Data Annotation Tools Market Global Industry Analysis, By System, Growth Potential, Share, Top Key Players, Trends & Forecast to 2027

The data annotation tools market is estimated to register a commendable growth by 2027 owing to rising adoption of big data analytics by government agencies, extensive product usage for medical image labelling, growing adoption of polygonal annotation, and surging product use in the banking sector.
Data annotation is the method of labeling the data, which is often present in different formats like videos, images, and texts. Data labeling makes the objects identifiable to computer vision, which further instructs the machine. In general, the process aids the machine in understanding as well as memorizing the input patterns.
The data annotation tools market is segmented in terms of data type, annotation approach, application, and regional landscape.
Based on data type, the market for data annotation tools is classified into audio, text, and image/video. Here the image/video segment is further classified into lines & splines, polygonal annotation, semantic annotation, and bounding box. Among these, the polygonal annotation segment is projected to show around 37.3% CAGR through the analysis timeline. Polygonal annotation offers higher level of precision in object detection as well as image/video localization in comparison to other types, which is fostering segment growth.
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In terms of application, the overall data annotation tools market is bifurcated into agriculture, automotive, retail, healthcare, BFSI, and IT & telecom. BFSI segment is expected to witness a distinguished CAGR of 30.9% over the projected time period. Increasing use of data annotation in banking sector to automate manual documentation tasks will drive the segment growth.
The healthcare segment held an industry share of more than 20% in 2020 and is forecasted to witness similar growth over the coming time period. The forecast growth is accredited to the extensive use of data annotation for medical image labelling. Machine learning models as well as application enable vendors, payers and providers to utilize data to make much better decisions and enhance the overall healthcare outcomes.
The retail segment is likely to witness a remunerative CAGR of over 30.9% through the forecast time. This anticipated growth is attributed to the use of data annotation for shelf management, price checking, misplaced product detection, as well as eliminating inventory shrinkage.
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From a regional frame of reference, Latin America will grow at a respectable CAGR of around 31% over the forthcoming time period. Governments across Latin America have identified artificial intelligence as an important economic plan, investing heftily into the technology. Consequently, this factor is likely to favor the overall regional market growth.
Meanwhile, in 2020, the Middle East & Africa region accounted for a market share of more than 3.71% and will continue to exhibit strong growth patterns in coming years. Rapidly rising adoption of big data analytics by government agencies, and introduction of favorable initiatives that promote the economy's migration away from the existing oil-dependent setup.
Table of Contents
Chapter 1 Methodology & Scope
	
1.1 Scope & definitions
	
1.2 Methodology & forecast parameters
	
1.3 Region wise COVID-19 impact analysis	
		
1.3.1 North America
		
1.3.2 Europe
		
1.3.3 Asia Pacific
		
1.3.4 Latin America
		
1.3.5 Middle East & Africa
	
	
	
1.4 Regional trends
	
1.5 Data Sources	
		
1.5.1 Secondary
		
1.5.2 Primary
	
	
Chapter 2 Executive Summary
	
2.1 Data annotation tools industry 360 degree synopsis, 2016 - 2027
	
2.2 Business trends
	
2.3 Regional trends
	
2.4 Data type trends
	
2.5 Annotation approach trends
	
2.6 Application trends
Chapter 3 Data Annotation Tools Industry Insights
	
3.1 Introduction
	
3.2 Industry segmentation
	
3.3 Impact of COVID-19 outbreak	
		
3.3.1 Global outlook
		
3.3.2 By region		
			
3.3.2.1 North America
			
3.3.2.2 North America
			
3.3.2.3 Europe
			
3.3.2.4 Asia Pacific
			
3.3.2.5 Latin America
			
3.3.2.6 Middle East & Africa
		
		
		
3.3.3 Industry value chain		
			
3.3.3.1 Suppliers
			
3.3.3.2 Data annoation technology providers
			
3.3.3.3 Marketing and distribution channels
		
		
		
3.3.4 Compeitive landscape		
			
3.3.4.1 Strategy
			
3.3.4.2 Distribution network
			
3.3.4.3 Business growth
		
		
	
	
	
3.4 Evolution of data annotation tools
	
3.5 Data annotation tools industry architecture
	
3.6 Data annotation tools industry ecosystem analysis	
		
3.6.1 Data annotation software vendors
		
3.6.2 Cloud service providers
		
3.6.3 Distributors and resellers
		
3.6.4 Third party service providers
		
3.6.5 End-users
	
	
	
3.7 Investment portfolio
	
3.8 Patent analysis
	
3.9 Technology & innovation landscape	
		
3.9.1 Pseudo labelling
		
3.9.2 Online content moderation
	
	
	
3.10 Regulatory landscape	
		
3.10.1 North America		
			
3.10.1.1 NIST Special Publication 800-144 - Guidelines on Security and Privacy in Public Cloud Computing (U
			
3.10.1.2 Health Insurance Portability and Accountability Act (HIPAA) of 1996 (U
			
3.10.1.3 Personal Information Protection and Electronic Documents Act [(PIPEDA) Canada]
		
		
		
3.10.2 Europe		
			
3.10.2.1 General Data Protection Regulation (EU)
			
3.10.2.2 German Privacy Act (Bundesdatenschutzgesetz- BDSG)
		
		
		
3.10.3 APAC		
			
3.10.3.1 Information Security Technology- Personal Information Security Specification GB/T 35273-2017 (China)
			
3.10.3.2 Secure India National Digital Communications Policy 2018 - Draft (India)
		
		
		
3.10.4 Latin America		
			
3.10.4.1 National Directorate of Personal Data Protection (Argentina)
			
3.10.4.2 The Brazilian General Data Protection Law (LGPD)
		
		
		
3.10.5 MEA		
			
3.10.5.1 Law No. 13 of 2016 on protecting personal data (Qatar)
			
3.10.5.2 Federal Law No. 2 of 2019 on the use of ICT in Healthcare (UAE)
		
		
	
	
	
3.11 Industry impact forces	
		
3.11.1 Growth drivers		
			
3.11.1.1 Rising demand for annotated data to improve machine learning models
			
3.11.1.2 Increasing investments in the development of autonomous driving technologies
			
3.11.1.3 Growing adoption of data annotation for medical imaging data
			
3.11.1.4 Surging uptake of text annotation for document classification
		
		
		
3.11.2 Industry pitfalls & challenges		
			
3.11.2.1 Inaccurate data labelling due to poor content quality
			
3.11.2.2 Lack of skilled professionals
			
3.11.2.3 High costs associated with manual data annotation
		
		
	
	
	
3.12 Growth potential analysis
	
3.13 Porter's analysis
	
3.14 PESTEL analysis