Data Annotation Tools Market worth over $5bn by 2026
Published Date: January 28, 2020
Data Annotation Tools Market size is set to surpass USD 5 billion by 2026, according to a new research report by Global Market Insights, Inc.
Data annotation tools market growth is supported by growing trend of leveraging AI technologies for document categorization and classification. Annotated text data enables enterprises in streamlining workflows, minimizing delays, and eliminating bottlenecks caused due to manual document classification. With growing volumes of textual data and the increasing importance of accurately classifying documents, data annotation tools are rapidly gaining traction as viable solutions for document labeling.
Data annotation tool providers are focusing on providing advanced text & document classification solutions to enable enterprises in effectively training their machine learning models. For instance, in November 2019, IBM launched IBM Lambada, a data synthesizer platform, which automatically generates labeled datasets for text classification. The new software helps the company in addressing the challenging issues of grammatic and semantic data labeling in complex documents. With the increased focus of data annotation tool providers in offering new and innovative solutions, the market is expected to witness rapid growth over the forecast timespan.
Rising uptake of image labeling in healthcare augmenting data annotation tools market growth
Healthcare enterprises are increasingly adopting data labeling solutions for annotating vast volumes of patient medical records, leading to improved AI-based diagnosis and medical care. Accurately annotated medical images/videos can assist healthcare institutes in implementing advanced AI technologies, which can derive medical conclusions without any direct human input from medical experts.
The margin of errors in such algorithms can be vastly reduced by using reliable and accurate annotated data as input. For instance, in May 2018, researchers from Monash University and University of Queensland used more than 100,000 labeled medical images to train an AI algorithm for recognizing skin cancer. The AI algorithm exhibited greater accuracy than human experts, with over 95% correct results compared to 86.6% detected by medical professionals. With the continuously rising uptake of data annotation tools for developing automated diagnosis systems, the market is expected to exhibit a sharp increase over the forecast timeline.
Browse key industry insights spread across 230 pages with 259 market data tables and 29 figures & charts from the report, “Data Annotation Tools Market Size By Data Type (Image/Video [Bounding Box, Semantic Annotation, Polygon Annotation, Lines and Splines], Text, Audio), By Annotation Approach (Manual Annotation, Automated Annotation), By Application (Telecom, BFSI, Healthcare, Retail, Automotive, Agriculture), Industry Analysis Report, Regional Outlook, Growth Potential, Competitive Market Share & Forecast, 2020 – 2026” in detail along with the table of contents:
The audio labeling segment to grow impressively with developments in speech recognition
The audio data annotation tools market is expected to witness a lucrative growth rate of over 25% over the forecast timeline as enterprises are focusing on leveraging audio training data to develop sophisticated speech recognition applications such as virtual assistants, speech to text programs, and chatbots. With the proliferation of digital voice assistants, such as Amazon Alexa, Apple Siri, and Google voice assistant in smartphones and IoT devices, audio data labeling has become increasingly crucial to ensure the effective performance of natural language generation algorithms used in these assistants.
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According to the April 2019 press release by Amazon, the company regularly annotates multiple voice samples sourced from Alexa recordings to train speech recognition and natural language generation models. High performance automated data labeling tools are assisting Amazon in transitioning from the conventional supervised learning models to fully unsupervised transcribing and annotation algorithms, vastly improving performance and user experience of Alexa assistant.
Increasing volume of training datasets propelling the deployment of automated annotation
The automated data annotation tools market is set to witness the fastest growth rate with a CAGR of nearly 40% from 2020 to 2026. This is attributed to growing importance of automated data labeling tools in handling large volumes of raw, unlabeled data, which are too complex and time-consuming to be annotated through manual labeling approaches. Fully automated data labeling can quickly and accurately convert datasets into high-quality input training data, assisting enterprises in accelerating the development cycle of their AI-based projects.
As the manual data labeling process is time-consuming and more prone to errors, automated data labeling solutions can address these issues by precisely annotating data without issues of fatigue or errors. For instance, in August 2019, researchers from Central South University of Forestry and Technology, China, developed AI systems for assessing the suitability of agricultural land for farming through automated data labeling tools. The research team was able to achieve accuracy of over 80% with automated annotation tools while reducing the data labeling time by more than half.
Demand for cognitive computing in the telecom sector propelling data annotation tools market growth
The telecom sector will witness rapid growth in the adoption of data annotation tools with increasing investments in machine learning and AI. According to the October 2018 report published by the World Mobile Congress, more than 76% of the global telecom enterprises are focusing on integrating AI & related technologies into their business operations by 2021. Data labeling tools assist telecom enterprises in processing and analyzing data for a variety of applications including customer experience, network automation, business process automation, launching of innovative digital services, and timely maintenance of infrastructure.
Increasing number of AI startups propelling the uptake of data annotation tools in China
China is expected to witness growth at over 35% CAGR from 2020 to 2026 due to surging adoption of data labeling technologies by the AI industry. By October 2018 China had 14 AI-focused startups valued at over USD 1 billion, with a combined market valuation of over USD 40 billion. Startups, such as iFlytek, Hikvision, and Deepmind, are extensively deploying data annotation tools for developing novel technologies in the field of voice recognition, computer vision, and natural language processing.
Data annotation tools industry players are laying emphasis on forging strategic alliances and improving their research capabilities to offer new solutions and provide regular software updates, strengthening their market position and gaining a competitive edge. For instance, in November 2019, Scale AI, a global provider of data labeling solutions, partnered with Plotly, a Canadian AI-based firm. The partnership helped the company to offer custom AI models for data labeling to its customers, without the complexities of analyzing the underlying code.
Some of the key players operating in the data annotation tools market include Alegion, Inc., Appen Limited, Amazon Web Services, Inc., Clickworker GmbH, CloudApp, Inc., CloudFactory Limited, Cogito, Google LLC, Hive, IBM Corporation, iMerit, Labelbox, Inc., LionBridge AI, Mighty AI, MonkeyLearn Inc., Neurala, Inc., Playment Inc., Samasource Inc., Scale, Inc., Trilldata Technologies Pvt. Ltd., and Webtunix AI.
The increasing investments for accelerating the commercialization of autonomous technologies created a positive outlook for the data labelling tools industry. Data annotation techniques will benefit significantly as automotive manufacturers seek out high precision annotated data to improve their on-boad AI systems.
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