to target potential clients and allowing analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as accounting systems, customer management software, and cloud services. Understanding a company's technology stack is key to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can use this information to assess the level of technology adoption and interest among companies in Vietnam.

• Industry Sector: This field specifies the industry in which the company operates, such as manufacturing, retail, or finance. Knowing the industry helps vendors tailor their products to sector-specific demands and emerging trends in Vietnam.

• Geographic Location: This field identifies the company's headquarters or primary operations within Vietnam. Geographic information aids in regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Vietnam

• E-commerce Expansion: With a rapidly growing digital consumer base, Vietnamese companies are increasingly investing in e-commerce platforms, digital marketing, and online payment systems to capture a larger market share and enhance customer experience.

• Fintech Innovations: Vietnam’s fintech sector is experiencing significant growth, with businesses adopting advanced financial technologies such as mobile payment solutions, digital wallets, and blockchain to improve financial transactions and services.

• Smart Manufacturing: The manufacturing sector in Vietnam is embracing Industry 4.0 technologies, including automation, IoT, and AI-driven analytics, to enhance productivity, efficiency, and competitiveness in the global market.

• Cloud Computing and SaaS: Cloud-based solutions and Software-as-a-Service (SaaS) offerings are gaining traction, providing Vietnamese businesses with scalable and flexible IT infrastructure that supports remote work and digital transformation initiatives.

• Cybersecurity Enhancements: As digital activities increase, so does the need for robust cybersecurity measures. Companies in Vietnam are investing in advanced security solutions, including threat detection systems and data protection tools, to safeguard their operations and customer data.

Top 5 Companies with Notable Technographic Data in Vietnam

• Vietcombank: A leading financial institution, Vietcombank is implementing cutting-edge digital banking solutions, including mobile banking apps and secure online transaction systems, to enhance customer service and operational efficiency.

• Vingroup: As a major conglomerate, Vingroup leverages advanced technologies across its diverse business segments, including real estate, retail, and healthcare, integrating smart technologies and digital platforms into its operations.

• FPT Corporation: A major IT services and software development company, FPT is at the forefront of digital transformation in Vietnam, offering solutions in cloud computing, AI, and cybersecurity to both domestic and international clients.

• Masan Group: A leading consumer goods and retail company, Masan Group is adopting digital tools and e-commerce platforms to optimize its supply chain, enhance customer engagement, and drive business growth.

• VNPT: Vietnam’s largest telecommunications provider, VNPT is expanding its network infrastructure and investing in advanced technologies such as 5G and IoT to improve connectivity and support the digital economy.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Vietnam, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access ...

Business Confidence in the United States decreased to 48.20 points in November from 48.70 points in October of 2025. This dataset provides the latest reported value for - United States ISM Purchasing Managers Index (PMI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Techsalerator’s Import/Export Trade Data for Madagascar offers a detailed overview of international trade activities involving Malagasy companies. This dataset provides an in-depth examination of trade transactions, documenting and categorizing imports and exports across various industries in Madagascar.

To access Techsalerator’s Import/Export Trade Data for Madagascar, please contact us at info@techsalerator.com or visit Techsalerator Contact with your specific requirements. We will provide a customized quote based on your needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for Madagascar integrates information from customs reports, trade agreements, and shipping records, offering businesses, investors, and trade analysts a comprehensive understanding of Madagascar’s trade landscape.

Key Data Fields

• Company Name: Identifies the companies involved in trade transactions, helping to locate potential partners or competitors and track industry-specific trade patterns.
• Trade Volume: Details the quantity or value of goods traded, providing insights into the scale and economic impact of trade activities.
• Product Category: Specifies the types of goods traded, such as raw materials or finished products, aiding in understanding market demand and supply chain dynamics.
• Import/Export Country: Identifies the countries of origin or destination for traded goods, revealing regional trade relationships and market access.
• Transaction Date: Records the date of transactions, showcasing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in Madagascar

• Agricultural Exports: Madagascar’s economy is heavily influenced by agricultural exports, including vanilla, cloves, and coffee, which are significant contributors to the country’s trade balance.
• Mineral Resources: The export of minerals, particularly nickel and cobalt, plays a crucial role in Madagascar’s trade dynamics, reflecting the country’s rich mineral resources.
• Growth in Tourism: As tourism grows, there is an increasing export of services related to travel and hospitality, contributing to Madagascar’s economic development.
• Diversification Efforts: Efforts to diversify the economy are evident, with expanding sectors such as textiles and manufacturing gaining prominence in trade data.
• Environmental and Sustainability Focus: Madagascar is increasingly integrating sustainable practices into trade, particularly in sectors like agriculture and mining, to balance economic growth with environmental conservation.

Notable Companies in Malagasy Trade Data

• AustCham: A major player in the export of nickel and other minerals, contributing significantly to Madagascar’s mineral trade.
• Madagascar Oil: Involved in the oil and gas sector, playing a role in the export of petroleum products and related services.
• Vanilla Madagascar: A key exporter of vanilla, impacting global vanilla markets and reflecting Madagascar’s prominence in the spice trade.
• MadaOutillage: Specializes in the export of industrial tools and equipment, supporting Madagascar’s growing manufacturing sector.
• Tsarasaotra: Known for its involvement in the export of agricultural products, particularly coffee and cloves, highlighting its role in the country’s agribusiness sector.

Accessing Techsalerator’s Data

To access Techsalerator’s Import/Export Trade Data for Madagascar, please reach out to us at info@techsalerator.com with your specific requirements. We will provide a tailored quote based on the number of data fields and records needed, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

• Company Name
• Trade Volume
• Product Category
• Import/Export Country
• Transaction Date
• Shipping Details
• Customs Codes
• Trade Value

For detailed insights into Madagascar’s import and export activities and trends, Techsalerator’s dataset is an essential resource for making informed and strategic decisions.

Dataset Management for Machine Vision Market Outlook

According to our latest research, the global dataset management for machine vision market size reached USD 1.72 billion in 2024, supported by a robust demand for automated, data-driven quality assurance solutions across multiple industries. The market is experiencing a strong growth rate, posting a CAGR of 13.4% during the forecast period. By 2033, the dataset management for machine vision market is expected to achieve a value of approximately USD 5.28 billion, propelled by advances in artificial intelligence, the proliferation of industrial automation, and the increasing need for high-quality, labeled datasets to train sophisticated machine vision systems. As per our latest research, key growth factors include the rapid adoption of Industry 4.0, the expansion of smart manufacturing, and increasing investments in AI-powered visual inspection technologies.

One of the primary growth drivers in the dataset management for machine vision market is the surge in demand for automated quality inspection systems across manufacturing sectors. As industries strive to minimize defects and improve product consistency, machine vision systems have become indispensable. These systems rely on vast, well-organized datasets to train algorithms that can detect minute anomalies in real time. The rise of smart factories, which leverage IoT and AI technologies, further amplifies the need for efficient dataset management solutions. Companies are investing heavily in software and services that can handle the complexities of data labeling, annotation, storage, and retrieval, ensuring their machine vision models remain accurate and reliable. Furthermore, as regulatory standards for quality and safety tighten globally, manufacturers are compelled to adopt robust dataset management practices, fueling continued market expansion.

Another significant growth factor is the rapid evolution of machine learning and deep learning algorithms, which require enormous volumes of high-quality data to achieve optimal performance. The complexity of modern machine vision tasks, such as object detection, predictive maintenance, and image classification, necessitates advanced dataset management solutions capable of handling diverse data types and formats. Vendors are responding by offering platforms that automate data curation, enable collaborative labeling workflows, and provide seamless integration with cloud infrastructure. The ability to efficiently manage datasets not only accelerates model development cycles but also enhances the scalability and adaptability of machine vision applications across different industries. This trend is particularly pronounced in sectors such as automotive, electronics, and pharmaceuticals, where precision and reliability are paramount.

The increasing adoption of cloud-based deployment models is also shaping the dataset management for machine vision market. Cloud solutions offer unparalleled scalability, flexibility, and cost-effectiveness, making them attractive to organizations of all sizes. By leveraging cloud infrastructure, companies can centralize their dataset management processes, facilitate remote collaboration, and access advanced analytics tools. This shift is particularly beneficial for enterprises with geographically dispersed operations or those engaged in global supply chains. Moreover, the integration of cloud-based AI and machine vision services is enabling real-time data processing and model updates, further driving the demand for sophisticated dataset management platforms. As cloud adoption continues to rise, vendors are expected to enhance their offerings with features such as automated data augmentation, compliance management, and robust security protocols.

Regionally, Asia Pacific stands out as the fastest-growing market for dataset management in machine vision, driven by the rapid industrialization of countries such as China, Japan, and South Korea. North America and Europe also hold significant shares, thanks to their mature manufacturing sectors and early adoption of advanced automation technologies. In contrast, Latin America and the Middle East & Africa are witnessing steady growth, supported by increasing investments in infrastructure and the gradual embrace of smart manufacturing practices. Each region presents unique opportunities and challenges, shaped by local regulatory environments, industry dynamics, and technological readiness.

Component Analysis

W

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Zambia in 2007 and 2013, as part of Africa Enterprise Surveys roll-out, an initiative of the World Bank.

Zambia ES 2013 was conducted between December 2012 and February 2014, Zambia ES 2007 was carried out in October and November 2007. The objective of the Enterprise Survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

Data from 1,204 establishments was analyzed: 568 businesses were from 2013 ES only, 332 - from 2007 ES only, and 304 firms were from both 2007 and 2013 panels.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an 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 private 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. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Zambia ES 2013 was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

Industry stratification was designed in the way that follows: the universe was stratified into four manufacturing industries (food, textiles and garments, chemicals and plastics, other manufacturing) and two service sectors (retail and other services).

Size stratification was defined following the standardized definition for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees).

Regional stratification for the Zambia ES was defined in five regions: Kitwe, Livingstone, Lusaka, and Ndola.

One of the sampling frames for Zambia ES 2013 consisted of enterprises interviewed in Zambia 2007. The World Bank required that attempts should be made to re-interview establishments responding to the Zambia 2007 survey where they were within the selected geographical regions and met eligibility criteria. Due to the fact that the previous round of surveys seemed to have utilized different stratification criteria (or no stratification at all) and due to the prevalence of small firms and firms located in the capital city in the 2007 sample the following convention was used. The presence of panel firms was limited to a maximum of 50% of the achieved interviews in each cell. That sample is referred to as the Panel.

The sample for Zambia ES 2007 was drawn from a master list obtained by compiling two different updates of a list of establishments provided by Central Statistical Office. During the survey period, the master list was updated as new information regarding establishments that had closed or were out-of-scope was gathered and other establishments were added. The final population size in all strata and locations was 3,336.

The 2007 survey included panel data collected from establishments surveyed in 2003. That survey included establishments in all four manufacturing strata distributed across the entire country. In order to collect the largest possible set of panel data, an attempt was made to contact and survey every establishment in the panel, provided it was located in one of the four cities covered by this survey, it operated in the universe under study, and that the number of panel firms of a certain size in a given industry in a given city did not exceed the number of establishments in the corresponding sample structure. The remainder of the sample (including the entire rest of universe and retail sample in each city) was selected at random from the master list by a computer program.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used for Zambia ES 2013: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth. There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

The following survey instruments were used for Zambia ES 2007: - Core Questionnaire + Manufacturing Module; - Core Questionnaire + Retail Module; - Core Questionnaire.

Most of the questions in all three questionnaires are the same. The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

Techsalerator’s Import/Export Trade Data for San Marino

Techsalerator’s Import/Export Trade Data for San Marino provides a thorough and insightful collection of information on international trade activities involving San Marino-based companies. This dataset offers a detailed examination of trade transactions, documenting and classifying imports and exports across various industries within San Marino.

To obtain Techsalerator’s Import/Export Trade Data for San Marino, please reach out to info@techsalerator.com or visit Techsalerator Contact Us with your specific requirements. Techsalerator will provide a customized quote based on your data needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for San Marino delivers a thorough analysis of trade activities, integrating data from customs reports, trade agreements, and shipping records. This comprehensive dataset helps businesses, investors, and trade analysts understand San Marino’s trade landscape in detail.

Key Data Fields

• Company Name: Lists the companies involved in trade transactions. This information helps identify potential partners or competitors and track industry-specific trade patterns.
• Trade Volume: Details the quantity or value of goods traded, providing insights into the scale and economic impact of trade activities.
• Product Category: Specifies the types of goods traded, such as raw materials or finished products, aiding in understanding market demand and supply chain dynamics.
• Import/Export Country: Identifies the countries of origin or destination for traded goods, offering insights into regional trade relationships and market access.
• Transaction Date: Records the date of transactions, revealing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in San Marino

• Trade Balance Dynamics: San Marino’s trade balance shows fluctuations influenced by its major trade partners, including Italy. Trade agreements and economic policies continue to shape the country’s trade dynamics.
• Italy-San Marino Trade Relations: The trade relationship with Italy is central to San Marino’s economy, given its geographical proximity and economic ties. This partnership influences significant aspects of San Marino's trade policies.
• Expansion of Trade Networks: San Marino is working to diversify its trade partners beyond traditional relationships, aiming for a broader global trade presence.
• Focus on Luxury Goods: San Marino has a notable trade in luxury goods and high-value products, reflecting its status as a niche market for exclusive items.
• Emphasis on Sustainable Trade: There is a growing emphasis on incorporating sustainability into trade practices, promoting environmentally friendly policies and technologies.

Notable Companies in San Marino Trade Data

• Banca di San Marino: Involved in financial services, including the import and export of financial products and services.
• Timbro S.p.A.: A significant player in the manufacturing sector, known for exporting high-quality stamps and printing products.
• SMI Group: A major entity involved in the export of specialized industrial equipment, reflecting San Marino’s role in niche markets.
• San Marino Innovation: A key organization promoting technological trade and innovation, impacting San Marino’s trade in tech products and services.
• Palladium Srl: A company involved in the import and export of precious metals and jewelry, highlighting San Marino’s involvement in high-value trade sectors.

Accessing Techsalerator’s Data

To obtain Techsalerator’s Import/Export Trade Data for San Marino, please contact us at info@techsalerator.com with your requirements. We will provide a customized quote based on the number of data fields and records needed, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

• Company Name
• Trade Volume
• Product Category
• Import/Export Country
• Transaction Date
• Shipping Details
• Customs Codes
• Trade Value

For detailed insights into San Marino’s import and export activities and trends, Techsalerator’s dataset is an invaluable resource for staying informed and making strategic decisions.

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).

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Argentina in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

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

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 sample for the 2006-2017 Argentina Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

Three levels of stratification were used in every country: industry, establishment size, and region.

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 middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Argentina ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don't know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from "Don't know" responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

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Robotics Synthetic Data Services Market Outlook

According to our latest research, the global Robotics Synthetic Data Services market size reached USD 1.42 billion in 2024, demonstrating robust expansion driven by the accelerating adoption of AI and robotics across diverse industries. The market is set to grow at a CAGR of 34.8% from 2025 to 2033, reaching an estimated USD 19.62 billion by 2033. This explosive growth is fueled by the increasing need for high-quality, scalable, and cost-effective training data to enhance the accuracy and reliability of robotic systems. The proliferation of autonomous and intelligent robotics solutions in sectors such as automotive, healthcare, and manufacturing is a primary catalyst behind this surge, as organizations seek to overcome the limitations of real-world data collection and annotation.

One of the principal growth factors driving the Robotics Synthetic Data Services market is the rapid evolution of artificial intelligence and machine learning technologies within the robotics domain. As robotics systems become more sophisticated, the demand for comprehensive datasets that can simulate a wide range of real-world scenarios has intensified. Traditional data collection methods are often expensive, time-consuming, and limited in scope, creating a bottleneck for AI model development. Synthetic data services address these challenges by generating highly realistic, diverse, and customizable datasets that enable accelerated training and validation cycles. This, in turn, significantly reduces time-to-market for robotics solutions, while enhancing their operational safety and performance in complex environments.

Another influential driver is the increasing deployment of autonomous vehicles and advanced industrial robots across multiple industries. In automotive manufacturing, for example, synthetic data is essential for training autonomous driving algorithms to recognize and respond to a myriad of road conditions, obstacles, and human behaviors. Similarly, in industrial robotics, synthetic data enables the simulation of intricate assembly line processes, object detection, and manipulation tasks that would be difficult or impractical to capture in real-world settings. As companies strive to achieve higher levels of automation and operational efficiency, the adoption of robotics synthetic data services is becoming integral to their digital transformation strategies.

The growing emphasis on regulatory compliance and data privacy is also shaping the trajectory of the Robotics Synthetic Data Services market. In sectors such as healthcare and defense, stringent regulations restrict the use of real-world data due to privacy concerns and security risks. Synthetic data provides a viable alternative, enabling organizations to develop and test robotics applications without compromising sensitive information. Furthermore, synthetic data can be engineered to include rare or hazardous scenarios, ensuring that robotic systems are robustly trained for edge cases that may never be encountered in traditional datasets. This regulatory-driven demand is expected to further accelerate market growth in the coming years.

From a regional perspective, North America currently dominates the Robotics Synthetic Data Services market, accounting for the largest share in 2024. The region's leadership is underpinned by substantial investments in AI research, a mature robotics ecosystem, and the presence of key technology providers. However, the Asia Pacific region is projected to exhibit the fastest growth over the forecast period, driven by rapid industrialization, government initiatives supporting automation, and the expansion of manufacturing hubs in countries like China, Japan, and South Korea. Europe also remains a significant market, particularly in automotive and healthcare robotics, benefiting from robust R&D activities and stringent regulatory frameworks that favor synthetic data adoption.

Component Analysis

The Robotics Synthetic Data Services

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Cloud Database Market Outlook

According to our latest research, the global cloud database market size reached USD 21.8 billion in 2024, reflecting robust adoption across industries due to increasing digital transformation initiatives. The market is expected to maintain a strong growth trajectory, with a CAGR of 15.2% from 2025 to 2033. By the end of the forecast period in 2033, the cloud database market is projected to achieve a value of USD 58.7 billion. This growth is primarily fueled by the escalating demand for scalable, flexible, and cost-efficient data management solutions, as organizations leverage cloud technologies to drive innovation and operational efficiency.

One of the primary growth factors for the cloud database market is the exponential increase in data generation across enterprises of all sizes and sectors. The proliferation of IoT devices, mobile applications, and digital business models has resulted in vast amounts of structured and unstructured data that require agile and scalable storage solutions. Cloud databases offer seamless scalability, high availability, and real-time data processing capabilities, making them an ideal choice for organizations aiming to harness big data analytics and derive actionable insights. Furthermore, the shift to remote and hybrid work environments has accelerated cloud adoption, as businesses seek to ensure data accessibility and collaboration across distributed teams.

Another significant driver is the continuous advancement in cloud computing technologies, including the integration of artificial intelligence (AI) and machine learning (ML) with cloud databases. Leading cloud service providers are investing heavily in enhancing their database offerings with advanced analytics, automated management, and security features. These innovations are lowering the barriers to entry for enterprises, enabling them to deploy sophisticated database solutions without the need for extensive in-house IT expertise. Additionally, the rise of multi-cloud and hybrid cloud strategies is giving organizations greater flexibility to optimize workloads, enhance disaster recovery, and comply with data sovereignty regulations.

The rapid digitalization of core business processes across industry verticals is also contributing to the robust growth of the cloud database market. Sectors such as BFSI, healthcare, retail, and manufacturing are leveraging cloud databases to modernize legacy systems, improve customer experiences, and launch new digital services. Regulatory compliance, data security, and the need for real-time analytics are driving enterprises to adopt cloud-native and managed database solutions. As a result, cloud databases are becoming integral to enterprise IT strategies, enabling organizations to remain competitive in an increasingly data-driven economy.

From a regional perspective, North America continues to dominate the global cloud database market, owing to the presence of major cloud service providers, early technology adoption, and mature digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid economic growth, increasing cloud investments, and the digital transformation of small and medium enterprises (SMEs). Europe is also witnessing significant adoption, particularly in sectors such as BFSI and government, where data privacy and compliance are paramount. The Middle East & Africa and Latin America are gradually catching up, driven by growing awareness of cloud benefits and government-led digital initiatives.

Database Type Analysis

The cloud database market is segmented by database type into SQL, NoSQL, NewSQL, and others, each catering to distinct data management needs and application scenarios. SQL databases remain the backbone of enterprise data management, favored for their robust transactional support, data integrity, and mature ecosystem. These databases are widely used in industries with stringent data consistency and regulatory requirements, such as banking, government, and h

Edge Database as a Service Market Outlook

According to our latest research, the Global Edge Database as a Service market size was valued at $1.8 billion in 2024 and is projected to reach $9.7 billion by 2033, expanding at a robust CAGR of 20.7% during the forecast period of 2025–2033. The primary factor fueling this remarkable growth is the exponential increase in connected devices and the need for real-time data processing at the network edge, which is driving enterprises to seek scalable, low-latency database solutions. As organizations in diverse sectors strive to harness the power of edge computing for faster analytics and improved decision-making, the demand for Edge Database as a Service (DBaaS) is set to surge globally.

Regional Outlook

North America currently holds the largest share in the Edge Database as a Service market, accounting for over 38% of global revenue in 2024. This dominance is attributed to the region's mature IT infrastructure, high adoption of advanced technologies, and proactive digital transformation initiatives across industries such as BFSI, healthcare, and manufacturing. The presence of leading cloud service providers and edge computing innovators, coupled with supportive regulatory frameworks, further solidifies North America's leadership. Enterprises in the United States and Canada are increasingly deploying edge DBaaS solutions to support mission-critical, latency-sensitive applications, ensuring data compliance and security in line with stringent data privacy laws. These factors, combined with a robust ecosystem of technology vendors and early adopters, position North America as the cornerstone of global market growth.

The Asia Pacific region is forecasted to be the fastest-growing market, boasting a projected CAGR of 25.3% from 2025 to 2033. This rapid expansion is underpinned by the explosive growth in IoT deployments, smart city initiatives, and the proliferation of 5G networks across China, India, Japan, and Southeast Asia. Governments and private enterprises in the region are investing heavily in digital infrastructure, fostering innovation and the adoption of edge computing solutions. Additionally, the rise of Industry 4.0, coupled with a burgeoning e-commerce sector, is generating unprecedented volumes of data at the edge, necessitating agile and scalable database services. The increasing integration of AI and machine learning at the edge is also driving demand for advanced DBaaS platforms capable of supporting complex, real-time analytics workloads.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing Edge Database as a Service, albeit with unique adoption challenges. These regions face hurdles such as limited high-speed connectivity, fragmented regulatory environments, and a shortage of skilled IT professionals. Nevertheless, localized demand is rising in sectors like energy, utilities, and retail, where edge computing can deliver tangible benefits in terms of operational efficiency and customer engagement. Policymakers are beginning to recognize the importance of digital transformation, introducing incentives and pilot projects to stimulate adoption. As infrastructure investments accelerate and awareness grows, these markets are poised to contribute more significantly to global market expansion, particularly as localized use cases and tailored solutions gain traction.

Report Scope

Cloud Computing Market Growth | Industry Analysis, Size & Forecast Report

Dataset updated: Jun 27, 2024

Dataset authored and provided by: Mordor Intelligence

License: https://www.mordorintelligence.com/privacy-policy

Time period covered: 2019 - 2029

Area covered: Global

Variables measured: CAGR, Market size, Market share analysis, Global trends, Industry forecast

Description: The Cloud Computing Market size is estimated at USD 0.68 trillion in 2024, and is expected to reach USD 1.44 trillion by 2029, growing at a CAGR of 16.40% during the forecast period (2024-2029).

Report Attribute

Quantitative Units: Revenue in USD Billion, Volumes in Units, Pricing in USD

Regions and Countries Covered:

Industry Segmentation Covered:

By Cloud Computing: IaaS, SaaS, PaaS

By End-User: IT and Telecom, BFSI, Retail and Consumer Goods, Manufacturing, Healthcare, Media and Entertainment

Market Players Covered: Amazon Web Services, Google LLC, Microsoft Corporation, Alibaba Cloud, and Salesforce

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Peru in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

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

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 sample for the 2006-2017 Peru Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

Three levels of stratification were used in every country: industry, establishment size, and region.

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 middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Peru ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

Time-Series Database for OT Data Market Outlook

According to our latest research, the Global Time-Series Database for OT Data Market size was valued at $1.7 billion in 2024 and is projected to reach $6.3 billion by 2033, expanding at an impressive CAGR of 15.7% during the forecast period of 2025–2033. One of the primary factors driving this robust growth is the accelerating digital transformation across operational technology (OT) environments, especially in sectors such as manufacturing, energy, and utilities. As organizations increasingly deploy IoT devices and smart sensors within their OT infrastructure, the volume and velocity of time-series data generated have surged, necessitating advanced database solutions tailored for real-time analytics, predictive maintenance, and process optimization. The demand for scalable, high-performance time-series databases is further amplified by the growing emphasis on Industry 4.0 initiatives and the need for seamless integration between IT and OT systems, enabling enterprises to unlock actionable insights from their operational data.

Regional Outlook

North America currently holds the largest share of the global time-series database for OT data market, accounting for nearly 38% of total revenue in 2024. This dominance is attributed to the region’s mature industrial sector, early adoption of digital transformation strategies, and a robust ecosystem of technology providers. The United States, in particular, has been at the forefront of deploying advanced OT data management systems, driven by stringent regulatory requirements for asset monitoring, a high concentration of manufacturing and energy enterprises, and significant investments in R&D. Additionally, the presence of leading time-series database vendors and cloud service providers has fostered a competitive landscape that accelerates innovation and market penetration. North America’s proactive policy environment, promoting smart manufacturing and energy efficiency, further cements its leadership position in this market.

In contrast, the Asia Pacific region is emerging as the fastest-growing market, projected to register a remarkable CAGR of 19.2% from 2025 to 2033. This rapid expansion is underpinned by the region’s ongoing industrialization, significant investments in smart infrastructure, and the proliferation of IoT devices across manufacturing, transportation, and utilities. Countries such as China, Japan, South Korea, and India are witnessing accelerated adoption of time-series database solutions to support predictive maintenance, asset management, and process optimization initiatives. Government-led digitalization programs, coupled with a surge in foreign direct investment in industrial automation, are propelling market growth. The increasing focus on energy efficiency, grid modernization, and smart city projects further boosts the demand for real-time OT data management platforms in the Asia Pacific.

Meanwhile, emerging economies in Latin America, the Middle East, and Africa are gradually embracing time-series databases for OT data, albeit at a slower pace due to infrastructural limitations and budgetary constraints. Adoption in these regions is often driven by localized demand in sectors such as oil & gas, mining, and utilities, where real-time monitoring and asset optimization are critical. However, challenges such as limited access to advanced technologies, skill shortages, and inconsistent regulatory frameworks can impede widespread deployment. Despite these hurdles, increasing awareness of the benefits of digital transformation and ongoing policy reforms to attract foreign investment are expected to gradually improve adoption rates, positioning these regions as potential growth frontiers over the long term.

Report Scope

Techsalerator’s Import/Export Trade Data for Kenya offers an in-depth view of international trade activities involving Kenyan companies. This dataset provides a detailed examination of trade transactions, documenting and categorizing imports and exports across various industries within Kenya.

To access Techsalerator’s Import/Export Trade Data for Kenya, please contact us at info@techsalerator.com or visit Techsalerator Contact with your specific requirements. We will provide a customized quote based on your needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for Kenya integrates information from customs reports, trade agreements, and shipping records, providing businesses, investors, and trade analysts with a comprehensive understanding of Kenya’s trade landscape.

Key Data Fields

• Company Name: Identifies the companies involved in trade transactions, aiding in locating potential partners or competitors and tracking industry-specific trade patterns.
• Trade Volume: Provides details on the quantity or value of goods traded, offering insights into the scale and economic impact of trade activities.
• Product Category: Specifies the types of goods traded, such as raw materials or finished products, helping to understand market demand and supply chain dynamics.
• Import/Export Country: Identifies the countries of origin or destination for traded goods, revealing regional trade relationships and market access.
• Transaction Date: Records the date of transactions, showcasing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in Kenya

• Agricultural Exports: Kenya's economy benefits significantly from agricultural exports, including tea, coffee, and flowers, which are major contributors to the country’s export revenues.
• Manufacturing Growth: There is a growing emphasis on expanding the manufacturing sector in Kenya, including textiles, chemicals, and processed foods, reflecting efforts to enhance value-added exports.
• Regional Trade Hub: Kenya plays a crucial role as a regional trade hub within East Africa, with substantial trade relationships with neighboring countries and participation in regional trade agreements.
• Increased Imports of Machinery and Equipment: The country imports a significant amount of machinery and equipment for infrastructure development, mining, and industrialization projects.
• Digital and E-commerce Expansion: Kenya’s vibrant tech sector and growing e-commerce market are influencing trade, particularly in the digital and technology-driven industries.

Notable Companies in Kenyan Trade Data

• Kenya Tea Development Agency (KTDA): A major player in the export of tea, contributing significantly to Kenya’s agricultural export sector.
• Kenya Airways: The national carrier involved in air freight services, facilitating the export of goods such as fresh produce and flowers.
• Bidco Africa: A leading manufacturer in Kenya, exporting products like edible oils and beverages, reflecting the country’s growing industrial base.
• EABL (East African Breweries Limited): A prominent company in the beverage industry, known for exporting alcoholic and non-alcoholic drinks.
• Safaricom: While primarily a telecommunications provider, Safaricom’s involvement in digital services and mobile commerce impacts trade dynamics in the tech sector.

Accessing Techsalerator’s Data

To access Techsalerator’s Import/Export Trade Data for Kenya, please reach out to us at info@techsalerator.com with your specific requirements. We will provide a tailored quote based on the number of data fields and records required, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

• Company Name
• Trade Volume
• Product Category
• Import/Export Country
• Transaction Date
• Shipping Details
• Customs Codes
• Trade Value

For detailed insights into Kenya’s import and export activities and trends, Techsalerator’s dataset is an essential resource for making informed and strategic decisions.