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市場調査レポート
商品コード
1335879
データラベリングソリューションとサービスの世界市場規模、シェア、産業動向分析レポート:タイプ別、ラベリングタイプ別(手動、半監視、自動)、ソーシングタイプ別、業界別、地域別展望と予測、2023年~2030年Global Data Labeling Solution And Services Market Size, Share & Industry Trends Analysis Report By Type, By Labeling Type (Manual, Semi-Supervised, and Automatic), By Sourcing Type, By Vertical, By Regional Outlook and Forecast, 2023 - 2030 |
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データラベリングソリューションとサービスの世界市場規模、シェア、産業動向分析レポート:タイプ別、ラベリングタイプ別(手動、半監視、自動)、ソーシングタイプ別、業界別、地域別展望と予測、2023年~2030年 |
出版日: 2023年07月31日
発行: KBV Research
ページ情報: 英文 305 Pages
納期: 即納可能
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データラベリングソリューションとサービス市場規模は2030年までに469億米ドルに達すると予測され、予測期間中のCAGRは19.5%の市場成長率で上昇する見込みです。
KBV Cardinalのマトリックスに掲載された分析によると、Google LLC(Alphabet Inc.)が同市場におけるトップランナーです。Appen Limited、TELUS International(Playment, Inc.)、Alegion, Inc.などの企業は、市場における主要なイノベーターです。例えば、2021年5月、Cogitoは病理学、眼科学、心臓病学の機能を拡張しました。ヘルスケアにおけるAIの採用には、ヘルスケアにおける正確な注釈データのための専門知識が必要です。
市場成長要因
ヘルスケア画像処理におけるラベル付きデータの利用の増加
ヘルスケア産業は、より良い患者ケア、より迅速な診断、より早期の薬物発見のために、Al-enabledシステムを採用することで成長しています。適切にラベル付けされた医療画像の助けを借りて、人の手を借りずに患者の障害や病気を特定できるアルゴリズムが構築されています。医療スタッフはまた、知識豊富なデータラベリングソリューションやサービスプロバイダーと協力して、正確にラベル付けされた手術映像のデータベースを作成しています。このデータセットは、自律型手術ロボットを開発する際の基本的な要素となります。ヘルスケアにおけるデータラベリングソリューション・サービスのこのような幅広い利用が、予測期間中の市場拡大をサポートすると推定されます。
さまざまな産業でデジタル化が進む
この市場は、デジタル化の採用により大きく成長しています。デジタル化の結果、多くの産業でデータ収集が大幅に増加しています。デジタルプラットフォーム、ソーシャルメディア、オンラインコミュニケーションの成長により、膨大な量のテキストデータが分析可能になっています。このような情報の意味を理解し、有用な洞察を得るために、効果的なデータ・ラベリング・サービスが必要とされています。機械学習は膨大な量のデータを定期的に使用するため、企業は従業員にデータエンリッチメントのための適切なツールやトレーニングを提供するために時間と資金を投資しています。デジタル・プラットフォームの利用が広まるにつれて、データ・ラベリング・ソリューションとサービスの市場は支持を集めると予想されます。
市場抑制要因
低品質なトレーニングデータが引き起こす問題
高品質な入力データの不足は、データラベリングソリューションおよびサービスの市場成長を妨げる主な要因の1つです。アル・モデルのトレーニングに質の低いデータを使おうとすると、期待される結果が不正確になり、ある種の技術は完全に最適化されないまま劣化してしまいます。これは、アルゴリズムに投入するデータの精度が、その性能と高い相関関係があるためです。データの精度は、ヘルスケアのような規制の厳しい業界にとって不可欠です。最近のパンデミックの結果、今後のパンデミックに備えたデータ品質の向上はこれまで以上に重要になっています。その結果、データラベリングソリューションとサービスの市場は、これらの要因によって制限されると予測されます。
タイプ別展望
タイプ別に見ると、市場はテキスト、画像・動画、音声に分類されます。2022年の市場では、テキストセグメントが大きな収益シェアを獲得しました。テキストデータとは、文書、記事、チャットログ、ソーシャルメディアへの投稿、カスタマーレビュー、電子メールなど、書き留められたあらゆる情報を指します。企業が膨大なテキストデータをマイニングして洞察に満ちた情報を得るために、機械学習や自然言語処理技術への依存度を高めているため、テキストデータのラベリング分野は大きく成長しています。
ラベリングタイプの展望
ラベリングタイプ別に見ると、市場は手動、半教師付き、自動に細分化されます。2022年には、手動セグメントがこの市場で最も高い収益シェアを記録しました。手動によるデータの分類やラベリングのプロセスには人間が関与します。この方法は、高い完全性、一貫性、最小限のデータ注釈作業などの利点があるため、自動ラベリングと比較して魅力的です。手作業によるラベリングは、公開データセットや合成データセットに含まれるエッジ・インスタンスやニッチ・セクター/産業を扱う際に、不適切または不十分である場合に不可欠です。
ソーシングタイプの展望
ソーシングの種類によって、市場はインハウスとアウトソーシングに区分されます。インハウス・セグメントは2022年の市場でかなりの収益シェアを獲得しました。企業は、社内データラベリングソリューションを導入することで、信頼できるラベリング手順と再現可能なデータ管理システムを開発することができます。クライアントの用途や要件に応じて、ベンダーは特化したソリューションも提供しています。また、社内にラベリングチームを設置することで、運用プロセスの理解と管理が容易になり、組織の観点からも有利となります。
業界別展望
業界別では、IT、自動車、政府、ヘルスケア、金融サービス、小売、その他に分類されます。2022年には、IT分野が市場で最大の収益シェアを占めました。AIアプリケーションの広範な利用が、このセグメントの成長に本質的に寄与しています。世界のIT分野での革新と最先端技術の採用の増加により、市場はさらにこの分野で成長すると予測されます。
地域別展望
地域別に見ると、市場は北米、欧州、アジア太平洋、LAMEAで分析されます。2022年には、北米地域が市場で最も高い収益シェアを獲得しました。この地域では、データラベリングソリューションへの投資が拡大しており、市場拡大を牽引しています。カナダと米国は、北米地域におけるAIの早期導入国であり、データラベリングソリューションとサービスの最先端を走っています。現代の調査需要により、企業は強力なバーチャル機能を搭載せざるを得なくなり、こうしたサービスの利用が拡大しています。
List of Figures
The Global Data Labeling Solution and Services Market size is expected to reach $46.9 billion by 2030, rising at a market growth of 19.5% CAGR during the forecast period.
The adoption of data labeling solutions and services technology and medical imaging techniques for the early & precise diagnosis of diseases is expected to lead to more data collection. Therefore, the healthcare segment has acquired $1779.8 million revenue in the market in 2022. Several market participants are launching strategic endeavors to create a strong artificial intelligence network by outsourcing data labeling solutions and services. Solutions based on artificial intelligence can be trained to recognize marked and labeled data. Common information sources include medical images, X-rays, CT scan images, and magnetic resonance imaging. Data solution labels and services are crucial in healthcare since medical imaging uses computer vision technology to recognize patterns and identify illnesses and injuries.
The major strategies followed by the market participants are Product Launches as the key developmental strategy to keep pace with the changing demands of end users. In June, 2022, Google introduced a dedicated server for AI system training along with example-based explanations into its Vertex to expedite the adoption of machine learning models in businesses. Additionally, In February, 2023, Appen Limited launched Automated NLP Labeling, Reinforcement Learning with Human Feedback, and Document Intelligence.
Based on the Analysis presented in the KBV Cardinal matrix; Google LLC (Alphabet Inc.) is the forerunner in the Market. Companies such as Appen Limited, TELUS International (Playment, Inc.) and Alegion, Inc. are some of the key innovators in the Market. For instance, In May, 2021, Cogito expanded its capabilities in Pathology, Ophthalmology & Cardiology. The adoption of AI in healthcare requires expertise for accurately annotated data in healthcare.
Market Growth Factors
Increasing Use of Labeled Data in Healthcare Imaging
The healthcare industry is growing due to adopting Al-enabled systems for better patient care, faster diagnostics, and earlier medication discovery. With the help of adequately labeled medical images, algorithms have been built that can identify patients' disorders and illnesses without a human's aid. Medical staff also work with knowledgeable data labeling solutions and service providers to compile a database of precisely labeled operation videos. The dataset would serve as a fundamental component in developing autonomous surgical robots. Such wide usage of data labelling solutions and services in healthcare is estimated to support the expansion of the market during the projection period.
Increased Digitalization Across Various Industries
This market is growing significantly due to the adoption of digitalization. Data collection has increased significantly across numerous industries as a result of digitalization. An enormous amount of text data is available for analysis because of the growth of digital platforms, social media, and online communication. There is a need for effective data labeling services to make sense of the information and gain useful insights. Machine learning uses enormous volumes of data regularly, and firms invest time and money in giving employees the proper tools and training for data enrichment. It is anticipated that the market for data labeling solutions and services will gain support as the usage of digital platforms becomes widespread.
Market Restraining Factors
Problems Caused by Low-Quality Training Data
The shortage of high-quality input data continues to be one of the main obstacles to the market growth of data labeling solutions and services. Every attempt to use poor-quality data for training Al models leads to inaccuracies in the anticipated results, with certain techniques deteriorating to the point that they are never fully optimized. This is because the accuracy of the data used to feed the algorithms is highly correlated with their performance. Data accuracy is essential for industries with strict regulations, such as healthcare. As a result of the recent pandemic, increasing the data quality for upcoming pandemics is more important than ever. As a result, it is projected that the market for data labeling solutions and services will be restricted due to the these factor.
Type Outlook
Based on type, the market is classified into text, image/video, and audio. The text segment garnered a significant revenue share in the market in 2022. Text data refers to any information written down, such as documents, articles, chat logs, social media posts, customer reviews, emails, etc. As enterprises increasingly rely on machine learning and natural language processing technologies for mining vast amounts of textual data for insightful information, the text data labeling segment has grown significantly.
Labeling Type Outlook
By labeling type, the market is fragmented into manual, semi-supervised, and automatic. In 2022, the manual segment registered the highest revenue share in this market. The process of manually classifying or labeling any data involves humans. The method is fascinating compared to automatic labeling because of its advantages, including high integrity, consistency, and minimal data annotation effort. Manual labeling is essential when working with edge instances or niche sectors/industries in public or synthetic datasets is inadequate or insufficient.
Sourcing Type Outlook
On the basis of sourcing type, the market is segmented into in-house, and outsourced. The in-house segment acquired a substantial revenue share in the market in 2022. Businesses may develop trustworthy labeling procedures and a replicable data management system by implementing their in-house data labeling solutions. Per the clients' applications and requirements, the vendors also provide specialized solutions. Additionally, setting up in-house labeling teams offers a better understanding and management of operational processes, which is advantageous from the organization's point of view.
Vertical Outlook
On the basis of vertical, the market is categorized into IT, automotive, government, healthcare, financial services, retails, and others. In 2022, the IT segment witnessed the largest revenue share in the market. The industry's extensive use of AI applications essentially contributes to the growth in this segment. The market is further predicted to grow in the segment with the growing innovations and adoption of cutting-edge technologies within the IT sector globally.
Regional Outlook
Region wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. In 2022, the North America region generated the highest revenue share in the market. This region's growing investment in data labeling solutions is driving market expansion. Canada and the U.S. were early adopters of AI in the North American region and are on the cutting edge of data labeling solutions and services. Modern research demands have compelled businesses to include strong virtual capabilities, which have expanded the use of these services.
The market research report covers the analysis of key stakeholders of the market. Key companies profiled in the report include Google LLC (Alphabet Inc.), Appen Limited, TELUS International (Playment, Inc.), Yandex N.V., Uber Technologies, Inc. (Mighty AI, Inc.), Zight, Alegion, Inc., Scale AI, Inc., Labelbox, Inc., Cogito Tech LLC
Recent Strategies Deployed in Data Labeling Solution and Services Market
Partnerships, Collaborations & Agreements:
Mar-2022: Labelbox, Inc. signed an agreement with Hitachi Solutions Co., Ltd., a core IT company of the Hitachi Group which provides IT solutions. Through this agreement, both companies get data labeling tools that help the creation of learning data for AI development and begin selling.
May-2021: Labelbox teamed up with Databricks, an enterprise software company. Together, the companies announced the features for teams to develop unstructured data for AI and analytics in Databricks. By integrating Databricks and Labelbox, users get an end-to-end surrounding for unstructured data workflows, a query engine developed around Delta Lake, quick annotation tools, and a strong Machine Learning computing environment.
May-2021: Alegion, Inc. signed an agreement with Yayasan Peneraju Pendidikan Bumiputera, a Malaysian government agency. This agreement was signed to address the quickly emerging technologies of artificial intelligence (AI) and machine learning (ML) by offering training and certification in ML data labeling.
Feb-2021: Google Cloud formed a partnership with NextBillion AI, an industry-leading startup in mapping platforms. The partnership aims to enhance time-to-market for hyperlocal AI solutions by operating datasets & algorithms on Cloud Storage & Cloud SQL to reduce the operational overheads with Google Kubernetes Engine
Product Launches and Product Expansions:
Feb-2023: Appen Limited launched Automated NLP Labeling, Reinforcement Learning with Human Feedback, and Document Intelligence. The launched products would leverage generative AI capabilities and zero shots learning techniques to rate up data annotation. Additionally, the product would unlock generative AI and strengthen exceptional customer experiences.
Jun-2022: Google introduced a dedicated server for AI system training along with example-based explanations into its Vertex. This product expansion aimed to expedite the adoption of machine learning models in businesses. In addition, the company also aimed to democratize AI To allow more people to deploy models in production, continuously monitor, and drive business impact with AI.
Jun-2022: Google rolled out Imagen, a text-to-image AI model. The new product aimed to generate photorealistic images of text and is pre-trained on text data. In addition, the new solution also outperforms DALL-E 2 on the COCO benchmark.
Oct-2021: Scale AI Inc. announced the launch of Scale Rapid, a service that aims to solve the problem by labeling a data sample within one to three hours. With this launch, users would be able to ensure the labeling is being done correctly, recapitulate upon their labeling instruction if important, then ramp up to have Scale AI label all of their datasets.
May-2021: Cogito expanded its capabilities in Pathology, Ophthalmology & Cardiology. The adoption of AI in healthcare requires expertise for accurately annotated data in healthcare.
Feb-2021: Appen Limited launched the latest off-the-shelf (OTS) datasets. These datasets are developed to make it simpler and quicker for companies to get the high-quality training data required to boost their artificial intelligence (AI) and machine learning (ML) projects.
Acquisitions and Mergers:
Aug-2021: Appen Limited agreed to acquire Quadrant, a global leader in mobile location data, Point-of-Interest data, and corresponding compliance services. This acquisition aimed to strengthen Appen's position in the market and also enable the company to provide high-quality data to companies that depend on geolocation for their business.
Jul-2021: TELUS International took over Playment, a complete data labeling platform. Through this acquisition, Playment would enhance TELUS' deep domain expertise and uniquely position it to support customers in developing AI-powered solutions across verticals.
Mar-2021: TELUS International took over Lionbridge AI, a leading and global provider of scalable data annotation services for text, images, videos, and audio. This acquisition aimed to expand TELUS International's global service offerings and penetration into the fast-growing economy services market under its digital transformation strategy.
Market Segments covered in the Report:
By Type
By Labeling Type
By Sourcing Type
By Vertical
By Geography
Companies Profiled
Unique Offerings from KBV Research