AI Training Dataset Market

Market Overview

The global AI training dataset market was valued at USD 1.32 billion in 2021. It is projected to reach USD 7.23 billion by 2030, growing at a CAGR of 20.8% during the forecast period (2022-2030). Artificial intelligence gives machines the ability to learn from their mistakes, mimic human behavior, and adapt to their environment. These machines are taught to analyze vast amounts of data and find patterns to carry out a specific activity. Introducing these robots to perform a particular task requires specialized datasets. The need for artificial intelligence training datasets is rising to meet this growing demand. The dataset provided determines how well the machines operate entirely and improves the effectiveness of AI. As a result, offering top-notch training datasets becomes crucial. Additionally, it helps to speed up data preparation and improve prediction accuracy. Market players are consequently concentrating on acquiring businesses that may assist them in improving data quality.

Market Dynamics

Global AI Training Dataset Market Drivers

Rapid Growth of AI and Machine Learning

The emergence of big data is anticipated to fuel the expansion of the artificial intelligence market since it necessitates the recording, storing, and analyzing a significant amount of data. End-users are more focused on the need for monitoring and enhancing the computational models associated with big data. This focus is causing them to adopt artificial intelligence solutions more quickly. Since the annotated data catalyzes training AI models and machine learning systems in important domains like speech recognition and a picture identification, the adoption of artificial intelligence is predicted to increase demand for AI training datasets considerably.

Data annotation strengthens AI by explicitly supplying data essential to predicting future outcomes and making decisions. Domain-specific data, including data from many applications like national intelligence, fraud detection, marketing, medical informatics, and cybersecurity, is collected by numerous public and private organizations. By continuously enhancing the accuracy of each piece of data, data annotation enables the labeling of such unstructured and unsupervised data.

Global AI Training Dataset Market Restraint

Lack of Technological Adoption in Developing Regions

In the Asia-Pacific region, data collecting is anticipated to be constrained by substantial restrictions on protecting personal information. In Japan, for instance, the Act on the Protection of Personal Information has been put into effect, prohibiting the transmission of any sensitive personal data to an unapproved entity or location. The inaccurate classification of data serves as a barrier to the market's expansion.

The main issue in the data annotation tools is output precision. The concerns with the output's quality, such as data inaccuracy, should be kept to a minimum. In certain circumstances, manual labeling is not done correctly, and it can take some time to find these labels, which increases the expense of the business. However, it is anticipated that the accuracy of automated AI data training dataset tools will increase with the development of advanced algorithms, lowering the need for manual annotation and tool costs.

Global AI Training Dataset Market Opportunities

Growing Applications of Training Dataset across Diversified Industry Verticals

The amount of digital content in the form of photographs and videos has increased exponentially with digital capturing devices, especially cameras built into smartphones. A significant amount of visual and digital information is being collected and shared through numerous applications, websites, social networks, and other digital channels. With data annotation, several companies have used this freely accessible web content to provide their clients with more innovative and better services. Unstructured text records collected due to the increasing use of Electronic Health Record (EHR) systems are now one of the most critical resources for clinical research. These factors are anticipated to create tremendous opportunities for market growth over the forecast period.

Regional Analysis

The global AI training dataset market is bifurcated into four regions, namely North America, Europe, Asia-Pacific, and LAMEA.

North America Dominates the Global Market

Asia-Pacific is the most significant shareholder in the global AI training dataset market and is expected to grow at a CAGR of 21.5% during the forecast period. Organizations in developing nations like India are significantly boosting the adoption rate of innovative technologies to modernize their enterprises. Additionally, several significant players are concentrating on growing their impact in Asia-Pacific. For instance, Microsoft created a dataset called Indoor Location Dataset to gather various data from buildings in Chinese cities, including the geomagnetic field and indoor Wi-Fi signature. These datasets aid in studying and advancing localization, indoor environments, and navigation. In addition, Microsoft and other significant players are increasing their presence in this religion. These elements are predicted to increase dataset usage in the area and significantly grow throughout the forecast term.

Europe is expected to grow at a CAGR of 20.6%, generating USD 1,990.20 million during the forecast period. By integrating technologies for workflow management, brand buying advertising, and trend forecasting, AI has advanced corporate management practices in Europe. These factors have caused businesses to invest heavily in machine learning and artificial intelligence technology, fueling the expansion of the market for AI training datasets. To improve the productivity of their enterprises, numerous tech firms and small startups are also investing in implementing artificial intelligence. The growth of the market for AI training datasets is accelerated by the direct relationship between the rise in demand for training datasets and the need for artificial intelligence.

North America is anticipated to grow significantly over the forecast period. Vendors are concentrating on supplying new datasets to hasten artificial intelligence technology adoption in emerging North American sectors. For instance, a new dataset for driverless vehicles was released by Google LLC company Waymo LLC. This dataset contains sensor data gathered via video sensors and LiDAR under various driving circumstances, including the presence of pedestrians, cyclists, and other objects. Such advancements influence the market's acceptance of training datasets and serve a sizable portion of the training dataset market.

While Latin American financial institutions frequently implement new technology, such as AI, similarly to their international counterparts, they also confront some particular difficulties. Fortunately, it is getting simpler to overcome these obstacles. Despite having a lower level of technology and investment than their North American counterparts, Latin American nations might decide to take advantage of opportunities and tackle problems with superior resources. The region's countries ought to be aware of the rapid technological development and create national strategies to take advantage of the prospects.

Segmental Analysis

The global AI training dataset market is segmented by type and industry vertical.

Based on type, the global AI training dataset is bifurcated into text, image/video, and audio.

The image/video segment is the highest contributor to the market and is expected to grow at a CAGR of 22.2% during the forecast period. It is a process in which an image/video is manually assigned metadata in captions or keywords or by a computer system. The massive expansion is due to the efforts of key stakeholders to provide new datasets that can be used in a wider variety of contexts. For instance, Google LLC, a global technology business, recently unveiled Google-Locations-v2, a new AI training dataset with millions of photos and thousands of landmarks.

The text segment accounted for a significant share owing to its rising applications in clinical research and e-commerce. With the growing implementation of Electronic Health Record (EHR) systems, the accumulation of clinical data, including unstructured text documents, has become one of the valuable resources for clinical research. Statistical Natural Language Processing (NLP) models have been developed to unlock information embedded in clinical text. Gathering text datasets, or data that resembles text, from numerous sources aids in developing technology that can comprehend textual representations of human language. Machines and applications must consume enormous amounts of text data to advance to this point. Text labeling is highly used in social media monitoring to build recommendation systems. For instance, e-commerce companies use social media data to influence their customers to purchase.

Based on industry verticals, the global AI training dataset is bifurcated into IT, automotive, government, healthcare, BFSI, retail and e-commerce, and others.

The automotive segment owns the highest market share and is expected to grow at a CAGR of 21.1% during the forecast period. The automotive vertical includes automobile manufacturing and supply chain business and autonomous vehicle developments. The top use cases for data collection and labeling in the automotive industry are voice and speech recognition for in-car entertainment, understanding and predicting user behavior, and autonomous vehicles. AI is quickly transforming how the automobile industry used to operate, from autonomous cars to cutting-edge robotics on the manufacturing floor. Artificial intelligence is leading the charge to create a new future of value for the automobile sector thanks to the groundbreaking possibilities of machine learning. While the use of AI in autonomous vehicles has been extensively acknowledged and praised, other industry priority areas include production, engineering, supply chain, customer experience, and mobility services.

The IT segment is expected to grow significantly during the forecast period. This vertical includes technology, software, and related services businesses. The top use cases for data collection and labeling in the IT industry are automatic speech recognition to better understand human language, customer relationship management (CRM)/customer experience management (CEM), consultative services, machine translation, social media analytics, virtual assistants, and chatbots. Various technology companies in the market are using machine learning technology to deliver enhanced user experience and develop innovative products. In order to be efficient, machine learning technology requires high-quality training data to ensure that ML algorithms are continuously optimized. Besides, high-quality datasets help IT companies to enhance various solutions such as computer vision, crowdsourcing, data analytics, virtual assistants, and others. Such factors contribute to the high usage of training datasets in the sector.

According to Gartner, governments should concentrate on growing digital initiatives because, by 2023, more than 85% of governments without a holistic experience strategy will fail to transform services. As a result, governments are prepared to invest in AI, following the lead of enterprises. For instance, the Chinese internet company Terminus and the Danish design firm BIG recently announced plans to develop Cloud Valley, an "AI City," in the city of Chongqing in southwest China.

The retail segment is also anticipated to grow significantly in the data collection and labeling market over the forecast period. The retail and e-commerce vertical holds data collection and labeling processes for grocery stores, e-commerce platforms, and retail chains/conveyance stores. With the help of image labeling, online shoppers can search for clothing or accessories by taking a picture of their choice's texture, print, or color. The photo captured by the smartphone is uploaded to an app that searches an inventory of products to find similar products using AI technology.

Recent Developments

• October 2022- Crowdworks (CEO Park Min-woo), an Artificial Intelligence (AI) training data platform company, announced on the 28th of October that it completed the registration of a US patent for a 'method for selecting worker according to feature of project based on crowdsourcing.’
• June 2022- Amazon Web Services Inc. added new capabilities to its cloud platform to help developers write code more efficiently and generate training datasets for their artificial intelligence projects.