According to the Regional Research Reports, the Global Data Labeling Software market size was valued at USD 1,668.7 million in 2021 and is estimated to grow to reach over USD 8,218.0 million by 2030 at a CAGR of 25.7 % over the forecast period (2022-2030).

Data Labelling Software Market Definition

Data labeling software—also known as training data, data annotation, data tagging, or data classification software—provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding artificial intelligence algorithms. Within these tools, the user inputs a given dataset, and the software provides a label through machine learning-assisted labeling, a human task force, or the user. Some platforms allow for the combination of the three, giving the user (or the system itself) the ability to choose who or what is doing the labeling based on factors such as price, quality, and speed.

Data labeling tools differ regarding the types of data (e.g., image, video, audio, and text) and the subsets of those types (e.g., satellite imagery, LIDAR, etc.) they support. The annotation types also vary, including image segmentation and object detection for image data, named entity recognition (NER) and sentiment detection for text data, and transcription and emotion recognition for speech annotation. Most tools use metrics like consensus, ground truth, and more to assess the quality of the labels.

This software can often integrate with data science and machine learning platforms, whereby the labeled data from the data labeling software helps to train an algorithm.

Data Labelling Software Market Pricing

The Data Labelling Software pricing is estimated to range from USD 100 to USD 150 per year. The pricing depends on the features and specifications integrated into the software. The software’s main features include emotional intelligence, conversational ability, broad knowledge base, personal, and personality.

Market Scope

The research study provides an in-depth analysis of the Data Labelling Software market, current market trends, and future estimations to elucidate the imminent investment pockets. Information about key drivers, restraints, and opportunities and their impact on the market size is provided. Porter’s five forces analysis illuminates the potency of suppliers and buyers operating in the market. The quantitative analysis of the Data Labelling Software market from 2022 to 2030 is provided to determine the market potential.

This report also contains the market size, untapped opportunity index, and forecasts of Data Labelling Software in the global market, including the following market information:

• Global Data Labelling Software Market Revenue, 2018-2021, 2022-2030, (USD Millions)
• Global Data Labelling Software Market Sales, 2018-2021, 2022-2030, (Units)
• Global top five Data Labelling Software companies in 2021 (%)

Regional Research Reports has surveyed the Data Labelling Software manufacturers, suppliers, distributors, and industry experts in this end-use industry, involving the consumption, production, revenue generators, demand-side, supply-side, price change, product type analysis, recent development and strategies, industry trends, drivers, challenges, obstacles, and potential risks.

Data Labelling Software Market Segmentation

Global Data Labelling Software Market, By Deployment Model, 2018-2021, 2022-2030 (USD Millions)

Global Data Labelling Software Market Segment Percentages, By Deployment Model, 2021 (%)

• On-Premise
• Cloud
• Hybrid

Global Data Labelling Software Market, By Component, 2018-2021, 2022-2030 (USD Millions)

Global Data Labelling Software Market Segment Percentages, By Component, 2021 (%)

• Solution
• Services

Global Data Labelling Software Market, By End User, 2018-2021, 2022-2030 (USD Millions)

Global Data Labelling Software Market Segment Percentages, By End User, 2021 (%)

• Small Business
• Mid Market
• Enterprise

Global Data Labelling Software Market, By Industry, 2018-2021, 2022-2030 (USD Millions)

Global Data Labelling Software Market Segment Percentages, By Industry, 2021 (%)

• BFSI
• Healthcare
• Energy & Utility
• IT & Telecommunication
• Retail & E-commerce
• Manufacturing
• Government & Defense
• Media & Entertainment
• Others

Global Data Labelling Software Market, By Region and Country, 2018-2021, 2022-2030 (USD Millions)

Global Data Labelling Software Market Segment Percentages, By Region and Country, 2021 (%)

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • France
  • The U.K.
  • Italy
  • Russia
  • Nordic Countries
  • Benelux
  • Rest of Europe
• Asia
  • China
  • Japan
  • South Korea
  • Southeast Asia
  • India
  • Rest of Asia
• South America
  • Brazil
  • Argentina
  • Rest of South America
• Middle East & Africa
  • Turkey
  • Israel
  • Saudi Arabia
  • UAE
  • Rest of the Middle East & Africa

Challenges with Data Labeling

The software can come with its own set of challenges. Data labelings, which are changing many industries and use cases (such as customer support and e-commerce), have some key issues which one should keep in mind.

Preference for human agents: Although Data labelings are great at many tasks, some contexts, such as those which require a significant amount of empathy, may be better served by a human agent.

Handoffs to humans: There might come a time when data labeling does not have an answer to a question from the user. The system must be designed in a way to resolve this problem successfully. Typically, the best way to solve this is to transition the user to a human agent.

Global Data Labelling Software Market Trend

In addition, artificial intelligence techniques such as NLP software help make data labeling solutions easier to use and more powerful, providing more accurate results. Below are the trends relevant to this software.

Conversational interfaces

In general, users are looking to conversational interfaces to get answers to their burning questions. For instance, they are looking to query their data more naturally. Since natural language understanding has improved, people can talk to their data, finding and exploring insights using natural, intuitive language. With this powerful technology, users can focus on discovering patterns and finding meaning hidden in the data instead of memorizing SQL queries.

Data-focused businesspeople, like data analysts, can benefit from conversational interfaces like Data labellings. Users can uncover the material they are looking for using intuitive language. Intuitive methods of querying data mean a larger user base that can access and make sense of company data.

Voice

Voice is a primal method of interacting with others. It is only natural that we now converse with our machines using our voice and that the platforms for said voicebots have seen great success. Voice makes technology feel more human and allows people to trust it more. Voice will be an important natural interface that mediates human communication, relationships with devices, and, ultimately, within an AI-powered world.

Artificial intelligence

AI is quickly becoming a promising feature of many, if not most, types of software. With machine learning, end users can identify data patterns, make sense of content, and help understand what they are seeing. This pattern recognition is fueling the rise of more powerful, contextually-aware Data labelings.

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Competitor Analysis of the Global Data Labelling Software Market

Analysis of leading market companies and participants, including:

• Key companies Data Labelling Software revenues in the global market, 2018-2021 (Estimated), (USD Millions)
• Key companies Data Labelling Software revenues share in global market, 2021 (%)
• Key companies Data Labelling Software sales in the global market, 2018-2021 (Estimated), (MT)
• Key companies Data Labelling Software sales share in global market, 2021 (%)

 

Further, the report detailed the leading competitors in the market, namely:

• Scale AI
• Snorkel AI
• Crowd AI
• Datasaur
• Sama
• Edgecase
• Africa AI
• Labelbox
• Playment
• SuperAnnotate
• Surge AI
• Cogito Tech
• CloudFactory
• Pure Moderation
• V7 Labs
• AI
• Alegion
• ai
• Ango AI
• Dataloop
• Appen
• Hive
• Clarifai
• Automaton AI
• Amazon Sagemaker Ground Truth