Global Data Annotation Tools Market Size & Analysis By Type (Text, Image), By Annotation Type (Manual, Semi-supervised, Automatic), By Vertical (IT, Automotive, Government, Healthcare, Financial Services, Retail), End-Use Landscape; Vendor Landscape, Company Market Share Anaylsis and Competitor Landscape - Forecasts to 2026 The document discusses the global data annotation tools market. It covers market drivers such as the rise of digital data in various formats like images and video that require annotation. Manual annotation is time-consuming so machine learning is being used more for annotation. The document also discusses applications of data annotation across industries like healthcare, automotive, government, and financial services

1. Global Data Annotation Tools Market Size & Analysis By Type (Text, Image), By Annotation Type
(Manual, Semi-supervised, Automatic), By Vertical (IT, Automotive, Government, Healthcare,
Financial Services, Retail), End-Use Landscape; Vendor Landscape, Company Market Share Anaylsis
and Competitor Landscape - Forecasts to 2026
Global Data Annotation Market Drivers
Advanced features of Data Annotations:
Data generation is taking place constantly across multiple verticals in a digital format. The types of
data have changed drastically over the years as well. A few years ago, majority of the data dealt with
was structured or textual. However with emergence of newer digital platforms has led to image and
video data, which now occupies the major share of data generated across verticals. With advent of
new technologies, offline information can be digitized at a cheaper cost and at a faster rate- which
calls for the need for management of these huge datasets. The available information in form of text
and images needs to be categorized, segregated and labelled efficiently in order to use it in practical
end use applications. Such categorization or annotation allows users easy access to correct data
which can be made sense out of. Manual annotation is possible, but is extremely time consuming,
expensive and has a higher risk of error. Especially for large datasets, annotation requires niche
expertise which is not readily available in manual annotation. Use of machine learning to annotate
data is therefore on the rise.
Neural networks can be used to build artificial intelligence powered applications owing to
development in hardware, which has enabled collection of large amounts of data and has provided
the computational power required to devise large models. According to experts, the major
bottleneck to development of artificial intelligence platforms is not availability or access to data, it is
labelling of data. There is a huge need for data labelling professionals and tools to keep up with the
amount of data generated every day. In March 2019, Appen Limited announced its acquisition of
Figure Eight Technologies. In June 2019, Innodata announced launch of its expertly managed data
annotation and labelling services. These two data annotation companies are collaborating to train
artificial intelligence and machine learning. Advent of big data and presence of numerous companies
working in the artificial intelligence industry is expected to boost the demand for annotation services
during the forecast period.
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Applications of data annotation in multiple end use industries:
Data annotation tools find applications across multiple end use industries such as information
technology, automotive, Government, healthcare, financial services and retail among others. In the
healthcare industry, artificial intelligence is used for various applications such as diagnostic
automation, treatment prediction, drug development and gene sequencing among others. In the
automotive segment, solutions are focused on high quality data, automation of the annotation
process and optimization of costs and time. Growing advent of artificial intelligence platforms is
further expected to boost growth in the market. Data labelling plays a crucial role in development of
machine learning models. Images and videos account for a significantly large share in the data
generated in all industries. These data can be used extensively in image and object classification,
development of advanced driver automation systems, objects tracking, LIDAR segmentation and

2. keypoint annotation among others. It is also used to detect poses of sports players for sports
analytics, facial features for face recognition and prediction of pedestrian motion for autonomous
vehicles among others. In the financial services industry, data annotation allows consumers to utilize
data in order to come up with smarter and smarter decisions more efficiently. The financial industry
is utilizing algorithms meant for machine learning to devise business strategies. It is also used in
financial risk management and to enhance consumer experience. Growing digitization, use of big
data and development of data annotation tools are further expected to fuel growth in the market.
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