IntroductionEnsuring high quality and reusability of personal health data is costly and time-consuming. An AI-powered virtual assistant for health data curation and publishing could support patients to ensure harmonization and data quality enhancement, which improves interoperability and reusability. This formative evaluation study aimed to assess the usability of the first-generation (G1) prototype developed during the AI-powered data curation and publishing virtual assistant (AIDAVA) Horizon Europe project.MethodsIn this formative evaluation study, we planned to recruit 45 patients with breast cancer and 45 patients with cardiovascular disease from three European countries. An intuitive front-end, supported by AI and non-AI data curation tools, is being developed across two generations. G1 was based on existing curation tools and early prototypes of tools being developed. Patients were tasked with ingesting and curating their personal health data, creating a personal health knowledge graph that represented their integrated, high-quality medical records. Usability of G1 was assessed using the system usability scale. The subjective importance of the explainability/causability of G1, the perceived fulfillment of these needs by G1, and interest in AIDAVA-like technology were explored using study-specific questionnaires.ResultsA total of 83 patients were recruited; 70 patients completed the study, of whom 19 were unable to successfully curate their health data due to configuration issues when deploying the curation tools. Patients rated G1 as marginally acceptable on the system usability scale (59.1 ± 19.7/100) and moderately positive for explainability/causability (3.3–3.8/5), and were moderately positive to positive regarding their interest in AIDAVA-like technology (3.4–4.4/5).DiscussionDespite its marginal acceptability, G1 shows potential in automating data curation into a personal health knowledge graph, but it has not reached full maturity yet. G1 deployed very early prototypes of tools planned for the second-generation (G2) prototype, which may have contributed to the lower usability and explainability/causability scores. Conversely, patient interest in AIDAVA-like technology seems quite high at this stage of development, likely due to the promising potential of data curation and data publication technology. Improvements in the library of data curation and publishing tools are planned for G2 and are necessary to fully realize the value of the AIDAVA solution.

Dataplace Curation AI Market Outlook

As per our latest research, the global Dataplace Curation AI market size reached USD 2.94 billion in 2024, reflecting significant momentum driven by the rapid adoption of AI-powered data management solutions across industries. The market is poised for robust expansion, projected to grow at a CAGR of 23.7% from 2025 to 2033, with the total market value anticipated to reach USD 24.24 billion by 2033. This remarkable growth is primarily fueled by the increasing need for automated, intelligent data curation systems to handle the ever-expanding volume and complexity of enterprise data, as organizations strive for operational excellence and competitive differentiation.

The primary growth factor for the Dataplace Curation AI market is the exponential increase in data volume generated by businesses, particularly as digital transformation initiatives accelerate across sectors. Enterprises now recognize that traditional, manual data curation processes are no longer viable in the face of big data challenges, leading to a surge in demand for AI-powered platforms that can automate and optimize data organization, enrichment, and governance. Furthermore, the proliferation of cloud computing and the integration of AI technologies into data management workflows are empowering organizations to unlock actionable insights from disparate data sources, thereby driving efficiency, reducing operational costs, and enhancing decision-making capabilities. This paradigm shift is especially pronounced in industries such as BFSI, healthcare, and retail, where real-time data curation directly impacts customer experience and business outcomes.

Another significant driver is the growing emphasis on regulatory compliance and data quality. With stringent data privacy laws such as GDPR and CCPA, organizations are under increasing pressure to ensure the accuracy, consistency, and security of their data assets. Dataplace Curation AI solutions provide advanced capabilities for metadata management, data lineage tracking, and automated policy enforcement, which are critical for maintaining compliance and mitigating risks associated with data breaches or inaccuracies. Moreover, the integration of machine learning and natural language processing enables these platforms to continuously learn and adapt to evolving data landscapes, offering scalable solutions that cater to both structured and unstructured data environments.

The market is also witnessing strong momentum from the rising adoption of AI-driven content curation and knowledge management tools, particularly in sectors such as media and entertainment, education, and IT. Organizations are leveraging Dataplace Curation AI to streamline content discovery, personalize user experiences, and foster knowledge sharing across distributed teams. The ability of these systems to aggregate, categorize, and recommend relevant content based on user behavior and preferences is enhancing productivity and innovation. Additionally, the integration of AI-powered analytics is enabling deeper insights into content performance and user engagement, further amplifying the value proposition of Dataplace Curation AI solutions.

Regionally, North America continues to dominate the Dataplace Curation AI market, driven by early technology adoption, a robust ecosystem of AI solution providers, and significant investments in digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitization, expanding cloud adoption, and increasing government initiatives to promote AI innovation. Europe is also making notable strides, particularly in sectors such as BFSI and healthcare, where data governance and compliance requirements are stringent. The Middle East & Africa and Latin America are gradually catching up, with organizations in these regions recognizing the strategic value of AI-powered data curation for business transformation.

Component Analysis

The Dataplace Curation AI market is segmented by component into software and services, each playing a pivotal role in the overall ecosystem. The software segment, which includes AI-powered platforms and tools for data curation, dominates the market owing to continuous advancements in machine learning algorithms, natural language processing, and automation capabilities. These software solutions are designed to seamlessly integrate with existing data infrastructure, providing organizations with scalable, flexible, and

Dataset Card for 2024.10.06.22.04.02

This is a FiftyOne dataset with 8629 samples.

  Installation

If you haven't already, install FiftyOne: pip install -U fiftyone

  Usage

import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub

Load the dataset

Note: other available arguments include 'max_samples', etc

dataset = load_from_hub("dgural/Data-Curation-for-Visual-AI-Module-4-VisDrone")

Launch the App

session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/dgural/Data-Curation-for-Visual-AI-Module-4-VisDrone.

Data Prep Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 16.12 Billion by 2031, growing at a CAGR of 19% from 2024 to 2031.

Global Data Prep Market Drivers

Increasing Demand for Data Analytics: Businesses across all industries are increasingly relying on data-driven decision-making, necessitating the need for clean, reliable, and useful information. This rising reliance on data increases the demand for better data preparation technologies, which are required to transform raw data into meaningful insights. Growing Volume and Complexity of Data: The increase in data generation continues unabated, with information streaming in from a variety of sources. This data frequently lacks consistency or organization, therefore effective data preparation is critical for accurate analysis. To assure quality and coherence while dealing with such a large and complicated data landscape, powerful technologies are required. Increased Use of Self-Service Data Preparation Tools: User-friendly, self-service data preparation solutions are gaining popularity because they enable non-technical users to access, clean, and prepare data. independently. This democratizes data access, decreases reliance on IT departments, and speeds up the data analysis process, making data-driven insights more available to all business units. Integration of AI and ML: Advanced data preparation technologies are progressively using AI and machine learning capabilities to improve their effectiveness. These technologies automate repetitive activities, detect data quality issues, and recommend data transformations, increasing productivity and accuracy. The use of AI and ML streamlines the data preparation process, making it faster and more reliable. Regulatory Compliance Requirements: Many businesses are subject to tight regulations governing data security and privacy. Data preparation technologies play an important role in ensuring that data meets these compliance requirements. By giving functions that help manage and protect sensitive information these technologies help firms negotiate complex regulatory climates. Cloud-based Data Management: The transition to cloud-based data storage and analytics platforms needs data preparation solutions that can work smoothly with cloud-based data sources. These solutions must be able to integrate with a variety of cloud settings to assist effective data administration and preparation while also supporting modern data infrastructure.

Golden Dataset Curation for LLMs Market Outlook

According to our latest research, the Global Golden Dataset Curation for LLMs market size was valued at $1.2 billion in 2024 and is projected to reach $8.7 billion by 2033, expanding at a CAGR of 24.8% during 2024–2033. This remarkable growth trajectory is primarily driven by the increasing demand for high-quality, bias-mitigated, and diverse datasets essential for training and evaluating large language models (LLMs) across industries. As generative AI applications proliferate, organizations are recognizing the strategic importance of curating "golden datasets"—carefully selected, annotated, and validated data collections that ensure robust model performance, regulatory compliance, and ethical AI outcomes. The accelerating adoption of AI-powered solutions in sectors such as healthcare, finance, and government, coupled with ongoing advances in data curation technologies, are further fueling the expansion of the Golden Dataset Curation for LLMs market globally.

Regional Outlook

North America currently commands the largest share of the Golden Dataset Curation for LLMs market, accounting for approximately 38% of the global revenue in 2024. This dominance is underpinned by the region’s mature artificial intelligence ecosystem, the presence of leading technology companies, and robust investments in R&D. The United States, in particular, boasts a high concentration of AI expertise, advanced data infrastructure, and a strong regulatory framework that supports ethical data curation. Furthermore, North America’s proactive adoption of generative AI across industries such as healthcare, BFSI, and government has spurred demand for meticulously curated datasets to drive innovation and ensure compliance with evolving data privacy standards. The region’s leadership in launching open-source initiatives and public-private partnerships for AI research further cements its preeminent position in the global market.

Asia Pacific is emerging as the fastest-growing region, projected to register a robust CAGR of 28.4% from 2024 to 2033. The region’s rapid market expansion is propelled by exponential growth in digital transformation initiatives, increasing AI investments, and supportive government policies aimed at fostering indigenous AI capabilities. Countries such as China, India, and South Korea are making significant strides in AI research, with a particular emphasis on local language and multimodal dataset curation to cater to diverse populations. The proliferation of startups and technology incubators, coupled with strategic collaborations between academia and industry, is accelerating the development and adoption of golden datasets. Additionally, the region’s burgeoning internet user base and mobile-first economies are generating vast volumes of data, providing fertile ground for dataset curation innovation.

Emerging economies in Latin America, the Middle East, and Africa are witnessing gradual but promising adoption of Golden Dataset Curation for LLMs. While market penetration remains lower compared to developed regions, localized demand for AI-driven solutions in sectors such as public health, education, and government services is spurring investment in dataset curation capabilities. However, challenges such as limited access to high-quality data, fragmented regulatory environments, and a shortage of specialized talent are impeding rapid growth. Despite these hurdles, targeted policy reforms, international collaborations, and capacity-building initiatives are laying the groundwork for future market expansion, particularly as governments recognize the strategic value of AI and data sovereignty.

Report Scope

Real-World Evidence Curation AI Market Outlook

According to our latest research, the global Real-World Evidence (RWE) Curation AI market size reached USD 1.42 billion in 2024, demonstrating robust momentum across healthcare and life sciences sectors. The market is projected to grow at a CAGR of 23.9% from 2025 to 2033, reaching an estimated USD 11.44 billion by 2033. This remarkable expansion is primarily driven by the increasing demand for advanced analytics in drug development, regulatory compliance, and personalized medicine. The integration of artificial intelligence for curating real-world evidence is transforming the way stakeholders derive actionable insights from complex, unstructured healthcare data, thus fueling market growth.

One of the primary growth factors propelling the Real-World Evidence Curation AI market is the exponential increase in healthcare data generation. With the proliferation of electronic health records (EHRs), wearable devices, insurance claims, and patient registries, the volume and variety of real-world data have surged. AI-driven curation solutions are uniquely positioned to extract, normalize, and analyze this data at scale, enabling pharmaceutical companies, healthcare providers, and payers to make informed decisions. The growing regulatory emphasis on real-world data for clinical trials and drug approvals by agencies such as the FDA and EMA further underscores the importance of leveraging AI for efficient and accurate evidence curation.

Another significant driver is the shift towards value-based healthcare and personalized medicine. As healthcare systems worldwide transition from fee-for-service to outcome-based models, there is a critical need for real-world evidence to support reimbursement decisions, monitor long-term drug safety, and assess treatment effectiveness in diverse populations. AI-powered curation platforms facilitate the rapid synthesis of heterogeneous datasets, helping stakeholders identify patient cohorts, monitor adverse events, and optimize clinical trial designs. This capability not only accelerates time-to-market for new therapies but also enhances patient outcomes by tailoring interventions based on real-world insights.

Collaboration between technology vendors, pharmaceutical companies, and research organizations is also accelerating market growth. Strategic partnerships are fostering innovation in AI algorithms, natural language processing, and data interoperability standards, making it easier to integrate RWE curation tools into existing healthcare workflows. Furthermore, the increasing adoption of cloud-based deployment models is democratizing access to advanced analytics, enabling small and medium enterprises to leverage AI for real-world evidence generation. These collaborative efforts are expected to further expand the market’s reach and impact over the coming years.

From a regional perspective, North America currently dominates the Real-World Evidence Curation AI market, driven by strong investments in healthcare IT, favorable regulatory frameworks, and the presence of leading pharmaceutical and biotech firms. Europe follows closely, with significant initiatives aimed at standardizing health data and promoting cross-border research collaborations. The Asia Pacific region is witnessing the fastest growth, fueled by expanding healthcare infrastructure, increasing adoption of digital health technologies, and supportive government policies. As emerging markets continue to invest in AI and data analytics, the global landscape for real-world evidence curation is poised for substantial transformation.

Component Analysis

The Component segment of the Real-World Evidence Curation AI market is bifurcated into software and services, each playing a pivotal role in shaping the industry’s trajectory. AI-powered software solutions are at the core of evidence curation, leveraging advanced machine learning, natural language processing, and data harmonization technologies to transform unstructured data into actionable insights. These platforms are designed to integrate seamlessly with diverse data sources, including EHRs, claims databases, and patient registries, automating the extraction, normalization, and analysis processes. The rapid advancements in AI algorithms and user-friendly interfaces have made these software solutions indispensable for pharmaceutical companies, healthcare providers, and payers seeking to gain a competitive edge through data-driven decision-making.<br /&

AI-Driven Photo Organizer Device Market Outlook

According to our latest research, the global AI-driven photo organizer device market size reached USD 1.38 billion in 2024, reflecting a robust surge in demand for advanced photo management solutions. The market is projected to grow at a compelling CAGR of 15.2% from 2025 to 2033, reaching a forecasted value of USD 4.89 billion by the end of the period. This impressive growth trajectory is primarily fueled by the increasing proliferation of digital photography, the explosion of image data across personal and professional domains, and the growing need for efficient, AI-powered organizational tools that can seamlessly sort, tag, and retrieve visual content.

One of the primary growth drivers of the AI-driven photo organizer device market is the exponential increase in digital image creation due to the widespread adoption of smartphones, digital cameras, and other imaging devices. With billions of photos being captured and stored annually, both individuals and organizations face significant challenges in managing, categorizing, and accessing their visual data. AI-powered devices offer automated photo sorting, facial recognition, duplicate detection, and smart tagging, significantly reducing manual effort and enhancing user experience. This automation not only streamlines workflows for professional photographers and enterprises but also simplifies personal photo management, making these devices highly attractive to a broad user base.

Another significant factor propelling market growth is the integration of advanced machine learning and deep learning algorithms into photo organizer devices. These technologies enable devices to learn user preferences, recognize complex patterns, and provide personalized recommendations for photo organization and curation. The growing sophistication of AI models enhances device accuracy in identifying people, places, and objects, supporting more intuitive search and retrieval functionalities. As AI algorithms continue to evolve, the capabilities of these devices are expected to expand further, driving higher adoption rates across both consumer and commercial segments.

The rising demand for data privacy and security is also shaping the evolution of the AI-driven photo organizer device market. As users become more aware of the risks associated with cloud-based storage, there is a marked shift towards devices that offer robust on-device AI processing and secure data management. This trend is particularly pronounced among professionals and enterprises handling sensitive visual content, such as legal firms, healthcare providers, and creative agencies. The ability to organize and store photos locally, without compromising privacy, is a key value proposition that is accelerating market adoption and driving innovation among manufacturers.

From a regional perspective, North America currently dominates the AI-driven photo organizer device market due to high consumer awareness, early technology adoption, and the presence of leading market players. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid urbanization, increasing digitalization, and the expanding middle-class population with rising disposable incomes. Europe also demonstrates strong growth potential, particularly in countries with vibrant creative industries and strict data privacy regulations. The interplay of these regional dynamics is expected to shape market trends and competitive strategies over the forecast period.

Product Type Analysis

The AI-driven photo organizer device market is segmented by product type into standalone devices and integrated devices, each catering to distinct user needs and preferences. Standalone devices are purpose-built hardware solutions designed exclusively for photo organization, offering dedicated processing power, storage, and advanced AI functionalities. These devices are particularly popular among professional photographers, creative agencies, and enterprises requiring robust, hig

The purpose of the online survey on data curation was to arrive at a better understanding of the process of creating, organizing, and maintaining data(sets) by organizations in the field of grey literature. The survey population was based on the number of respondents to the earlier questionnaire on Data Retention Status, which was the first phase in the study on global information repository research for STI development. The ten-question online survey was constructed and implemented via SurveyMonkey. Nine of the questions required closed-ended checkbox responses, while the tenth was open-ended. The closed-ended part of the questionnaire dealt with such issues as the strengths and tasks of the organization related to data curation, improving the user experience, collaboration on data sharing, and the introduction of AI technology in the work environment. The results of the survey remain compiled and preserved in SurveyMonkey as well as in DANS, Data Station for the Social Sciences and Humanities.

Golden Dataset Curation for LLMs Market Outlook

According to our latest research, the global Golden Dataset Curation for LLMs market size stood at USD 1.42 billion in 2024, reflecting the surging demand for high-quality, bias-mitigated datasets in large language model (LLM) development. The market is projected to grow at a robust CAGR of 27.8% from 2025 to 2033, reaching an estimated USD 13.9 billion by 2033. This remarkable growth is fueled by the increasing sophistication of AI models, the critical need for reliable training data, and the expanding adoption of LLMs across diverse sectors.

Several key factors are driving the rapid expansion of the Golden Dataset Curation for LLMs market. First and foremost is the exponential growth in the deployment of large language models across industries such as healthcare, finance, legal, and customer service. As organizations seek to leverage LLMs for complex natural language processing tasks, the demand for meticulously curated, high-quality datasets has become paramount. This is because the performance, reliability, and ethical alignment of LLMs are intrinsically linked to the quality of their training data. Companies are increasingly investing in the curation of "golden datasets"—datasets that are not only comprehensive and representative but also rigorously annotated and validated to minimize bias and ensure regulatory compliance. This trend is expected to intensify as AI regulations tighten and as organizations strive for greater transparency and accountability in AI deployments.

Another significant growth driver for the Golden Dataset Curation for LLMs market is the advancement in data curation technologies and methodologies. The integration of automation, machine learning, and human-in-the-loop systems has revolutionized the way datasets are curated and validated. These advancements enable the efficient handling of vast and complex data sources, including text, image, audio, and multimodal datasets. The rise of specialized data curation platforms and services has further accelerated the adoption of golden dataset practices, allowing organizations to scale their AI initiatives while maintaining data integrity. Moreover, as LLMs become more multilingual and domain-specific, the need for curated datasets that reflect diverse languages, cultures, and industry-specific knowledge is growing rapidly, further boosting market demand.

The expanding ecosystem of AI applications is also propelling the Golden Dataset Curation for LLMs market forward. As LLMs are increasingly utilized for tasks such as model training, evaluation, benchmarking, and fine-tuning, the scope and complexity of required datasets have grown exponentially. Organizations are now seeking datasets that not only support model development but also facilitate continuous evaluation and improvement of AI models in real-world scenarios. This has led to a surge in demand for datasets that are regularly updated, contextually rich, and tailored to specific use cases. Additionally, the proliferation of open-source and third-party data sources, coupled with the need for proprietary datasets, has created a dynamic and competitive market landscape where data quality and curation expertise are key differentiators.

From a regional perspective, North America currently dominates the Golden Dataset Curation for LLMs market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology companies, a robust research ecosystem, and significant investments in AI and machine learning infrastructure. Europe and Asia Pacific are also emerging as key markets, driven by increasing regulatory focus on AI ethics and the rapid digital transformation of enterprises. The Asia Pacific region, in particular, is expected to witness the highest CAGR during the forecast period, fueled by rising AI adoption in countries such as China, Japan, and India. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness of AI's potential and investments in digital infrastructure.

Dataset Type

Snapshot img

AI Data Management Market Size 2025-2029

The AI data management market size is valued to increase by USD 51.04 billion, at a CAGR of 19.7% from 2024 to 2029. Proliferation of generative AI and large language models will drive the AI data management market.

Market Insights

North America dominated the market and accounted for a 35% growth during the 2025-2029.
By Component - Platform segment was valued at USD 8.66 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 306.58 million 
Market Future Opportunities 2024: USD 51042.00 million
CAGR from 2024 to 2029 : 19.7%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on generative AI and large language models to gain insights from their data. This trend is driven by the ascendancy of data-centric AI and the industrialization of data curation. With the proliferation of data sources and the extreme complexity of managing and ensuring data quality at scale, businesses are turning to advanced AI solutions to streamline their data management processes. One real-world scenario where AI data management is making a significant impact is in supply chain optimization. In the manufacturing sector, for instance, AI algorithms are being used to analyze vast amounts of data from various sources, including production records, sales data, and external market trends.
By identifying patterns and correlations, these systems can help optimize inventory levels, improve order fulfillment, and reduce lead times. Despite the benefits, managing AI data comes with its own set of challenges. Ensuring data accuracy, security, and privacy are critical concerns, especially as more data is generated and shared across organizations. Additionally, managing data at scale requires significant computational resources and expertise. As a result, businesses are investing in advanced data management solutions that can handle the complexities of AI data and provide robust data quality assurance. In conclusion, the market is poised for continued growth as businesses seek to harness the power of AI to gain insights from their data.
From supply chain optimization to compliance and operational efficiency, the applications of AI data management are vast and varied. Despite the challenges, the benefits far outweigh the costs, making it an essential investment for businesses looking to stay competitive in today's data-driven economy.

What will be the size of the AI Data Management Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, driven by the increasing adoption of advanced technologies such as machine learning, predictive modeling, and data analytics. According to recent studies, businesses are investing heavily in AI data management solutions to enhance their operations and gain a competitive edge. For instance, data governance policies have become essential for organizations to ensure data security, privacy, and compliance. Moreover, AI data management is crucial for product strategy, enabling companies to make informed decisions based on accurate and timely data.
For example, predictive modeling techniques can help businesses forecast sales trends and optimize inventory levels, while data validation rules ensure data accuracy and consistency. Furthermore, data cataloging systems facilitate efficient data discovery and access, reducing processing time and improving overall productivity. Advancements in AI data management also include model selection criteria, such as accuracy, interpretability, and fairness, which are essential for responsible AI practices. Encryption algorithms and access control policies ensure data security, while data standardization methods promote interoperability and data consistency. Additionally, edge computing infrastructure and hybrid cloud solutions enable faster data processing and analysis, making AI data management a strategic priority for businesses.

Unpacking the AI Data Management Market Landscape

In today's data-driven business landscape, effective AI data management is a critical success factor. According to recent studies, AI data management processes can reduce data integration complexities by up to 70%, enabling faster time-to-insight and improved ROI. Anomaly detection algorithms, powered by machine learning models, can identify data anomalies with 95% accuracy, ensuring regulatory compliance and reducing potential losses. Synthetic data generation can enhance model training pipelines by up to 50%, improving model accuracy and reducing reliance on labeled data. Cloud-based data platforms offer secure data access control, while model accuracy assessment techniques ensure consistent performance across model retraining schedules. Data lineage

Real-World Evidence Curation AI Market Outlook

According to our latest research, the Global Real-World Evidence Curation AI market size was valued at $1.3 billion in 2024 and is projected to reach $7.9 billion by 2033, expanding at a robust CAGR of 21.8% during the forecast period of 2025–2033. The primary driver of this remarkable growth is the increasing demand for advanced data analytics and artificial intelligence solutions to efficiently curate, analyze, and derive actionable insights from vast and diverse healthcare datasets. As healthcare organizations, pharmaceutical companies, and regulatory bodies strive to accelerate drug development, optimize clinical trials, and ensure regulatory compliance, the adoption of Real-World Evidence (RWE) curation AI is gaining significant momentum globally.

Regional Outlook

North America currently holds the largest share of the Real-World Evidence Curation AI market, accounting for approximately 43% of the global market value in 2024. This dominance is attributed to the region’s mature healthcare infrastructure, high adoption rate of advanced digital technologies, and a favorable regulatory environment that encourages real-world data utilization for clinical and regulatory decision-making. The presence of leading pharmaceutical companies and technology innovators, coupled with substantial investments in healthcare AI, further reinforces North America’s leadership. The United States, in particular, has been at the forefront, driven by initiatives from the FDA and other agencies to integrate RWE into the drug approval and post-market surveillance processes.

The Asia Pacific region is poised to be the fastest-growing market for Real-World Evidence Curation AI, projected to register a CAGR of 25.4% from 2025 to 2033. This exponential growth is fueled by increasing healthcare digitization, expanding clinical trial activities, and significant investments from both public and private sectors in countries such as China, India, Japan, and South Korea. The region’s rapidly growing patient population, rising prevalence of chronic diseases, and government initiatives to modernize healthcare systems are creating fertile ground for the adoption of AI-driven RWE solutions. Additionally, the emergence of local AI startups and partnerships with global technology providers are accelerating innovation and market penetration.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing Real-World Evidence Curation AI, though adoption remains at a nascent stage compared to developed regions. Key challenges include limited healthcare IT infrastructure, data privacy concerns, and a shortage of skilled professionals. However, localized demand is rising as governments and healthcare organizations recognize the value of RWE in improving patient outcomes and optimizing resource allocation. Policy reforms, international collaborations, and targeted investments are expected to drive incremental adoption, especially in urban centers and among leading healthcare providers.

Report Scope

Data Product Catalog with AI Market Outlook

According to our latest research, the global Data Product Catalog with AI market size reached USD 3.2 billion in 2024, with a robust Compound Annual Growth Rate (CAGR) of 21.7% projected through the forecast period. By 2033, the market is expected to attain a value of USD 23.6 billion. This impressive expansion is primarily driven by the increasing adoption of artificial intelligence for data curation, cataloging, and management across diverse industries, as well as the growing demand for seamless data discovery and governance solutions.

The principal growth factors propelling the Data Product Catalog with AI market include the exponential growth of data volumes across enterprises, coupled with the rising complexity of data ecosystems. Organizations are increasingly recognizing the need to efficiently catalog, access, and govern vast datasets to drive actionable insights and maintain regulatory compliance. AI-powered data product catalogs facilitate automated metadata management, intelligent search, and dynamic data lineage tracking, thereby reducing manual intervention and enhancing data accessibility. The proliferation of digital transformation initiatives, particularly in sectors such as BFSI, healthcare, and retail, further accelerates the adoption of these advanced cataloging solutions.

Another significant driver is the integration of AI capabilities that enable contextual data discovery, anomaly detection, and recommendation systems within data catalogs. These advanced features empower enterprises to unlock deeper analytical value from their data assets, improve decision-making processes, and foster a data-driven culture. The ongoing shift toward cloud-native architectures and hybrid data environments has also necessitated the deployment of scalable and interoperable data catalog solutions. As organizations embrace multi-cloud and hybrid strategies, the demand for unified, AI-enhanced data product catalogs that ensure seamless data integration and governance is surging globally.

Furthermore, the increasing emphasis on data privacy and regulatory compliance, such as GDPR and CCPA, is compelling organizations to invest in robust data cataloging solutions with embedded AI functionalities. These solutions not only automate the identification and classification of sensitive data but also streamline compliance reporting and auditing processes. The convergence of AI, machine learning, and data catalog technologies is enabling enterprises to proactively manage data quality, improve data trustworthiness, and accelerate time-to-insight. Collectively, these trends are shaping the rapid evolution and expansion of the Data Product Catalog with AI market.

In the evolving landscape of data management, Dataplace Curation AI is emerging as a pivotal tool for enhancing the efficiency and accuracy of data cataloging processes. By leveraging advanced artificial intelligence, Dataplace Curation AI automates the curation of vast datasets, ensuring that data is not only organized but also enriched with meaningful metadata. This technology empowers organizations to streamline data discovery, reduce manual errors, and enhance the overall quality of their data assets. As businesses increasingly rely on data-driven insights, the role of Dataplace Curation AI in facilitating seamless data integration and governance becomes ever more critical. Its ability to dynamically adapt to changing data environments and user needs positions it as a key enabler of digital transformation across industries.

Regionally, North America continues to dominate the Data Product Catalog with AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of technology giants, early adoption of AI-driven data management solutions, and mature digital infrastructure are key factors supporting North America's leadership. Meanwhile, Asia Pacific is poised for the fastest growth over the forecast period, fueled by the rapid digitalization of economies, increasing investments in AI and cloud technologies, and expanding enterprise data footprints. EuropeÂ’s stringent data privacy regulations and focus on data governance further contribute to the robust adoption of AI-powered data catalogs in the region.

Academic libraries are evolving rapidly in the face of digital transformation, artificial intelligence (AI), and changing user expectations. This research article examines the future-ready skills, knowledge, and competencies needed by academic librarians at the City University of New York (CUNY), positioning CUNY as a case study for innovation in a large public urban university system. Through a comprehensive literature review of global trends and an analysis of CUNY’s strategic priorities, five core competency areas emerge as crucial: (1) advanced digital skills and IT proficiency, (2) AI literacy and ethical awareness, (3) facility with immersive technologies (augmented and virtual reality), (4) data curation and data literacy expertise, and (5) community engagement and outreach capabilities. The introduction frames the challenges and objectives. The literature review synthesizes recent scholarship (2020–2025) on emerging competencies in academic librarianship worldwide. The methodology describes the qualitative approach of integrating literature analysis with a case study of CUNY libraries. Findings detail each competency area, highlighting global exemplars and specific CUNY initiatives. The discussion interprets how these competencies align with broader academic library trends such as digital transformation, open science, and diversity, equity, and inclusion (DEI) efforts. Recommendations are offered for CUNY and similar institutions to implement training, professional development, and strategic planning that cultivate these future-ready competencies. The conclusion underscores that empowering librarians with these emerging skills will position academic libraries—at CUNY and beyond—as innovative, inclusive centers of knowledge in the digital age.

Safety Training Data Curation Market Outlook

According to our latest research, the global Safety Training Data Curation market size reached USD 1.32 billion in 2024, reflecting robust growth momentum. The market is projected to expand at a CAGR of 12.1% during the forecast period, reaching USD 3.38 billion by 2033. This remarkable growth is primarily driven by the escalating need for accurate and reliable data to power safety training programs across diverse industries, as organizations increasingly prioritize workplace safety and compliance in an evolving regulatory landscape.

One of the primary growth factors fueling the expansion of the Safety Training Data Curation market is the heightened emphasis on workplace safety regulations and compliance standards globally. As governments and industry bodies enforce stricter safety mandates, organizations are compelled to adopt advanced safety training solutions. The demand for curated, high-quality datasets is intensifying, as these datasets form the backbone of effective safety training modules, especially those leveraging artificial intelligence and machine learning. The rise in workplace accidents, coupled with the increasing complexity of industrial operations, further underscores the necessity for meticulously curated safety training data. Organizations are investing heavily in digital transformation initiatives, which include the integration of data-driven safety training programs to reduce incidents and improve overall workforce safety.

Another significant driver is the rapid digitalization of training environments and the adoption of immersive technologies such as virtual reality (VR) and augmented reality (AR) in safety training. These technologies require vast amounts of curated data to simulate real-world scenarios and deliver effective experiential learning. The proliferation of cloud-based platforms has also made it easier for organizations to access, manage, and update safety training data, thereby enhancing scalability and flexibility. Additionally, the increasing prevalence of remote and hybrid work models has necessitated the development of digital safety training programs, further boosting demand for curated data that can be seamlessly integrated into diverse training delivery modes. The growing awareness among enterprises about the tangible benefits of data-driven safety training, including reduced incident rates and improved compliance, is expected to sustain market growth over the coming years.

The market is also benefiting from the surge in investments by both public and private sectors in occupational health and safety (OHS) initiatives. Governments across regions are launching campaigns and providing incentives to promote workplace safety, which in turn is driving the adoption of advanced safety training solutions. The integration of artificial intelligence, big data analytics, and IoT technologies into safety training programs requires large volumes of high-quality, annotated data, further propelling the need for professional data curation services and software. However, the market faces challenges such as data privacy concerns, high initial costs, and the complexity of curating data across multiple languages and regulatory frameworks. Despite these hurdles, the market outlook remains positive, with continuous technological advancements and regulatory support expected to create new growth avenues.

From a regional perspective, North America currently dominates the Safety Training Data Curation market, owing to the presence of stringent regulatory standards, a mature industrial sector, and high adoption of advanced training technologies. Europe follows closely, driven by robust workplace safety regulations and increasing investments in digital transformation. The Asia Pacific region is anticipated to witness the highest CAGR during the forecast period, fueled by rapid industrialization, growing awareness of workplace safety, and expanding manufacturing and construction sectors. Latin America and the Middle East & Africa are also expected to register notable growth, supported by improving regulatory frameworks and increasing focus on occupational safety. The regional outlook indicates a broadening global footprint for safety training data curation solutions, with significant opportunities for market players to capitalize on emerging markets.

Component Analysis

The Component segment of the Safety Training Data Curation market is bifurca

Snapshot img { margin: 10px !important; } AI Data Labeling Market Size 2025-2029

The ai data labeling market size is forecast to increase by USD 1.4 billion, at a CAGR of 21.1% between 2024 and 2029.

The escalating adoption of artificial intelligence and machine learning technologies is a primary driver for the global ai data labeling market. As organizations integrate ai into operations, the need for high-quality, accurately labeled training data for supervised learning algorithms and deep neural networks expands. This creates a growing demand for data annotation services across various data types. The emergence of automated and semi-automated labeling tools, including ai content creation tool and data labeling and annotation tools, represents a significant trend, enhancing efficiency and scalability for ai data management. The use of an ai speech to text tool further refines audio data processing, making annotation more precise for complex applications.Maintaining data quality and consistency remains a paramount challenge. Inconsistent or erroneous labels can lead to flawed model performance, biased outcomes, and operational failures, undermining AI development efforts that rely on ai training dataset resources. This issue is magnified by the subjective nature of some annotation tasks and the varying skill levels of annotators. For generative artificial intelligence (AI) applications, ensuring the integrity of the initial data is crucial. This landscape necessitates robust quality assurance protocols to support systems like autonomous ai and advanced computer vision systems, which depend on flawless ground truth data for safe and effective operation.

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

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe global ai data labeling market's evolution is shaped by the need for high-quality data for ai training. This involves processes like data curation process and bias detection to ensure reliable supervised learning algorithms. The demand for scalable data annotation solutions is met through a combination of automated labeling tools and human-in-the-loop validation, which is critical for complex tasks involving multimodal data processing.Technological advancements are central to market dynamics, with a strong focus on improving ai model performance through better training data. The use of data labeling and annotation tools, including those for 3d computer vision and point-cloud data annotation, is becoming standard. Data-centric ai approaches are gaining traction, emphasizing the importance of expert-level annotations and domain-specific expertise, particularly in fields requiring specialized knowledge such as medical image annotation.Applications in sectors like autonomous vehicles drive the need for precise annotation for natural language processing and computer vision systems. This includes intricate tasks like object tracking and semantic segmentation of lidar point clouds. Consequently, ensuring data quality control and annotation consistency is crucial. Secure data labeling workflows that adhere to gdpr compliance and hipaa compliance are also essential for handling sensitive information.

How is this AI Data Labeling Industry segmented?

The ai data labeling industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. TypeTextVideoImageAudio or speechMethodManualSemi-supervisedAutomaticEnd-userIT and technologyAutomotiveHealthcareOthersGeographyNorth AmericaUSCanadaMexicoAPACChinaIndiaJapanSouth KoreaAustraliaIndonesiaEuropeGermanyUKFranceItalySpainThe NetherlandsSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaUAESouth AfricaTurkeyRest of World (ROW)

By Type Insights

The text segment is estimated to witness significant growth during the forecast period.The text segment is a foundational component of the global ai data labeling market, crucial for training natural language processing models. This process involves annotating text with attributes such as sentiment, entities, and categories, which enables AI to interpret and generate human language. The growing adoption of NLP in applications like chatbots, virtual assistants, and large language models is a key driver. The complexity of text data labeling requires human expertise to capture linguistic nuances, necessitating robust quality control to ensure data accuracy. The market for services catering to the South America region is expected to constitute 7.56% of the total opportunity.The demand for high-quality text annotation is fueled by the need for ai models to understand user intent in customer service automation and identify critical

Files:

* F1_performance.ipynb # The code used for generating metrics presented in manuscript Figure 1.
* phasepdbv2_1.csv # The expert-curated data from PhaSepDB V2.1, which is used for performance evaluation.
* validation_set/test_set # The AI-generated information as well as the evaluation results. Note the test set now include files of validation set, but these validation set are excluded during performance evaluation.

Snapshot img

AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot

Semantic Data Catalog AI Market Outlook

According to our latest research, the global Semantic Data Catalog AI market size reached USD 2.67 billion in 2024, reflecting a dynamic surge in enterprise data management solutions. With the growing need for intelligent data discovery and governance, the market is forecasted to expand at a robust CAGR of 24.2% from 2025 to 2033. By the end of 2033, the market is projected to achieve a value of USD 21.39 billion, driven by the widespread adoption of AI-powered data catalogs across multiple industries. The primary growth factor fueling this expansion is the increasing demand for automated metadata management and semantic search capabilities, which are critical for efficient data governance and compliance in today’s data-driven landscape.

The proliferation of big data and the exponential growth in unstructured and structured data volumes are significant contributors to the rapid expansion of the Semantic Data Catalog AI market. Enterprises are increasingly recognizing the value of leveraging advanced AI and machine learning technologies to automate data cataloging, streamline data discovery, and enhance data governance processes. As organizations grapple with disparate data sources and complex data environments, the need for semantic understanding and intelligent metadata management has become paramount. Semantic data catalogs powered by AI enable businesses to extract meaningful insights from vast datasets, improve data accessibility, and drive informed decision-making. This shift towards intelligent data management is further accelerated by the rise of cloud computing, digital transformation initiatives, and the adoption of hybrid and multi-cloud architectures, all of which necessitate robust, scalable, and automated data catalog solutions.

Another key growth driver for the Semantic Data Catalog AI market is the increasing emphasis on regulatory compliance and data privacy. With stringent data protection regulations such as GDPR, CCPA, and other regional mandates coming into effect, organizations are under immense pressure to ensure data transparency, traceability, and accountability. AI-powered semantic data catalogs offer advanced capabilities in metadata management, data lineage tracking, and automated policy enforcement, enabling organizations to achieve and maintain compliance with evolving regulatory requirements. Additionally, these solutions empower data stewards and business users with self-service access to trusted data assets, reducing the dependency on IT teams and accelerating the pace of analytics and innovation. As the regulatory landscape continues to evolve, the adoption of semantic data catalogs is set to become a strategic imperative for organizations seeking to mitigate compliance risks and foster a culture of data governance.

Moreover, the growing trend of data democratization and the need for business agility are propelling the adoption of Semantic Data Catalog AI solutions across diverse industry verticals. Enterprises are striving to break down data silos and provide seamless access to relevant data assets for various stakeholders, including data scientists, analysts, and business users. Semantic data catalogs equipped with AI-driven search, recommendation, and classification capabilities enable users to discover, understand, and utilize data more efficiently. This not only enhances operational efficiency but also accelerates time-to-insight and drives competitive advantage. The integration of semantic technologies with AI further facilitates context-aware data discovery, knowledge graph generation, and intelligent data curation, unlocking new opportunities for innovation and value creation in the digital economy.

From a regional perspective, North America continues to dominate the Semantic Data Catalog AI market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The strong presence of leading technology providers, early adoption of AI and cloud-based solutions, and a robust regulatory framework contribute to the region’s leadership position. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in data-driven initiatives across emerging economies such as China, India, and Southeast Asia. Europe’s market growth is bolstered by stringent data privacy regulations and the rising demand for advanced data governance solutions among enterprises. As organizations worldwide prioritize data-driven strategies, the Semantic Data

"Collection of 100,000 high-quality video clips across diverse real-world domains, designed to accelerate the training and optimization of computer vision and multimodal AI models."

Overview This dataset contains 100,000 proprietary and partner-produced video clips filmed in 4K/6K with cinema-grade RED cameras. Each clip is commercially cleared with full releases, structured metadata, and available in RAW or MOV/MP4 formats. The collection spans a wide variety of domains — people and lifestyle, healthcare and medical, food and cooking, office and business, sports and fitness, nature and landscapes, education, and more. This breadth ensures robust training data for computer vision, multimodal, and machine learning projects.

The data set All 100,000 videos have been reviewed for quality and compliance. The dataset is optimized for AI model training, supporting use cases from face and activity recognition to scene understanding and generative AI. Custom datasets can also be produced on demand, enabling clients to close data gaps with tailored, high-quality content.

About M-ART M-ART is a leading provider of cinematic-grade datasets for AI training. With extensive expertise in large-scale content production and curation, M-ART delivers both ready-to-use video datasets and fully customized collections. All data is proprietary, rights-cleared, and designed to help global AI leaders accelerate research, development, and deployment of next-generation models.

Intellizence is an award-winning AI platform focused on monitoring growth & sales, risk & distress signals in companies of interest. Intellizence helps customers to identify emerging business opportunities & risks and make timely strategic & tactical decisions.

Intellizence Company News Signals API delivers curated news signals about your interested public & private companies.

Customers / Clients - Monitor news related to sales & risk signals like M&A, CXO changes, cost-cutting, etc.

Competitors - Track competitive moves like product launches, partnerships, new clients acquisitions, etc.,

Portfolios - Monitor news related to growth & distress signals like business expansion, Joint Venture, sustainability initiatives, employee activism, etc

Suppliers - Monitor adverse news like supply chain disruption, factory fire, employee strike, etc.,

Partners - Track news related to major partnership announcements, product launches, etc.,

The API is designed for product & data teams. Stop spending time, effort & cost in searching for news about your interested companies.

Accelerate your product launches by doing a bold integration with Intellizence Company News Signals API. The API gives the flexibility to customize news signals for the companies & triggers relevant to you.

Intellizence News Signals are highly curated with a signal relevance of over 95%. The curation is done by a proprietary curation platform powered by advanced Natural Language Processing, Machine Learning & Deep Learning techniques and validated by human curators to ensure the signals are contextual and relevant.

Aggregated from thousands of business news sources in real-time Noise-filtered De-duplicated Contextually classified to ~80 sales & growth, risk & distress signals Delivered through Rest API