TORONTO, CANADA, July 9, 2020/ — Data annotation is the progression of pre-processing data to make it operational for machine learning. The word “annotation” denotes any metadata tag used to mark up the fundamentals of a dataset. Adding meaningful metadata on top of the unique data set computer provides a layer of rich information for input into machine learning models. Data annotation basically is the process of labelling the data. This data can be in any of the following forms like images, text, audio, and video.
The Global Data Annotation market was valued at US$ 320.0 Million in 2018 and is expected to reach US$ 1,820 Million by the year 2026, growing at a CAGR of ~24.5% during the forecast period (2018-2026). Increasing investments in research & development for autonomous vehicles are also contributes significantly to the growth of the market. Data annotation tools are playing an important role for the enhancement of AI applications in medical imaging technologies. The increasing demand for mining meaningful patterns from huge chunk of data is also accentuating the growth of data annotation tools market growth.
Data annotation tools provide users for improving the data value by labeling & adding data attributes into it. Different combination of data characteristics helps the end-users for managing the data definition at a centralized location and eliminating the duplicity of rewriting similar standards in multiple places.
Image recognition and video processing are used to train machine learning models to recognize and block sensitive content, guide autonomous vehicles, and categorize e-commerce product listings.
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Supervised data annotation tools give reduced administration efforts and a high level of data integrity which results in improved chances of discovering interesting insights related to data as compared to other types of annotation tools. The market share for supervised annotation in Europe was ~43% in 2018 among different data annotation types.
A Data Annotation Tool should comply with regulatory or other requirements, based on the level of security, data requires. To use a data annotation tool, it should have a documented data security approach for workforce, technology, network, and workspace, which is mostly provided by Linux, where Windows and Mac OS are also on the top providers list.The Linux segment accounted for the highest share of ~47% in 2018 and is expected to dominated the market during the forecast period.
The growing technological evolution in the telecom industry is making telecom operators to use advanced technologies such as machine learning to analyze the vast amount of data keep up the changing ecosystem and better serve their customers.The Global IT and Telecom segment for Data Annotation Tool is expected to expand at a CAGR of ~23% during the forecast period.
Data Annotation Leadership Mapping-2020
Mechanical Turk, Lionbridge AI, Edgecase, Crowdflower, Clickworker are providing solution to get crowdsourced human-annotated data in North American countries and also has prominent services all over the World.
Data annotation tools market is highly competitive in nature with major players including Amazon Mechanical Turk, Inc., Alegion, Annotate, Appen Limited, Clickworker GmbH, CloudApp, CloudFactory Limited, Cogito, iMerit, Innodata and Playment Inc. Some of the recent developments in this market are Alegion (USA) provider of labeling and annotation services for enterprise data science teams (USA), has secured $12 million in series A funding contributed by RHS Investments.
Playment’s annotation tools deliver high quality labeled datasets at benchmarking speed. AI assistance, Superior design and Configurability make their tools smarter and their major client includes, Daimler, Samsung, Alibaba, SAIC Motors, VAYAVisions etc.
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Table of Contents
1 Introduction
1.1 Goal & Objective
1.2 Report Coverage
1.3 Supply Side Data Modelling & Methodology
1.4 Demand Side Data Modelling & Methodology
2 Executive Summary
2.1 Regional Snapshot
2.2 Global Data Annotation Tools Market by Data Form
2.3 Global Data Annotation Tools Market by Annotation Type
2.4 Global Data Annotation Tools Market by Type of Compatible Platform
2.5 Global Data Annotation Tools Market by End-use Industry
3 Market Outlook
3.1 Introduction
3.2 Current & Future Outlook
3.3 DROC
3.3.1 Drivers
3.3.1.1 Demand Drivers
3.3.1.2 Supply Drivers
3.3.2 Restraints
3.3.3 Opportunities
3.3.4 Challenges
3.4 Market Entry Matrix
3.5 Market Opportunity Analysis
3.6 Market Regulations
3.7 Pricing Mix
3.8 Key Customers
3.9 Value Chain & Ecosystem
4 Market Demand Analysis
4.1 Product Benchmarking
4.1 Competitor Benchmarking
4.3 Business Recommendations
4.3.1 Invest in key growth segments
4.3.2 Potential End-use Market
4.3.3 Focus on Regions
4.3.4 Market Expansion & Product Launch
4.3.5 Investment on R&D
4.3.6 Marketing Strategy
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