Data Annotation and Labeling Market

Data Annotation and Labeling Market - Analysis, Industry Size & Forecast

The global Data Annotation and Labeling Market size was valued at USD 0.8 billion in 2022 and is expected to grow at a CAGR of 33.2% from 2022 to 2027. The revenue forecast for 2027 is projected to reach $3.6 billion. The base year for estimation is 2021, and the historical data spans from 2022 to 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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Scope of the Report

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.

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