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Data Annotation Tools Market: Information by Type (Text, Image/Video, Audio), Annotation Type (Manual, Semi-supervised, Automatic), Vertical (IT, Automotive), and Region — Forecast till 2030

Market Snapshot
Study Period:
2020-2030
Base Year:
2021
Fastest Growing Market:
North America
Largest Market:
Asia Pacific
CAGR:
26 %
Key Players
Appen Limited

Annotate

CloudApp

Cogito Tech LLC

Deep Systems

LightTag

Labelbox Inc

The global data annotation tools market size is expected to reach a valuation of USD 5,037 million by 2030 growing at a CAGR of 26% during the forecast period (2022–2030). A data annotation tool is a software solution that may be used to annotate production-grade training data for machine learning. It can be cloud-based, on-premise, or containerized. At the same time, some businesses prefer to construct their tools, numerous data annotation solutions accessible as open-source or freemium.

Commercially, they are available for lease and purchase. Image, video, text, audio, spreadsheet, and sensor data annotation tools are all built to work with certain forms of data. They also provide many deployment options, such as on-premise, container, SaaS (cloud), and Kubernetes.

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Market Dynamics

Market Drivers: Rapid Penetration of AI in the Healthcare Sector

Data annotation is predicted to define a significant role in improving AI applications in healthcare. In medical imaging data technologies, AI-powered systems employ computer vision or machine vision to identify potential injuries and find trends, supporting health professionals in automatically writing reports once the patient has been assessed.

Artificial intelligence can quickly scan a database of X-Ray pictures, MRI scans, and CT scans to detect various injuries. To develop the final reports of the examined individuals, data annotation tools assist systems based on AI in separating data gathered from average and wounded medical photos. As a result, data annotation is projected to define a significant role in improving AI applications in the medical and healthcare industry.

For example, Innodata Inc., a U.S.-based startup, said in March 2021 that it was expanding its AI-based data annotation tools abilities to incorporate patient medical reports. Innodata wishes to merge its AI dataset annotating tools console and Synodex medical data extracting platform competencies to generate a medical record data annotation platform. This will result in high-standard artificial intelligence data training that will likely be HIPAA compliant and adhere to all security requirements.

Market Drivers: Generation of Massive Data Via Advanced Technologies

The main advantage of employing annotation tools is that the combination of data attributes allows users to manage data definition, eliminating the need to rewrite similar rules on numerous sites. The proliferation of enormous datasets and the rise of big data will almost certainly entail the usage of artificial intelligence technology in data annotation.

Massive data is generated by technologies like machine learning (ML), robotics, advanced predictive analytics, artificial intelligence (AI), and the Internet of Things (IoT). Data efficiency is becoming important as technology evolves, allowing for new economies, infrastructure, and business innovations. These elements have considerably aided the industry's expansion. Due to the increased scope of growth in data labeling, companies developing AI-enabled healthcare apps collaborate with data annotating companies to give the necessary sets of data to assist corporations in improving their deep learning and machine learning skills.

For example, Telus International, a supplier of digital IT technologies and customer experience, announced the acquisition of Lionbridge AI. This company provides annotation platform solutions for creating AI algorithms and training data that fuel machine learning in November 2020. Telus International's next-generation digital technology portfolio will be enhanced as a result of the acquisition, as well as its global reach.

Market Restraints: Inaccuracy of Data Annotation Tools

The inconsistency of data annotation tools to deliver accurate results hampers the market's growth. An image is given, for example, maybe low pixel and contains several items, making labeling difficult. The market's key challenge is the inaccuracy of the data labeled quality. In some circumstances, manually labeled data may contain errors, and the period it takes to uncover these errors varies, adding to the overall expense of the process of annotation.

However, as efficient algorithms are devised, the precision of autonomous data annotating tools is improving, eventually eliminating the need for manual annotations and reducing tool prices.

Market Trends: Growing Adoption of Data Annotation Tools By Corporations

The efficiency of automated data annotation tools and the rising use of cloud-based computing resources to annotate massive datasets contribute to market growth. Businesses' use of data annotation tools for their accuracy and for labeling large volumes of AI training data are two more important factors that can propel the industry forward in the near future.

For corporations managing the workforce and data has always been an issue. The adoption of data annotation tools helps corporations to solve these problems. Every data annotation tool, even those that lead with an AI-based automation capability, is designed to be used by a human workforce. As a result, top systems will include workforce management features like task assignment and productivity analytics, which track how much time is spent on each task or subtask.

Data labeling labor providers may bring their technology to examine quality work data. They might employ cameras, screenshots, inactivity timers, and clickstream data to figure out how they might help workers offer high-quality data annotation.

Annotation starts with a complete approach to managing the datasets businesses intend to annotate. Corporations must guarantee that the solution they are evaluating will import and support the high amount of data and file types they need to label as a crucial element of their workflow. This includes dataset searches, filters, sorting, cloning, and merging.

Additionally, the growing demand for annotated data to improve machine learning models and increased investments in autonomous driving technology improvements is expected to boost the market.

 

Impact of COVID-19

The COVID-19 pandemic lifted the global data annotation tools market significantly. During the COVID-19 period, demand for data annotation tools was expected to grow due to machine learning and artificial intelligence technologies. Growth in-text annotation for document classification has also been a key variable driving the market during the pandemic's onset.

Covid -19 Impact On Market

Artificial intelligence and machine learning were likely to be heavily used in the healthcare sector to develop new technologies to combat coronaviruses. Additionally, initiatives such as preparing lung datasets to study the impact of the disease on the lungs are anticipated to augment the data annotation business, which began during the COVID era. On the contrary, a lack of experience and a qualified staff hampered the efficient execution of operations, which was likely to have affected the market.

Market Recovery Timeline and its Challenges

As the outbreak passes, the global market's recovery may be delayed by a shortage of skilled professionals and workers. However, factors such as the rising adoption of artificial intelligence across many sectors and the collection of large amounts of data due to improved technology implementation will continue to drive the market further. As a result, the global data annotation tools market will quickly recover.

 

Segmental Analysis of Data Annotation Tools Market

The global data annotation tools market share has been classified based on type, annotation type, vertical, and regions.

Based on type, the data annotation tools market has been segmented into audio, image/video, and text. The image/video type segment is likely to dominate the global market, and it is projected to reach USD 1,840 million by 2030, registering a CAGR of 26% during the forecast period. The field of medical science, particularly in medical imaging, uses image data annotation extensively. 

By the first half of 2018, overall startup investments in designing machine learning technologies based on medical pictures had reached USD 522 million. Arteries, Zebra Medical Vision, and Infervision are some of the most well-known startups in the data annotation business in the medical and healthcare sector.

The data annotation tools market has been segmented into automatic, semi-supervised, and manual, based on annotation type. The automatic annotation segment is likely to dominate the global market during the forecast period. Artificial intelligence is becoming increasingly important in the data annotation sector because it enables the extraction of sophisticated abstractions from datasets through a learning process with a hierarchy. The demand for automatic data annotation tools is likely to increase as the necessity of extracting, and mining patterns from extensive data grows.

The data annotation tools market has been segmented into automotive, government, retail, IT, healthcare, financial services, and others based on vertical. The healthcare vertical segment is likely to dominate the global market during the forecast period. 

Artificial intelligence is frequently used for diagnostic automation, treatment prediction, gene sequencing, and medication discovery, among other medical and healthcare applications. Machine learning techniques must be used to train a set of information in the healthcare industry. The standard of the training heavily influences the accuracy and efficiency of the algorithm designed to construct applications based on artificial intelligence. Accessibility to reliable and high-quality sets of data is necessary for creating an effective AI-enabled healthcare product. As a result, data annotation tools urge the market forward by supplying artificial intelligence with learning information volumes.

 

Regional Analysis of Data Annotation Tools Market

The global data annotation tools market share has been segregated into North America, Europe, Asia-Pacific, South America, and the Middle East and Africa. 

Data Annotation Tools Market Regional Analysis

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With a market value of USD 1,405 million by 2030, registering a CAGR of 29%, Asia Pacific is expected to be the most significant data annotation tools market. Developing countries in the Asia Pacific have a lot of potential for data annotation tool adoption, especially in financial services and healthcare. The use of technology and creative healthcare access programs are driving the expansion of the Asia-Pacific’s healthcare sector. These variables are likely to increase the demand for image data annotation technologies over the forecast period in this region.

For example, in April 2021, Congenica Ltd, a developer of data analytics tools for annotating and dynamically evaluating genome sequencing data, partnered with Camtech Diagnostics, a microfluidics-focused software company based in the UK. Congenica's position in nations including Japan, Malaysia, South Korea, and Singapore is projected to grow due to this endeavor.

North America is likely to be the second-largest data annotation tools market, with a market value of USD 1,392 million by 2030, registering a CAGR of 25%. Canada and the United States are investing more in modern industrial technologies. Technological advancements have accelerated the introduction of the data annotation tools concept.

The North American health, industrial, and automotive industries are all seeing significant investment efforts expected to grow significantly. This is owing to market vendors' aggressive product and geographic expansion strategies to achieve a competitive advantage.

 

Top Industry Players

  • Appen Limited
  • Annotate
  • CloudApp
  • Cogito Tech LLC
  • Deep Systems
  • LightTag
  • Labelbox Inc
  • Lotus Quality Assurance
  • Playment Inc
  • Tagtog Sp.
  • CloudFactory Limited
  • ClickWorker GmbH
  • Alegion
  • Figure Eight Inc.
  • Amazon Mechanical Turk, Inc
  • Explosion AI Gmbh
  • Mighty AI, Inc
  • Trilldata Technologies Pvt Ltd
  • Scale AI, Inc.
  • Google LLC
  • Lionbridge Technologies, Inc
  • SuperAnnotate LLC.

 

Recent Developments

  • August 2021- Appen Limited, an Australian firm that provides high-quality training data to companies developing AI systems, has announced its intention to buy Quadrant, a global leader in mobile location data, related compliance services, and Pint-of-Interest data.
  • May 2021- Slack and Atlassian applications Jira and Confluence have joined CloudApp, a cross-platform for the modern office.
  • November 2020- Telus International, a supplier of digital customer experience (CX) and digital IT solutions & services, announced the acquisition of Lionbridge AI. This company provides training data and annotation platform solutions for creating AI algorithms that fuel machine learning.
  • March 2021- Innodata Inc., a U.S.-based startup, announced that it was expanding its AI data annotation tools to incorporate patient medical reports. To generate a medical record data annotation platform, Innodata wants to combine its capabilities from the AI Data annotation tools platform and the Synodex medical data extraction platform. This will result in high-quality AI training data that will likely be HIPAA compliant and adhere to all security requirements.

 

Global Data Annotation Tools Market: Segmentation

By Type

  • Text
  • Image/Video
  • Audio

By Annotation Type

  • Manual
  • Semi-Supervised
  • Automatic

By Vertical

  • IT
  • Automotive
  • Government
  • Healthcare
  • Financial Services
  • Retail
  • Others

By Region

  • North America
  • Europe
  • Asia-Pacific
  • South America
  • The Middle East and Africa
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