Data Annotation and Labeling Market Component, Data Type, Application (Dataset Management, Sentiment Analysis), Annotation Type, Vertical (BFSI, IT and ITES, Healthcare and Life Sciences) and Region - Global Forecast to 2027
Updated on : March 6, 2023
Data Annotation and Labeling Market Analysis
The global data annotation and labeling market size was crossed USD 0.8 billion in 2022 and is anticipated to exhibit a CAGR of 33.2% to reach USD 3.6 billion by the end of 2027. When creating a machine learning model, preprocessing includes data labeling and annotation. To enable the machine learning model to generate precise predictions, it is necessary to identify raw data, such as images, text files, and videos, and then to add one or more labels to that data to explain its context for the models. To organize, clean up, and label data, businesses incorporate software, procedures, and data annotators. This training data becomes the foundation for ML models. These labels give analysts the ability to isolate certain variables inside datasets, allowing them to choose the best data predictors for ML models.
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Data Annotation and Labeling Market Dynamics
Driver: Rising popularity of labeled data in medical imaging
The deployment of Al-enabled systems for improved patient care, improved diagnostics, and accelerated drug discovery has changed the healthcare sector. Algorithms have been developed that can detect anomalies and illnesses in patients without the assistance of a human being using accurately labelled medical images. To create a database of precisely labelled operation videos, medical personnel also collaborate with expert annotation service providers. The dataset would act as the building block for the creation of autonomous surgical robots. Companies such as Labelbox supports dynamic large scale tiled imaging formats for medical practitioners to label pathological data. Another significant application area where data annotation is important is drug development. In order to help researchers detect patterns relevant to drug discovery, data annotation and labeling can assist automated systems in sorting through a massive volume of research papers, patents, clinical trial paperwork, and patient records. Such annotated datasets can be utilized to deduce novel connections between illnesses, symptoms, and potential treatments.
Restraint: Issues associated with poor quality of training data
One of the key barriers to the market expansion of data annotation and labeling solutions continues to be the lack of high-quality input data. Any attempts to train Al models with low-quality data result in errors in the predicted output, with certain algorithms degrading to the point where their complete optimization is never attained. This is because the performance of Al is strongly correlated with the quality of data input into the algorithm. Missing, irrelevant, or manipulated datasets can also cause drastic financial repercussions if there is a significant delta between ground truth and Al algorithm predictions.
Opportunity: Increasing traction of crowdsourced data annotation for improved RoI
In order to quickly annotate massive amounts of data, crowdsourced data annotation involves outsourcing the data labeling project to a number of freelance data annotators. By distributing data annotation tasks to thousands of data labelers at once, businesses attempt to speed up the time it takes to market their Al products, fueling a fervent demand for crowdsourced data annotation. The emergence of platforms for crowdsourced data labeling has boosted consumers' trust in crowdsourced data annotation. When compared to the costs associated with hiring professional data annotation experts, the data annotation can be finished for a fraction of the price.
Challenge: Dearth of skilled data annotators
The industry for data annotation and labeling has been significantly hampered by a lack of highly experienced manual data labelers and subject matter experts. Highly accurate training data that has been properly labelled is necessary for creating complex AI models for handling crucial applications, including self-driving cars and medical diagnosis solutions. This increases the demand for specialized annotators who can decipher the underlying meanings of intricate scientific or medical imagery. The lack of qualified data annotators also poses the risk of poor data quality if inexperienced data annotators have labeled them.
BFSI segment to account for largest market size during forecast period
The BFSI sector is using machine learning to enhance operations, boost revenue, and improve customer experience by utilizing the vast amount of data generated across many formats. Data annotation and labeling tools ensure that these machine learning models perform at optimum levels by offering robust data quality. Predictive analytics systems may be able to extract useful and applicable insights from unstructured data by labeling critical consumer information, such as customer loan application data, insurance claims, and KYC forms. Among the verticals, the BFSI segment is anticipated to register the largest market size during the forecast period.
Automatic segment to register to grow at the highest CAGR during forecast period
The traditional data labeling procedure was time consuming and wholly manual. Humans have a low productivity rate and are prone to mistakes despite the high accuracy rate of their annotations. For computer vision and natural language processing activities, the demand for precise and high-quality data labelling is rising. Any unstructured customer data or content can be automatically labeled to discover segments of customers with similar combinations of attributes and treat them similarly in marketing efforts. Businesses can use automatic data labeling, which requires little to no human intervention, to significantly reduce operating costs and time. During the forecast period, automatic segment is anticipated to grow at the CAGR.
North America to account for largest market size during forecast period
By region, North America is estimated to lead the data annotation and labeling market during the forecast period. The region is one of the early adopters of data annotation and labeling solution, since the majority of large enterprises are located in this region. One of the biggest key drivers in North America is the substantial investments made by numerous businesses for outsourcing the data annotation and labelling solutions. Among the countries in these region, the United States have emerged as the main markets witnessing increased demand for affordable data annotation services and machine learning models.
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Data Annotation and Labeling Market - Key Players
The data annotation and labeling providers have implemented various organic and inorganic growth strategies, such as new product launches, product upgrades, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major players in the data annotation and labeling market include Google (US), Appen (Australia), IBM (US), Oracle (US), TELUS International (Canada), Adobe (US), AWS (US), Alegion (US), Cogito Tech (US), Anolytics (US), AI Data Innovation (US), Clickworker (Germany), CloudFactory (UK), CapeStart (US), DataPure (US), LXT (Canada), Precise BPO Solution (India), Sigma (US), Segment.ai (US), Defined.ai (US), Dataloop (Israel), Labelbox (US), V7 (UK), LightTag (Germany), SuperAnnotate (US), Scale (US), Datasur (US), Kili Technology (France), Understand.ai (Germany), Keylabs (Israel), and Label Your Data (US).
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Report Metrics |
Details |
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Market size available for years |
2019–2027 |
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Base year considered |
2021 |
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Forecast period |
2022–2027 |
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Forecast units |
USD (Billion) |
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Segments covered |
Component, Data Type, Application, Deployment Type, Organization Size, Annotation Type, Vertical, and Region |
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Geographies covered |
North America, Europe, Asia Pacific, Middle East and Africa, and Latin America |
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Companies covered |
Google (US), Appen (Australia), IBM (US), Oracle (US), TELUS International (Canada), Adobe (US), AWS (US), Alegion (US), Cogito Tech (US), Anolytics (US), AI Data Innovation (US), Clickworker (Germany), CloudFactory (UK), CapeStart (US), DataPure (US), LXT (Canada), Precise BPO Solution (India), Sigma (US), Segment.ai (US), Defined.ai (US), Dataloop (Israel), Labelbox (US), V7 (UK), LightTag (Germany), SuperAnnotate (US), Scale (US), Datasur (US), Kili Technology (France), Understand.ai (Germany), Keylabs (Israel), and Label Your Data (US). |
This research report categorizes the data annotation and labeling market based on component, data type, application, deployment type, organization size, annotation type, vertical, and region.
By Component
- Solution
- Services
By Data Type:
- Text
- Image
- Video
- Audio
By Deployment Type:
- On-premises
- Cloud
By Organization Size:
- Large enterprises
- SMEs
By Annotation Type:
- Manual
- Automatic
- Semi-Supervised
By Application:
- Dataset Management
- Security and Compliance
- Data Quality Control
- Workforce Management
- Content Management
- Catalogue Management
- Sentiment Analysis
- Other Applications
By Verticals:
- BFSI
- IT and ITES
- Healthcare & Lifescience
- Telecom
- Government, Defense and Public Agencies
- Retail and Consumer Goods
- Automotive
- Other Verticals
By Region:
-
North America
- US
- Canada
-
Europe
- UK
- Germany
- France
- Rest of Europe
-
Asia Pacific
- China
- Japan
- India
- Rest of Asia Pacific
-
Middle East and Africa
- Saudi Arabia
- UAE
- South Africa
- Rest of Middle East and Africa
-
Latin America
- Brazil
- Mexico
- Rest of Latin America
Recent Developments in Data Annotation and Labeling Market:
- In November 2022, TechSee had partnered with TELUS International to promote real-time computer vision in engagement centres. Through this partnership, TechSee's portfolio of AI-powered service automation and visual engagement technologies would be added to TELUS International's customer base.
- In October 2022, Accenture and Google Cloud today announced an expansion of their global partnership through a renewed commitment to growing their respective talent, increasing their joint capabilities, developing new solutions using data and AI, and providing enhanced support to help clients build a strong digital core and reinvent their enterprises on the cloud.
- In October 2022, Appen had collaborated with Novatics and offer shared synergies in the Latin American region to expand client offerings. This collaboration is another step in Appen's strategy to provide inclusive data for the AI lifecycle. As part of the collaboration, Novatics will be connecting Appen with key strategic clients in the Latin America region.
- In May 2022, Oracle and Informatica had partnered for data governance, enterprise cloud data connectivity, and lakehouse solutions on Oracle Cloud Infrastructure. This partnership would enable to deliver industry-leading cloud data management, integration, and governance solutions for databases, data warehouses, data lakes, data lakehouses, enterprise analytics, and data science.
- In November 2021, AWS and Goldman Sachs collaborated to create new Data Management and Analytics solutions for financial services organizations.
- In August 2021, Appen had announced that it had signed an agreement to acquire Quadrant. This acquisition of Quadrant and Appen would be well-positioned to provide high-quality data to businesses that depend on geolocation.
- In July 2021, TELUS International had announced to acquire Bangaluru based data annotation startup named Playment. With this acquisition, TELUS International can assist technology companies and large businesses in the development of AI-powered solutions, from enhancing the customer experience for current customers to opening new opportunities for the development of computer vision-powered applications across industries.
- In November 2020, TELUS International had announced the acquisition of Lionbridge with an intent to improve its artificial intelligence capabilities in response to the rising need for high-quality, multilingual data annotation.
Frequently Asked Questions (FAQ):
What is Data Annotation and Labeling?
Data annotation is the process of identifying raw data and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car and which words were uttered in an audio recording. Data labeling is required for various use cases, including computer vision, natural language processing, and speech recognition.
Which countries are considered in the European region?
The report includes an analysis of the market in the UK, Germany, and France.
Which are the key deployment types adopting data annotation and labeling?
The key deployment types adopting data annotation and labeling are cloud and on-premises.
Which are the key drivers supporting the growth of the data annotation and labeling market?
The key drivers supporting the growth of the data annotation and labeling market include the increasing need to improve machine learning models and train AI algorithms, growing demand for annotated datasets in autonomous mobility technologies, and rising popularity of labeled data in medical imaging.
Who are the key vendors in the data annotation and labeling market?
The key players in the data annotation and labeling market include Google (US), Appen (Australia), IBM (US), Oracle (US), TELUS International (Canada), Adobe (US), AWS (US), Alegion (US), Cogito Tech (US), Anolytics (US), AI Data Innovation (US), Clickworker (Germany), CloudFactory (UK), CapeStart (US), DataPure (US), LXT (Canada), Precise BPO Solution (India), Sigma (US), Segment.ai (US), Defined.ai (US), Dataloop (Israel), Labelbox (US), V7 (UK), LightTag (Germany), SuperAnnotate (US), Scale (US), Datasur (US), Kili Technology (France), Understand.ai (Germany), Keylabs (Israel), and Label Your Data (US).
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The research study for the Data annotation and labeling market involved extensive secondary sources, directories, journals, and paid databases. Primary sources were industry experts from the core and related industries, preferred data annotation and labeling providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information and assess the market’s prospects.
Secondary Research
The market size of companies offering data annotation and labeling, and services was arrived at based on secondary data available through paid and unpaid sources. It was also arrived at by analyzing major companies' product portfolios and rating them based on their performance and quality. In the secondary research process, various sources were referred to for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendors' websites. Additionally, Data annotation and labeling spending of various countries was extracted from the respective sources. Secondary research was mainly used to obtain key information related to the industry’s value chain and supply chain to identify key players based on solutions, services, market classification, and segmentation according to offerings of major players, industry trends related to solutions, services, deployment modes, functionality, applications, verticals, and regions, and key developments from both market and technology-oriented perspectives.
Primary Research
In the primary research process, various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and Data annotation and labeling expertise; related key executives from Data annotation and labeling solution vendors, SIs, professional service providers, and industry associations; and key opinion leaders.
Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using data annotation and labeling solutions, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of data annotation and labeling solutions and services, which would impact the overall data annotation and labeling market.
The following is the breakup of primary profiles:
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Data Annotation and Labeling Market Size Estimation
In the market engineering process, the top-down and bottom-up approaches were used along with multiple data triangulation methods to estimate and validate the size of the data annotation and labeling market and other dependent submarkets. Key market players were identified through secondary research, and their market share in the targeted regions was determined with the help of primary and secondary research. This research methodology included the study of annual and financial presentations of the top market players and interviews with experts for key insights (quantitative and qualitative).
The percentage share splits, and breakdowns were determined using secondary sources and verified through primary research. All possible parameters that affect the data annotation and labeling market were verified in detail with the help of primary sources and analyzed to obtain quantitative and qualitative data. This data was supplemented with detailed inputs and analysis from MarketsandMarkets and presented in the report.
- The pricing trend is assumed to vary over time.
- All the forecasts were made with the standard assumption that the accepted currency is USD.
- For the conversion of various currencies to USD, average historical exchange rates were used according to the year specified. For all the historical and current exchange rates required for calculations and currency conversions, the US Internal Revenue Service's website was used.
- All the forecasts were made under the standard assumption that the globally accepted currency USD will remain constant during the next five years.
- Vendor-side analysis: The market size estimates of associated solutions and services were factored in from the vendor side by assuming an average of licensing and subscription-based models of leading and innovative vendors in the market.
- Demand/end-user analysis: End users operating in verticals across regions were analyzed in terms of market spending on data annotation and labeling based on some of the key use cases. These factors for the data annotation and labeling industry per region were separately analyzed, and the average spending was extrapolated with an approximation based on assumed weightage. This factor was derived by averaging various market influencers, including recent developments, regulations, mergers and acquisitions, enterprise/SME adoption, startup ecosystem, IT spending, technology propensity and maturity, use cases, and the estimated number of organizations per region.
Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Report Objectives
- To define, describe, and predict the data annotation and labeling market by component (solutions and services), deployment mode, data type, applications, annotation type, organization size, verticals, and region
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
- To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
- To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the data annotation and labeling market
- To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
- To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, Middle East and Africa, and Latin America
- To profile key players and comprehensively analyze their market rankings and core competencies
- To analyze competitive developments, such as partnerships, new product launches, and mergers and acquisitions, in the data annotation and labeling market
- To analyze the impact of recession across all the regions across the data annotation and labeling market
Available Customizations
With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:
Product Analysis
- Product matrix provides a detailed comparison of the product portfolio of each company
Geographic Analysis
- Further breakup of the North American data annotation and labeling market
- Further breakup of the European market
- Further breakup of the Asia Pacific market
- Further breakup of the Latin American market
- Further breakup of the Middle Eastern and African market
Company Information
- Detailed analysis and profiling of additional market players (up to 5)
Growth opportunities and latent adjacency in Data Annotation and Labeling Market
There are several business prospects in the fast-expanding sector of data annotation and labeling. The following are a few of the most promising opportunities:
What are the top 6 business opportunities in Data Annotation and Labeling?