The Gross Domestic Product (GDP) in China expanded 5.40 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides - China GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in India expanded 7.40 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides - India GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Explore real GDP growth projections dataset, including insights into the impact of COVID-19 on economic trends. This dataset covers countries such as Spain, Australia, France, Italy, Brazil, and more.

growth rate, Real, COVID-19, GDP

Spain, Australia, France, Italy, Brazil, Argentina, United Kingdom, United States, Canada, Russia, Turkiye, World, China, Mexico, Korea, India, Saudi Arabia, South Africa, Germany, Indonesia, JapanFollow data.kapsarc.org for timely data to advance energy economics research..Source: OECD Economic Outlook database.- India projections are based on fiscal years, starting in April. The European Union is a full member of the G20, but the G20 aggregate only includes countries that are also members in their own right. Spain is a permanent invitee to the G20. World and G20 aggregates use moving nominal GDP weights at purchasing power parities. Difference in percentage points, based on rounded figures.

The statistic shows the growth of the real gross domestic product (GDP) in India from 2020 to 2024, with projections up until 2030. GDP refers to the total market value of all goods and services that are produced within a country per year. It is an important indicator of the economic strength of a country. Real GDP is adjusted for price changes and is therefore regarded as a key indicator for economic growth. In 2024, India's real gross domestic product growth was at about 6.46 percent compared to the previous year. Gross domestic product (GDP) growth rate in India Recent years have witnessed a shift of economic power and attention to the strengthening economies of the BRIC countries: Brazil, Russia, India, and China. The growth rate of gross domestic product in the BRIC countries is overwhelmingly larger than in traditionally strong economies, such as the United States and Germany. While the United States can claim the title of the largest economy in the world by almost any measure, China nabs the second-largest share of global GDP, with India racing Japan for third-largest position. Despite the world-wide recession in 2008 and 2009, India still managed to record impressive GDP growth rates, especially when most of the world recorded negative growth in at least one of those years. Part of the reason for India’s success is the economic liberalization that started in 1991and encouraged trade subsequently ending some public monopolies. GDP growth has slowed in recent years, due in part to skyrocketing inflation. India’s workforce is expanding in the industry and services sectors, growing partially because of international outsourcing — a profitable venture for the Indian economy. The agriculture sector in India is still a global power, producing more wheat or tea than anyone in the world except for China. However, with the mechanization of a lot of processes and the rapidly growing population, India’s unemployment rate remains relatively high.

This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

Five crises of human capital: a pathway to achieving socio-economic parity with developed nations in Russia today

This dataset contains selected indicators for monitoring progress towards green growth to support policy making and inform the public at large. The indicator bring together the OECD's statistics, indicators and measures of progress. The dataset covers OECD countries as well as BRIICS economies (Brazil, Russian Federation, India, Indonesia, China and South Africa), and selected countries when possible. The indicators are selected according to well specified criteria and embedded in a conceptual framework, which is structured around four groups to capture the main features of green growth: Environmental and resource productivity, to indicate whether economic growth is becoming greener with more efficient use of natural capital and to capture aspects of production which are rarely quantified in economic models and accounting frameworks; The natural asset base, to indicate the risks to growth from a declining natural asset base; Environmental quality of life, to indicate how environmental conditions affect the quality of life and wellbeing of people; Economic opportunities and policy responses, to indicate the effectiveness ofpolicies in delivering green growth and describe the societal responses needed to secure business and employment opportunities.

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..

Big Ten Countries include Argentina, Brazil, China, India, Indonesia, Mexico, Poland, South Africa, South Korea, and Turkey. The annual data for the years 2002-2019 was used. Growth Rate (GR), the literature’s basic economic variable, is selected as the dependent variable. As for the independent variable, the “Global Terror Index (GTI)” was used to represent the terror indicator. Besides, due to their effect on the growth rate, the ratio of Foreign Direct Investment (FDI) to the Gross Domestic Product (GDP), and the ratio of External Balance (EB) to Gross Domestic Product (GDP) are included in the model as the control variables.

PREDICT includes statistics on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries - the EU28 plus Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia -. The dataset provides indicators in a wide variety of topics, including value added, employment, labour productivity and business R&D expenditure (BERD), distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on Government financing of R&D in ICTs, and total R&D expenditure. Nowcasting of more relevant data in these domains is also performed until a year before the reference date, while time series go back to 1995.

ICTs determine competitive power in the knowledge economy. The ICT sector alone originates almost one fourth of total Business expenditure in R&D (BERD) for the aggregate of the 40 economies under scrutiny in the project. It also has a huge enabling role for innovation in other technological domains. This is reflected at the EU policy level, where the Digital Agenda for Europe in 2010 was identified as one of the seven pillars of the Europe 2020 Strategy for growth in the Union; and the achievement of a Digital Single Market (DSM) is one of the 10 political priorities set by the Commission since 2015.

The 2021 PREDICT Dataset updates and substitutes the 2020 PREDICT Dataset.

PREDICT includes statistics on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries - the EU28 plus Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia -. The dataset provides indicators in a wide variety of topics, including value added, employment, labour productivity and business R&D expenditure (BERD), distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on Government financing of R&D in ICTs, and total R&D expenditure. Nowcasting of more relevant data in these domains is also performed until a year before the reference date, while time series go back to 1995.

ICTs determine competitive power in the knowledge economy. The ICT sector alone originates almost one fourth of total Business expenditure in R&D (BERD) for the aggregate of the 40 economies under scrutiny in the project. It also has a huge enabling role for innovation in other technological domains. This is reflected at the EU policy level, where the Digital Agenda for Europe in 2010 was identified as one of the seven pillars of the Europe 2020 Strategy for growth in the Union; and the achievement of a Digital Single Market (DSM) is one of the 10 political priorities set by the Commission since 2015.

Database Audit and Protection Market Outlook

The Database Audit and Protection market size was valued at USD 4 billion in 2023 and is projected to reach USD 8.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 9%. This robust growth can be attributed to the increasing necessity for organizations to safeguard their data amidst a growing landscape of cyber threats and stringent regulatory requirements. Enterprises worldwide are prioritizing data security to protect their customer and operational data from breaches, unauthorized access, and tampering, fueling the demand for database audit and protection solutions.

One of the significant growth factors for the database audit and protection market is the escalating volume of data generated across various sectors. As businesses continue to digitize their operations, the influx of data has increased exponentially, making databases a prime target for cybercriminals. Consequently, organizations are increasingly investing in robust security tools to audit and protect their databases. This demand is further amplified by the adoption of big data analytics and the Internet of Things (IoT), which require comprehensive database security solutions to manage and protect the vast amounts of data being generated and processed.

Another driving force behind market growth is the rising awareness of legal and compliance obligations associated with data protection. Regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and others compel organizations to implement stringent security measures to protect personal and sensitive data. Failure to comply with these regulations can result in hefty fines and reputational damage, which has propelled companies to adopt reliable database audit and protection measures. The increasing focus on maintaining transparency and accountability in data handling practices further boosts the market's expansion.

Technological advancements are also significantly contributing to the market's growth. Innovations in artificial intelligence (AI) and machine learning (ML) are transforming database security solutions, offering advanced analytics, real-time monitoring, and automated threat detection and response capabilities. These technologies enable more efficient and proactive database protection, thus gaining favor among businesses seeking to enhance their security posture. Moreover, the integration of blockchain technology for secure data storage and transaction recording is emerging as a prominent trend, promising to further drive the market's growth.

Regionally, North America currently dominates the database audit and protection market due to the presence of major market players, advanced IT infrastructure, and high adoption rates of innovative technologies. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by the rapid digital transformation of businesses, increasing number of small and medium enterprises, and growing awareness of data security. The strong economic growth in countries like China and India, along with government initiatives promoting cybersecurity, are key factors contributing to this regional expansion.

Component Analysis

The component segment of the Database Audit and Protection market is bifurcated into software and services. Software solutions are critical to the market and encompass a range of functionalities including database activity monitoring, vulnerability assessment, data masking, and auditing. With businesses increasingly investing in comprehensive database protection software to ensure robust security against unauthorized access and cyber threats, this segment is expected to maintain a dominant position. The continuous evolution of software solutions, integrating capabilities like AI and machine learning to deliver predictive analytics and real-time threat detection, further enhances their appeal and drives market growth.

In parallel, the services segment is witnessing notable growth as organizations seek specialized expertise to manage and optimize their database security strategies. Services range from consultation and deployment to ongoing maintenance and training, ensuring that enterprises can effectively utilize their software solutions. With the growing complexity of threats and the rapid evolution of technology, many companies are opting for managed security services to stay ahead of potential vulnerabilities and ensure compliance with regulatory standards. The increasing trend of outsourcing security management to expert servic

NOTE: The 2018 PREDICT Dataset has been deprecated, and it is now superseded by its latest edition - 2019 PREDICT Dataset:

http://data.europa.eu/89h/6c6f7ce7-893b-48e9-b074-2baaa4b6c7d8

PREDICT includes statistics on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries - the EU28 plus Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia -. The dataset provides indicators in a wide variety of topics, including value added, employment, labour productivity and business R&D expenditure (BERD), distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on Government financing of R&D in ICTs, and total R&D expenditure. Nowcasting of more relevant data in these domains is also performed until a year before the reference date, while time series go back to 1995.

ICTs determine competitive power in the knowledge economy. The ICT sector alone originates almost one fourth of total Business expenditure in R&D (BERD) for the aggregate of the 40 economies under scrutiny in the project. It also has a huge enabling role for innovation in other technological domains. This is reflected at the EU policy level, where the Digital Agenda for Europe in 2010 was identified as one of the seven pillars of the Europe 2020 Strategy for growth in the Union; and the achievement of a Digital Single Market (DSM) is one of the 10 political priorities set by the Commission since 2015.

The Database Solutions market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions and the expanding need for data management in large enterprises and SMEs. The market, valued at approximately $150 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant growth is fueled by several key factors. The shift towards cloud computing offers scalability, cost-effectiveness, and enhanced accessibility, leading to widespread adoption of cloud-based database solutions. Furthermore, the exponential growth of data generated by businesses across various sectors necessitates robust and efficient database management systems. The rise of big data analytics and artificial intelligence further fuels demand, as organizations require advanced database solutions to handle and process massive datasets for insightful decision-making. While the on-premise segment still holds a significant share, the cloud-based segment is rapidly gaining traction, projected to dominate the market in the coming years. Competition among major players like IBM, Amazon, Oracle, Microsoft, and SAP, along with emerging players in the space, is driving innovation and fostering a competitive landscape. However, challenges remain, including data security concerns, the complexity of integrating diverse database systems, and the need for skilled professionals to manage these increasingly sophisticated technologies. The segmentation of the market reveals distinct growth patterns. Large enterprises, with their substantial data management needs, represent a larger market segment compared to SMEs. However, the SME segment is also experiencing significant growth as businesses of all sizes recognize the importance of data-driven decision-making. Geographically, North America and Europe currently hold the largest market shares, driven by early adoption and established technological infrastructure. However, Asia-Pacific is emerging as a rapidly expanding market, fueled by strong economic growth and increasing digitalization across countries like China and India. The overall market is expected to continue its upward trajectory, driven by technological advancements, increasing data volumes, and the growing need for efficient data management across all sectors. The forecast period of 2025-2033 promises substantial opportunities for both established and emerging players in the Database Solutions market.

AI Training Data Market Outlook

As of 2023, the global AI Training Data market size is valued at approximately USD 1.5 billion, with an anticipated growth to USD 8.9 billion by 2032, driven by a robust CAGR of 21.7%. The increasing adoption of AI across various industries and the continuous advancements in machine learning algorithms are primary growth factors for this market. The demand for high-quality training data is exponentially increasing to improve AI model accuracy and performance.

One of the primary growth drivers for the AI Training Data market is the rapid technological advancements in AI and machine learning. These advancements necessitate large volumes of high-quality training data to develop and fine-tune algorithms. Companies are continuously innovating and investing in AI technologies, which in turn boosts the demand for diverse and accurate training datasets. Furthermore, AI's capability to enhance business processes, improve decision-making, and drive operational efficiency motivates industries to leverage AI, thus fueling the need for robust training data.

Another significant factor propelling the market is the widespread adoption of AI across various sectors such as healthcare, automotive, retail, and BFSI (Banking, Financial Services, and Insurance). In healthcare, AI is revolutionizing diagnostics, patient care, and administrative processes, requiring vast amounts of data for training purposes. Similarly, the automotive industry relies on AI for developing autonomous vehicles, which demand extensive labeled data for functions like object recognition and navigation. The retail industry leverages AI for personalized customer experiences, inventory management, and sales forecasting, all of which require a substantial amount of training data.

The growth of the AI Training Data market is also driven by increasing investments in AI research and development by both private organizations and governments. Governments worldwide are recognizing the potential of AI in driving economic growth and are consequently investing in AI initiatives. Private companies, particularly tech giants, are also heavily investing in AI to maintain a competitive edge. These investments are aimed at acquiring high-quality training data, developing new AI models, and enhancing existing ones, further propelling market growth.

The increasing complexity and diversity of AI applications necessitate the use of advanced Ai Data Labeling Solution. These solutions are pivotal in transforming raw data into structured and meaningful datasets, which are essential for training AI models. By employing sophisticated labeling techniques, AI data labeling solutions ensure that data is accurately annotated, thereby enhancing the model's ability to learn and make predictions. This process not only improves the quality of the training data but also accelerates the development of AI technologies across various sectors. As the demand for high-quality labeled data continues to rise, leveraging efficient data labeling solutions becomes a critical component in the AI development lifecycle.

From a regional perspective, North America dominates the AI Training Data market, owing to the significant presence of leading AI companies and substantial R&D investments. The Asia Pacific region is anticipated to exhibit the fastest growth, driven by the increasing adoption of AI technologies in countries like China, Japan, and India. Europe also holds a considerable share of the market, with strong contributions from countries such as the UK, Germany, and France. The Middle East & Africa and Latin America regions are emerging markets, gradually catching up with advancements in AI and its applications.

Data Type Analysis

The AI Training Data market is segmented by data type into text, image, audio, video, and others. Text data holds a significant share due to its extensive use in natural language processing (NLP) applications. NLP algorithms require large volumes of textual data to understand, interpret, and generate human languages. The proliferation of digital content and social media has resulted in an abundance of text data, making it a critical component of AI training datasets. Moreover, advancements in text generation models, such as GPT-3, further amplify the need for high-quality textual data.

Image data is another crucial segment, primarily driven by the increasing applications of computer vision technologies. Industrie

Social Business Intelligence (BI) Market Outlook

The global Social Business Intelligence (BI) market is set to witness significant growth, with the market size expected to surpass USD 15.2 billion by 2023 and projected to reach approximately USD 35.8 billion by 2032, reflecting a robust CAGR of 9.8% throughout the forecast period. This impressive growth is primarily driven by the increasing adoption of data-driven decision-making processes across various industries, fueled by the rapid advancements in artificial intelligence and machine learning technologies. Moreover, the integration of social media analytics into business intelligence solutions is offering new avenues for organizations to glean actionable insights, thereby boosting the overall demand for social BI solutions.

One of the key growth factors propelling the Social BI market is the surge in social media usage across the globe. Businesses are leveraging data from platforms like Facebook, Twitter, and LinkedIn to gain insights into consumer behavior, preferences, and trends, which are invaluable for strategic planning. The ability of Social BI tools to analyze unstructured data from social media and transform it into structured, actionable insights is empowering businesses to enhance their customer engagement strategies, optimize marketing campaigns, and stay ahead in the competitive landscape. This increased focus on customer-centric approaches and personalized marketing is expected to significantly contribute to market growth.

Another major driver is the growing need for real-time analytics in business operations. In today's fast-paced world, organizations are increasingly reliant on the ability to make quick and informed decisions. Social BI solutions provide real-time data analytics capabilities that enable businesses to monitor and respond to social media trends as they occur. This real-time insight is crucial for mitigating risks, managing brand reputation, and maintaining a competitive edge. Furthermore, advancements in cloud computing have facilitated the deployment of social BI solutions, making them more accessible and scalable, thus further propelling market expansion.

The increasing integration of AI and machine learning technologies into Social BI solutions is also a significant growth factor. These advanced technologies enhance the capabilities of BI tools by enabling more sophisticated data analysis and predictive analytics. This integration allows businesses to anticipate market trends, automate data processing, and generate deeper insights from complex datasets. As a result, companies are increasingly investing in Social BI solutions to harness the power of AI-driven analytics for strategic decision-making, leading to a substantial increase in market demand.

Regionally, North America is expected to dominate the Social BI market due to the early adoption of advanced technologies and the presence of major industry players in the region. The Asia Pacific region, however, is projected to witness the highest growth rate during the forecast period. This can be attributed to the rapid digital transformation, increasing social media penetration, and growing adoption of cloud-based solutions in countries like China, India, and Japan. The region's robust economic growth and expanding IT infrastructure further contribute to the market's potential in Asia Pacific.

Component Analysis

In the Social BI market, the component segment is primarily divided into software and services. The software component is expected to hold the largest market share, driven by the increasing demand for advanced analytical tools that can harness data from social media platforms. Social BI software solutions offer a wide range of functionalities, including data visualization, dashboard creation, and predictive analytics, which are essential for businesses to interpret and act upon social data effectively. The growing emphasis on digital transformation across industries has led to a surge in demand for comprehensive software solutions that facilitate seamless integration with existing business processes.

On the other hand, the services segment, which includes consulting, implementation, training, and support services, is anticipated to grow at a significant rate. As organizations increasingly adopt Social BI tools, the need for expert guidance in selecting the right solutions and optimizing their implementation becomes critical. Service providers play a vital role in ensuring that businesses can fully leverage the benefits of Social BI technologies, leading to increased demand for professional services. Furth

Analysis of ‘2018 PREDICT Dataset (deprecated)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/jrc-predict-2018-core on 11 January 2022.

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

NOTE: The 2018 PREDICT Dataset has been deprecated, and it is now superseded by its latest edition - 2019 PREDICT Dataset:

http://data.europa.eu/89h/6c6f7ce7-893b-48e9-b074-2baaa4b6c7d8

PREDICT includes statistics on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries - the EU28 plus Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia -. The dataset provides indicators in a wide variety of topics, including value added, employment, labour productivity and business R&D expenditure (BERD), distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on Government financing of R&D in ICTs, and total R&D expenditure. Nowcasting of more relevant data in these domains is also performed until a year before the reference date, while time series go back to 1995.

ICTs determine competitive power in the knowledge economy. The ICT sector alone originates almost one fourth of total Business expenditure in R&D (BERD) for the aggregate of the 40 economies under scrutiny in the project. It also has a huge enabling role for innovation in other technological domains. This is reflected at the EU policy level, where the Digital Agenda for Europe in 2010 was identified as one of the seven pillars of the Europe 2020 Strategy for growth in the Union; and the achievement of a Digital Single Market (DSM) is one of the 10 political priorities set by the Commission since 2015.

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

Analysis of ‘2020 PREDICT Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ec1eb9c7-00c8-4d2b-85cb-0bba5c97b646 on 16 January 2022.

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

PREDICT includes statistics on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries - the EU28 plus Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia -. The dataset provides indicators in a wide variety of topics, including value added, employment, labour productivity and business R&D expenditure (BERD), distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on Government financing of R&D in ICTs, and total R&D expenditure. Nowcasting of more relevant data in these domains is also performed until a year before the reference date, while time series go back to 1995.

ICTs determine competitive power in the knowledge economy. The ICT sector alone originates almost one fourth of total Business expenditure in R&D (BERD) for the aggregate of the 40 economies under scrutiny in the project. It also has a huge enabling role for innovation in other technological domains. This is reflected at the EU policy level, where the Digital Agenda for Europe in 2010 was identified as one of the seven pillars of the Europe 2020 Strategy for growth in the Union; and the achievement of a Digital Single Market (DSM) is one of the 10 political priorities set by the Commission since 2015.

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

Data on laws governing shareholder and creditor protection in 30 countries for the period 1990-2013. 'Leximetric' data coding methods were used to construct the datasets. These involve the development of a coding algorithm or protocol to score legal rules according to how far they protect shareholder and creditor interests in the governance of business firms. The units of analysis are countries and the laws are coded annually. The primary data used to construct the datasets are laws in the forms of texts which were retrieved from law libraries or from internet sources. The data are presented in spreadsheets which indicate the scores for individual variables by country/year, accompanied by explanatory documents which set out the coding algorithms and provide legal sources for the scores recorded in the spreadsheets.

Additional qualitative data result from interviews conducted in the case-study countries. In the case of China and Russia, these are in the form of anonymised interview transcripts based on verbatim notes taken by the interviewers during the interview and typed up afterwards (as interviewees did not want to be recorded). In the case of Brazil and India, they are in the form of audio files.

The aim of the project was to analyse to what extent the quality of legal and other formal institutions affected financial development and economic growth in the BRIC countries, and whether reliance on informal institutions posed an obstacle to their future growth.

For over a decade, with the encouragement of the World Bank and western governments, developing countries have adopted programmes of legal and financial reform combining privatisation of state-owned banks and enterprises with the enactment of enhanced legal protections for shareholders and creditors. According to some accounts, China’s recent experience demonstrates the value of a developing legal framework in overcoming limits to growth in an informal, trust-based economy, while Russia is actively seeking to put in place the necessary legal and regulatory structures for market-based financial development. In Brazil, the example of the Novo Mercado, a new stock market segment which has attracted a large number of high-tech IPOs, suggests that a strategy of allowing firms to opt into a shareholder-rights based regulatory regime can work in promoting flows of equity finance in an emerging market context. In India, too, there is some evidence that recent corporate governance reforms have led to greater transparency on the part of listed firms and to increased investor confidence, although critics of the reform process argue that it has not gone far enough. The picture emerging from these experiences is one in which formal and informal institutions do not necessarily operate in tension. Rather, they may complement each other in providing the foundations for sustainable economic growth and societal development.

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Artificial Intelligence Text Generator Market Size 2024-2028

The artificial intelligence (AI) text generator market size is forecast to increase by USD 908.2 million at a CAGR of 21.22% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. One of these trends is the increasing popularity of AI generators in various sectors, including education for e-learning applications. Another trend is the growing importance of speech-to-text technology, which is becoming increasingly essential for improving productivity and accessibility. However, data privacy and security concerns remain a challenge for the market, as generators process and store vast amounts of sensitive information. It is crucial for market participants to address these concerns through strong data security measures and transparent data handling practices to ensure customer trust and compliance with regulations. Overall, the AI generator market is poised for continued growth as it offers significant benefits in terms of efficiency, accuracy, and accessibility.

What will be the Size of the Artificial Intelligence (AI) Text Generator Market During the Forecast Period?

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The market is experiencing significant growth as businesses and organizations seek to automate content creation across various industries. Driven by technological advancements in machine learning (ML) and natural language processing, AI generators are increasingly being adopted for downstream applications in sectors such as education, manufacturing, and e-commerce. 
Moreover, these systems enable the creation of personalized content for global audiences in multiple languages, providing a competitive edge for businesses in an interconnected Internet economy. However, responsible AI practices are crucial to mitigate risks associated with biased content, misinformation, misuse, and potential misrepresentation.

How is this Artificial Intelligence (AI) Text Generator Industry segmented and which is the largest segment?

The artificial intelligence (AI) text generator industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solution
  Service


Application

  Text to text
  Speech to text
  Image/video to text


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) text generators have gained significant traction in various industries due to their efficiency and cost-effectiveness in content creation. These solutions utilize machine learning algorithms, such as Deep Neural Networks, to analyze and learn from vast datasets of human-written text. By predicting the most probable word or sequence of words based on patterns and relationships identified In the training data, AIgenerators produce personalized content for multiple languages and global audiences. The application spans across industries, including education, manufacturing, e-commerce, and entertainment & media. In the education industry, AI generators assist in creating personalized learning materials.

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The solution segment was valued at USD 184.50 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market holds the largest share in the market, driven by the region's technological advancements and increasing adoption of AI in various industries. AI text generators are increasingly utilized for content creation, customer service, virtual assistants, and chatbots, catering to the growing demand for high-quality, personalized content in sectors such as e-commerce and digital marketing. Moreover, the presence of tech giants like Google, Microsoft, and Amazon in North America, who are investing significantly in AI and machine learning, further fuels market growth. AI generators employ Machine Learning algorithms, Deep Neural Networks, and Natural Language Processing to generate content in multiple languages for global audiences.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and c