The global synthetic data market size is projected to grow from USD 0.4 billion in the current year to USD 19.22 billion by 2035, representing a CAGR of 42.14%, during the forecast period till 2035

The synthetic data generation market is projected to be worth US$ 300 million in 2024. The market is anticipated to reach US$ 13.0 billion by 2034. The market is further expected to surge at a CAGR of 45.9% during the forecast period 2024 to 2034.

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Report Scope

Market Analysis for Synthetic Data Solution The global synthetic data solution market is projected to reach USD XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The increasing demand for synthetic data in various industries, such as financial services, retail, and healthcare, drives this growth. Synthetic data offers a privacy-preserving alternative to real-world data, enabling organizations to train and evaluate models without compromising sensitive information. The growing adoption of cloud-based solutions and the increasing need for data privacy and security further contribute to market growth. Market segments include deployment types (cloud-based and on-premises) and applications (financial services industry, retail industry, medical industry, and others). Key regional markets include North America, South America, Europe, Middle East & Africa, and Asia Pacific. Major companies operating in the market include LightWheel AI, Hanyi Innovation Technology, Haohan Data Technology, Haitian Ruisheng Science Technology, and Baidu. Trends such as the adoption of artificial intelligence (AI) and machine learning (ML) and the rising concern over data privacy and governance are expected to shape the market's future.

According to a survey of artificial intelligence (AI) companies in South Korea carried out in 2023, nearly 66 percent of the data used when developing AI products and services was private data. On the other hand, public data comprised around 34 percent.

Data on businesses collected by statistical agencies are challenging to protect.Many businesses have unique characteristics, and distributions of employment,sales, and profits are highly skewed. Attackers wishing to conduct identificationattacks often have access to much more information than for any individual. Asa consequence, most disclosure avoidance mechanisms fail to strike an accept-able balance between usefulness and confidentiality protection. Detailed aggregatestatistics by geography or detailed industry classes are rare, public-use microdataon businesses are virtually inexistant, and access to confidential microdata can beburdensome. Synthetic microdata have been proposed as a secure mechanism topublish microdata, as part of a broader discussion of how to provide broader accessto such datasets to researchers. In this article, we document an experiment to cre-ate analytically valid synthetic data, using the exact same model and methods previ-ously employed for the United States, for data from two different countries: Canada(Longitudinal Employment Analysis Program (LEAP)) and Germany (EstablishmentHistory Panel (BHP)). We assess utility and protection, and provide an assessmentof the feasibility of extending such an approach in a cost-effective way to other data.

As of 2023, customer data was the leading source of information used to train artificial intelligence (AI) models in South Korea, with nearly 70 percent of surveyed companies answering that way. About 62 percent responded to use existing data within the company when training their AI model.

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

Context

Getting proper data for survival analysis is often difficult.

Content

This data represents entry dates, departure dates and other information about fictional clients of a life insurance company. You have the age at which the insured entered the contract, the age at which he left, and the reason : either death or withdrawal, equivalent for us to right-censorship since the actual age at death of the person will no longer be observed. The data are left-truncated at the 1st of January 1820 : you only know if a client was present before that date, but you have no idea for how long he's been there.

Acknowledgements

Entirely generated using the numpy.random module, source code attached. For the survival analysis notebooks to come, my theoretical basis is the excellent course of Duration Models by Olivier Lopez at ENSAE Paris.

Inspiration

Develop some survival analysis and duration models tools to estimate death or departure of your clients as accurately as possible !

Dataset

This dataset was created by pjmathematician

Released under MIT

Contents

As the frenzy around generative artificial intelligence intensifies, The Information has built a database of more than 100 companies making software and services that use generative AI. Investors are jockeying to join the action: Together, the startups on our list have raised more than $20 billion. Our data comes from our reporting, founders, investors and PitchBook, which provides private market data. We will regularly update the database with more companies and more information about how they are growing.

The Artificial Intelligence and Analytics in Defense Market Report is Segmented by Offering (Hardware, Software, and Services), Technology (Artificial Intelligence, Big Data Analytics, and Other Technologies), Platform (Army, Navy, and Airforce), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Report Offers Market Size and Forecast for all the Above Segments in Value (USD).

Comparison of Artificial Analysis Intelligence Index vs. Output Speed (Output Tokens per Second) by Model

The Saudi Arabia Big Data and Artificial Intelligence Market Report is Segmented by Solutions (Hardware, Software, Service), Organization Size (SMEs, Large Enterprises), and End User (IT and Telecom, Retail, Public and Government Institutions, BFSI, Healthcare, Energy, Construction and Manufacturing, and Other End Users). The Market Size and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Dataset

This dataset was created by KORCY

Released under Apache 2.0

Contents

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal EUR includes consumer transaction data on 6.7M+ credit cards, debit cards, direct debit accounts, and direct transfer accounts, including 5.3M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 5K+ merchants, 3K+ brands mapped to 600 global parent companies (500 publicly traded), and deep geographic breakouts with demographic breakouts coming soon for UK. Brick & mortar , and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 5 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Public and private investors can leverage insights from CE’s synthetic data to assess consensus estimates and investment opportunities, while consumer marketing and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and consumer insights.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details

As of 2023, over 90 percent of the respondents claim their companies must invest more into reassuring customers their data is being used for intended and legitimate purposes only throughout the use of artificial intelligence (AI).

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used by private investors for deal sourcing, providing daily spend for 12,000 brands by channel.

Inquire about a CE subscription to perform more complex, near real-time deal sourcing, diligence, and portfolio monitoring analysis functions on public tickers and private brands like: • Screen fast-growing brands in any consumer industry or subindustry • Search for lagging companies open to capital discussions

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Deal Sourcing & Diligence

Problem A $35B Private Equity company focused on growth & venture, credit, and public equity investing in later-stage companies was looking for a data solution to enable them to source and vet the health of potential investments vs. their peers and their industry. With limited visibility, they were seeking a data solution that would seamlessly and easily provide concrete data and analytics for their assessments.

Solution The firm leveraged CE data to monitor and report weekly on: • Sourcing: With the support of Consumer Edge’s Insight team, the firm set up dashboard views to find and track the struggling firms that are open to capital needs. • Diligence: The firm vetted the health of a potential investment target vs. their peers and their industry by monitoring key metrics such as YoY growth, spend amount % growth, transactions, and of transactions % growth.

Impact The diligence team able to: • Identify three target acquisition companies based on historic performance • Set benchmarks vs. competition and monitor growth trends • Develop growth plans for post-acquisition strategy

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors from quant and systematic funds to quantamental and fundamental funds include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth •...

Global Artificial Intelligence (AI) in Cybersecurity Market size was valued at USD 18.36 Billion in 2022 and is poised to grow from USD 22.49 Billion in 2023 to USD 114.30 Billion by 2031, at a CAGR of 22.53% during the forecast period (2024-2031).

Comparison of Artificial Analysis Intelligence Index vs. Price (USD per M Tokens) by Model

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Cloud Artificial Intelligence (AI) Market Size 2024-2028

The cloud artificial intelligence (ai) market size is forecast to increase by USD 12.61 billion at a CAGR of 24.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the emergence of technologically advanced devices and the increasing adoption of 5G and mobile penetration. These factors enable the integration of AI technologies into various applications, leading to improved efficiency and productivity. However, the market also faces challenges from open-source platforms, which offer free AI solutions, making it difficult for market players to compete on price. Despite this, the market is expected to continue its growth trajectory, driven by the increasing demand for AI solutions in various industries, including healthcare, finance, and retail. Organizations are leveraging cloud-based AI solutions to gain insights from their data, automate processes, and enhance customer experiences.The market analysis report provides a comprehensive overview of these trends and challenges, offering valuable insights for stakeholders looking to capitalize on the growth opportunities In the cloud AI market.

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

Request Free SampleThe market is experiencing robust growth, driven by the increasing adoption of machine learning (ML), deep learning, neural networks, and generative AI technologies. These advanced algorithms are revolutionizing various industries by emulating human intelligence in speech recognition, digital media, diagnostics, cybersecurity, and business decision-making. Hyperscale cloud platforms are becoming the preferred infrastructure for AI applications due to their ability to handle massive data processing requirements. Cloud AI solutions are transforming IT services by automating routine tasks, enhancing data analytics, and improving human capital management. They offer significant cost savings by eliminating the need for expensive hardware and maintenance. Moreover, AI-driven cloud management and data management solutions enable predictive analytics, personalization, productivity, and security enhancements.In addition, AI is playing a pivotal role in threat detection and cybersecurity, ensuring business continuity and data protection. Overall, the cloud AI market is poised for exponential growth, as organizations continue to leverage AI to gain a competitive edge In their respective industries.

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

The cloud artificial intelligence (ai) 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. ComponentSoftwareServicesGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanSouth AmericaMiddle East and Africa

By Component Insights

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

Artificial Intelligence (AI) software replicates human learning and behavior, revolutionizing various business sectors. AI development involves creating new software or enhancing existing solutions to deliver analytics results and trigger actions based on them. Applications of AI include automating business processes, personalizing services, and generating industry-specific insights. The digitization trend has driven industrial transformations, with healthcare being a prime example. According to BDO's Healthcare Digital Transformation Survey, 93% of US healthcare organizations adopted digital transformation strategies in 2021, integrating AI, computing, and enterprise resource planning software. AI functionality encompasses speech recognition, machine learning (ML), deep learning, neural networks, generative AI, automation, decision-making, and more.Hyperscale cloud platforms, IT services, infrastructure, data analytics, human capital management, cost savings, cloud management, data management, predictive analytics, personalization, productivity, security, threat detection, integration, talent gap, and chatbots are significant AI applications. AI tools process data, power business intelligence, and enable lower costs through ML-based models and GPUs. Enterprise datacenters, virtualization, public clouds, private clouds, and hybrid cloud solutions leverage AI for non-repetitive tasks. AI streamlines workloads, automates repetitive tasks, monitors and manages IT infrastructure, and offers dynamic cloud services. AI is transforming industries, from retail inventory management to financial organizations, providing competitive advantages through cost savings and improved decision-making capabilities.

Get a glance at the Cloud Artificial Intelligence (AI) Industry repo