시장보고서
상품코드
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세계의 DataOps 플랫폼 시장 규모, 점유율, 산업 동향 분석 보고서 : 전개별, 서비스 모형별, 컴포넌트별, 업계별, 지역별 전망(2023-2030년)

Global DataOps Platform Market Size, Share & Industry Trends Analysis Report By Deployment, By Service Model (Agile Development, DevOps and Lean Manufacturing), By Component, By Vertical, By Regional Outlook and Forecast, 2023 - 2030

발행일: | 리서치사: KBV Research | 페이지 정보: 영문 347 Pages | 배송안내 : 즉시배송


※ 본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문 목차를 참고해주시기 바랍니다.

세계의 DataOps 플랫폼(DataOps Platform) 시장 규모는 2030년 146억 달러 규모에 달할 것으로 예측되며, 예측 기간 중 21.2%의 CAGR 성장률로 상승할 전망입니다.

시장 성장 요인

데이터 복잡성과 양 증가

조직은 구조화 및 비구조화 형식, 실시간 데이터 스트림 등 많은 소스 계속 증가하는 데이터를 처리해야 합니다. 데이터 양, 속도 및 다양성으로 인해 기존 데이터 관리 기술을 따라잡기가 어려워지고 데이터 처리 및 분석 비효율성, 실수 및 지연이 자주 발생합니다. 조직은 이러한 복잡성을 처리하는 데 필요한 도구와 기술을 제공하는 DataOps 플랫폼 도움으로 데이터를 효율적으로 통합, 처리 및 분석할 수 있습니다. 데이터 운영을 위한 플랫폼은 일괄 처리와 실시간 데이터 스트리밍을 모두 가능하게 합니다. 이러한 요인이 시장 성장을 뒷받침하고 있습니다.

클라우드 네이티브 DataOps 채택 증가

클라우드 네이티브 DataOps는 DataOps 플랫폼 시은장 여전히 비교적 새로운 개척에도 불구하고 데이터 운영 현대화를 목표로 하는 기업이 늘어남에 따라 급속히 여러 접근법을 추진하고 있습니다. 클라우드 컴퓨팅 플랫폼 데이터 파이프라인과 프로세스를 생성하고 배포하는 것은 이 전략의 기초입니다. 클라우드 네이티브 DataOps 주요 장점 중 하나는 확장성입니다. 클라우드 기술은 리소스 신속한 확장을 가능하게 하므로 조직은 인프라 제약에 신경 쓰지 않고 대량 데이터를 관리할 수 있습니다. 이를 통해 다양한 최종 사용자가 DataOps 플랫폼을 활용할 수 있으며 시장 확대가 촉진될 것으로 예측됩니다.

시장 성장 억제 요인

인력 부족이 초래하는 과제를 해결할 필요성

보다 역량이 높은 전문가 필요성은 시장의 가장 큰 장애 중 하나입니다. DataOps 플랫폼에는 데이터 과학, 데이터 엔지니어링, 소프트웨어 개발 및 운영 전문가가 필요합니다. 그러나 이러한 특정 기술을 가진 전문가 심각한 부족으로 인력 격차가 발생합니다. 그 결과 기업은 DataOps 플랫폼을 설계, 구현 및 유지 관리하는 유능한 후보자를 찾는 데 어려움을 겪고 있습니다. 게다가 기술 개발 급속한 속도가 이 인재 격차를 악화시키고 있습니다. 결과적으로 기존 팀 구성원은 DataOps 기술에 익숙해지기 위해 추가 교육 및 재교육이 필요할 수 있습니다.

구성 요소 전망

구성 요소에 따라 시장은 플랫폼과 서비스로 구분됩니다. 플랫폼 부문은 2022년 수익 점유율 중 최대가 되었고 시장을 독점했습니다. 이 부문은 비효율적인 데이터 생산 및 처리, 실수 및 불일치로 인한 데이터 품질 저하와 같은 문제를 해결하는 데 사용됩니다. 이 플랫폼은 데이터 라이프타임을 통해 통합 및 최적화된 데이터 큐레이션, 거버넌스, 관리, 프로비저닝을 위한 민첩한 소프트웨어 액세스를 공급망의 모든 사람에게 제공합니다.

서비스 모형 전망

서비스 모형을 기반으로 시장은 민첩한 개발, DevOps, 생산으로 나뉩니다. DevOps 부문은 2022년 시장에서 큰 수익 점유율을 기록했습니다. 이는 DataOps가 DevOps 도구를 사용하여 데이터 전망을 생산용 출력으로 변환하기 때문입니다. 또한 실시간 모니터링은 데이터 파이프라인 최적화를 지원하는 이러한 기술의 특징입니다. 또한 DevOps 원칙은 사용자와 비즈니스 팀 제공하는 입력을 원활하게 구현하는 데 도움이 됩니다. 따라서 DevOps 이러한 특징은 기간 동안 부문 확장이 급증할 것으로 예상됩니다.

전개 전망

배포를 통해 시장은 클라우드와 온프레미스로 분류됩니다. 클라우드 부문은 2022년 동 시장 중 가장 큰 수익 점유율을 차지합니다. 이 부문은 고객이 인터넷 연결을 통해 어디서나 DataOps 플랫폼에 액세스할 수 있고 유연성과 접근성이 향상되는 클라우드 배포를 채택할 전망입니다. 기업은 접근성 향상을 통해 물리적 위치에 제약 없이 플랫폼 기능을 활용할 수 있습니다.

클라우드 기반 전망

클라우드 유형에 따라 시장은 퍼블릭 클라우드, 프라이빗 클라우드, 하이브리드 클라우드로 나뉩니다. 2022년 매출 점유율은 퍼블릭 클라우드가 가장 큰 시장을 선도하고 있습니다. 이는 클라우드 컴퓨팅 도입 형태로 가장 널리 사용되는 것이 퍼블릭 클라우드이기 때문입니다. 퍼블릭 클라우드 제공업체는 하드웨어, 소프트웨어 및 기타 지원 인프라를 소유하고 관리합니다. 퍼블릭 클라우드는 사용자가 서비스 비용을 지불하기만 하면 하드웨어나 소프트웨어를 구입할 필요가 없습니다. 퍼블릭 클라우드는 유지보수가 필요하지 않습니다. 또한 비즈니스 요구에 맞게 온 디맨드 리소스를 활용할 수 있으므로 거의 무한한 확장성을 얻을 수 있습니다.

업계별 전망

업계별로는 BFSI, 헬스케어, 치과, 소매, 제조, 정부, IT 및 통신, 에너지 및 유틸리티, 미디어 및 엔터테인먼트 등으로 분류됩니다. 2022년 시장은 IT 및 통신 분야가 큰 수익 점유율을 기록했습니다. 이는 대규모 사업을 전개하면서 탁월한 차별화된 소비자 경험을 제공하기 위해서는 데이터 빠르고 안전한 액세스가 필수적이기 때문입니다. 또한, 통신사업자는 DevOps 데이터 플랫폼을 도입함으로써 개인 고객 데이터를 보호하면서 클라우드 마이그레이션 등 프로젝트를 위해 기업 데이터를 안전하고 효율적으로 공급할 수 있습니다.

지역별 전망

지역별로 시장은 북미, 유럽, 아시아태평양, LAMEA로 구분됩니다. 북미는 2022년 가장 큰 수익 점유율을 얻어 시장을 선도했습니다. 이는 이 지역의 활발한 기술 부문과 혁신, 디지털 전환에 흔들리지 않는 헌신 때문입니다. 북미 DataOps 플랫폼이 널리 사용되는 배경은 혁신과 디지털 변환에 흔들리지 않는 점이 있습니다. 북미 기업들은 혁신을 촉진하고 경쟁력을 얻기 위한 새로운 전략을 끊임없이 찾고 있습니다.

목차

제1장 시장 범위와 조사 방법

  • 시장 정의
  • 목적
  • 시장 범위
  • 세분화
  • 조사 방법

제2장 시장 주요 요약

  • 소개
    • 주요 요약
      • 시장 구성과 시나리오
  • 시장에 영향을 미치는 주요 요인
    • 시장 성장 촉진 요인
    • 시장 성장 억제 요인

제3장 경쟁 분석

  • KBV Cardinal Matrix
  • 최근 업계 전체 전략 전개
    • 파트너십, 협업 및 계약
    • 제품 출시 및 제품 확대
    • 인수와 합병
  • 시장 점유율 분석(2021년)
  • 주요 성공 전략
    • 주요 전략
    • 주요 전략 움직임

제4장 세계의 DataOps 플랫폼 시장 : 전개별

  • 세계의 클라우드 시장 : 지역별
  • 세계의 DataOps 플랫폼 시장 : 클라우드 유형별
    • 세계의 퍼블릭 클라우드 시장 : 지역별
    • 세계의 프라이빗 클라우드 시장 : 지역별
    • 세계의 하이브리드 클라우드 시장 : 지역별
  • 세계의 온프레미스 시장 : 지역별

제5장 세계의 DataOps 플랫폼 시장 : 서비스 모형별

  • 세계의 애자일 개발 시장 : 지역별
  • 세계의 DevOps 시장 : 지역별
  • 세계의 린 생산 방식 시장 : 지역별

제6장 세계의 DataOps 플랫폼 시장 : 컴포넌트별

  • 세계의 플랫폼 시장 : 지역별
  • 세계의 서비스 시장 : 지역별

제7장 세계의 DataOps 플랫폼 시장 : 업계별

  • 세계의 BFSI 시장 : 지역별
  • 세계의 IT 및 통신 시장 : 지역별
  • 세계의 소매 시장 : 지역별
  • 세계의 미디어 및 엔터테인먼트 시장 : 지역별
  • 세계의 헬스케어 및 치과 시장 : 지역별
  • 세계의 에너지 c 유틸리티 시장 : 지역별
  • 세계의 제조업 시장 : 지역별
  • 세계의 정부 시장 : 지역별
  • 세계의 기타 시장 : 지역별

제8장 세계의 DataOps 플랫폼 시장 : 지역별

  • 북미
    • 북미 시장 : 국가별
      • 미국
      • 캐나다
      • 멕시코
      • 기타 북미
  • 유럽
    • 유럽 시장 : 국가별
      • 독일
      • 영국
      • 프랑스
      • 러시아
      • 스페인
      • 이탈리아
      • 기타 유럽
  • 아시아태평양
    • 아시아태평양 시장 : 국가별
      • 중국
      • 일본
      • 인도
      • 한국
      • 싱가포르
      • 말레이시아
      • 기타 아시아태평양
  • 라틴아메리카, 중동, 아프리카
    • 라틴아메리카, 중동, 아프리카 시장 : 국가별
      • 브라질
      • 아르헨티나
      • 아랍에미리트(UAE)
      • 사우디아라비아
      • 남아프리카
      • 나이지리아
      • 기타 라틴아메리카, 중동, 아프리카

제9장 기업 프로필

  • Microsoft Corporation
  • IBM Corporation
  • Amazon Web Services, Inc(Amazon.com, Inc.)
  • Oracle Corporation
  • Informatica, LLC
  • Teradata Corporation
  • Wipro Limited
  • Accenture PLC
  • SAS Institute, Inc
  • Hitachi Vantara LLC(Hitachi Ltd)
NJH 23.08.24

The Global DataOps Platform Market size is expected to reach $14.6 billion by 2030, rising at a market growth of 21.2% CAGR during the forecast period.

Asia Pacific region is the most promising region for DataOps platforms due to several causes, including the exponential expansion of big data, the popularity of cloud computing, and the development of artificial intelligence. Hence, Asia Pacific acquired $892.1 million revenue in the market in 2022. In addition, businesses are looking for automated solutions to manage their data effectively due to the unprecedented growth in data volumes to cut costs, enhance operational efficiency, and improve data quality. In the Asia Pacific region, the DataOps platform business environment is broad and continuously changing. It includes a wide range of technology manufacturers, service providers, and consulting companies that offer enterprises complete end-to-end data management solutions.

The major strategies followed by the market participants are Product Launches as the key developmental strategy in order to keep pace with the changing demands of end users. For instance, In May 2023, IBM announced the launch of the Watsonx Platform, a data platform used for increasing the effect of AI and features IBM Watsonx.ai used for testing and deploying new AI capabilities, IBM Watsonx.data, a data store used for governed data, and IBM Watsonx.governance, an AI-powered workflow enabler. Additionally, In May 2023, Hitachi Vantara announced the launch of Data Reliability Engineering (DRE), a collection of services used for enhancing the uniformity and quality of important business data. It features metadata engineering, data cost optimization, AI-powered automation, and data lineage for providing complete transparency and reliability throughout the data lifecycle.

Based on the Analysis presented in the KBV Cardinal matrix; Microsoft Corporation is the major forerunner in the Market. In October 2022, Microsoft announced the launch of ArcBox for DataOps. ArcBox for DataOps is a data-based service used for the automation of deployment of different business operations. The service features Azure Infrastructure and integrations and three Kubernetes clusters. Companies such as Hitachi Vantara LLC, Accenture PLC, Oracle Corporation are some of the key innovators in the Market.

Market Growth Factors

Rising data complexity and volumes

Organizations have to cope with ever-increasing amounts of data from many sources, in structured and unstructured formats, as well as real-time data streams. The quantity, velocity, and diversity of data frequently make it difficult for traditional data management techniques to keep up, which causes inefficiencies, mistakes, and delays in data processing and analysis. Organizations may integrate, process, and analyze data effectively with the help of DataOps platforms, which provide the required tools and technology to handle this complexity. Platforms for data operations can enable both batch processing and real-time data streaming. These factors are fueling the growth of the market.

The rising adoption of cloud-native DataOps

Despite the fact that cloud-native DataOps is still a relatively new development in the market for DataOps platforms, it is quickly overtaking other approaches as more companies look to modernize their data operations. Creating and deploying data pipelines and processes on cloud computing platforms is the foundation of this strategy. One of the main advantages of cloud-native DataOps is scalability. Cloud technology enables quick resource expansion, so organizations can manage large volumes of data without worrying about infrastructure restrictions. This is anticipated to promote the use of the DataOps platform by different end users, propelling the market expansion.

Market Restraining Factors

Need to address the challenges posed by the talent shortage

The need for more highly competent professionals is one of the most significant obstacles in the market. DataOps platforms necessitate data science, data engineering, software development, and operations experts. However, there is a talent gap due to a severe shortage of professionals with these specific skills. As a result, organizations are having trouble locating qualified candidates to design, implement, and maintain DataOps platforms. In addition, the rapid velocity of technological development is exacerbating this talent gap. As a result, existing team members may require additional or retraining to acclimate to the DataOps methodology.

Component Outlook

Based on component, the market is segmented into platform and services. The platform segment dominated the market with maximum revenue share in 2022. The segment growth is due to its usage to address issues with inefficient data production and processing and poor data quality brought on by mistakes and inconsistencies. It gives everyone in the supply chain access to agile software for data curation, governance, management, and provisioning that is integrated and optimized throughout the full data lifetime.

Service Model Outlook

On the basis of service model, the market is divided into agile development, DevOps and lean manufacturing. The DevOps segment procured a substantial revenue share in the market in 2022. This is because DataOps uses DevOps tools to convert data insights into outputs for production. In addition, real-time monitoring is a feature of these technologies that aid in optimizing the data pipelines. Moreover, the DevOps principles aid in smoothly implementing the inputs supplied by the user and business teams. Thus, such features of the DevOps are anticipated to surge the segment's expansion in the projected period.

Deployment Outlook

By deployment, the market is classified into cloud and on-premise. The cloud segment witnessed the largest revenue share in the market in 2022. The segment's growth results from the adoption of cloud deployment, which allows customers to access the DataOps platform from any location with an internet connection, increasing flexibility and accessibility. Businesses may now take advantage of the platform's capabilities without being constrained by physical location owing to the improved accessibility.

Cloud Type Outlook

Under the cloud type, the market is divided into public cloud, private cloud, and hybrid cloud. The public cloud segment led the market with maximum revenue share in 2022. This is because the most prevalent form of cloud computing deployment is public clouds. The provider owns and manages the hardware, software, and other supporting infrastructure with a public cloud. With the public cloud, users only pay for their services and don't need to buy hardware or software. No maintenance is required with the public cloud. In addition, on-demand resources are available to match business needs, providing nearly infinite scalability.

Vertical Outlook

Based on the vertical, the market is bifurcated into BFSI, healthcare & dental, retail, manufacturing, government, IT & telecommunications, energy & utilities, media & entertainment and others. The IT & telecommunication segment recorded a significant revenue share in the market in 2022. This is because fast and secure access to data is essential to provide exceptional, differentiating consumer experiences while operating at a large scale. In addition, telecom operators can supply enterprise data safely and effectively for projects like cloud migration while safeguarding private customer data by implementing a DevOps data platform.

Regional Outlook

Region-wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America region led the market by generating the maximum revenue share in 2022. This is due to the region's thriving technology sector and unwavering dedication to innovation and digital transformation. The region's unwavering focus on innovation and digital transformation is the primary factor behind the widespread use of DataOps platforms in North America. Businesses in North America are constantly looking for new strategies to encourage innovation and acquire a competitive edge.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, IBM Corporation, Oracle Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Informatica, LLC, Teradata Corporation, Wipro Limited, Accenture PLC, SAS Institute, Inc. and Hitachi Vantara LLC (Hitachi Ltd.)

Recent Strategies Deployed in DataOps Platform Market

Partnerships, Collaborations, and Agreements:

May-2023: Wipro partnered with ServiceNow, a software company based in the USA. The partnership aims to provide the joint clients of the two companies with solutions for business transformation. By doing so, the two companies would be able to serve their customers in a better way.

May-2022: Oracle announced a partnership with Informatica, an enterprise cloud data management solutions provider. The partnership integrates Oracle's portfolio with Informatica's portfolio and allows their customers to serve their customers in a better way.

Nov-2021: Amazon Web Services (AWS) teamed up with Goldman Sachs, an investment banking firm to launch Goldman Sachs Financial Cloud for Data. The collaboration allows Amazon Web Services to serve its customers in the financial sector in a better way by providing them with instant analytics in the cloud.

Nov-2021: Amazon Web Services announced a partnership with Accenture, a professional services company. Through this partnership, the two companies aim to provide their joint customers with cloud-based automation solutions through Accenture AWS Business Group (AABG). The partnership allows AWS to serve its customers in a better way by providing them with innovative solutions.

Dec-2020: Amazon Web Services (AWS) entered into a partnership with Alation, a data intelligence solutions provider to integrate their data governance and search solutions with AWS services. The partnership allows the two companies to serve their customers in a better way.

Jun-2020: Microsoft partnered with Hitachi, a Japanese Multinational Corporation, to provide automation solutions for industries in Southeast Asia, Japan, and North America. Through this partnership, Microsoft would be able to serve its customers in a better way by unlocking new opportunities for providing them with solutions.

Product Launches and Product Expansions:

May-2023: IBM announced the launch of the Watsonx Platform. The Watsonx Platform is a data platform used for increasing the effect of AI. The Watsonx Platform features IBM Watsonx.ai used for testing and deploying new AI capabilities, IBM Watsonx.data, a data store used for governed data, and IBM Watsonx.governance, an AI-powered workflow enabler.

May-2023: Hitachi Vantara announced the launch of Data Reliability Engineering (DRE). Data Reliability Engineering (DRE) is a collection of services used for enhancing the uniformity and quality of important business data. Data Reliability Engineering (DRE) features metadata engineering, data cost optimization, AI-powered automation, and data lineage for providing complete transparency and reliability throughout the data lifecycle.

Mar-2023: Teradata introduced Teradata VantageCloud, a cloud-based data analytics platform. The Teradata VantageCloud features Microsoft Azure Machine Learning (Azure ML). The benefits of the product include enhance demand forecast, better risk management, and better patient care.

Mar-2023: Oracle launched Java 20, an upgraded version of Java. Java 20 is used for delivering security, performance, and stability improvements. The new version features Language improvements such as JEP 432 and JEP 433, Incubator features including JEP 429, JEP 436, and JEP 437, and Project Panama preview features including JEP 434 and JEP 438.

Nov-2022: Informatica announced the launch of the Intelligent Data Management Cloud (IDMC) platform. The Intelligent Data Management Cloud (IDMC) platform is used for processing transactions and providing insights for the efficient delivery of services by different governments. The benefits of the Intelligent Data Management Cloud (IDMC) platform include quick reaction for crisis and speedy recovery, Enhanced cybersecurity, and a better digital citizen experience.

Oct-2022: Microsoft announced the launch of ArcBox for DataOps. ArcBox for DataOps is a data-based service used for the automation of deployment of different business operations. The service features Azure Infrastructure and integrations and three Kubernetes clusters.

Mar-2022: Hitachi Vantara announced new capabilities for Lumada DataOps. The new features include Data Catalog used for enhancing business insights and Data Integration used for combining data across a hybrid cloud.

Mar-2021: Informatica unveiled the Spark-based Cloud Data Integration engine, used for boosting performance. The Spark-based Cloud Data Integration engine features NVIDIA infrastructure and RAPIDS data science software. Benefits of the Spark-based Cloud Data Integration engine include cost minimization, enhanced data processing speed, and increased data access throughout the organization.

Dec-2020: Amazon Web Services (AWS) introduced Amazon HealthLake. Amazon HealthLake is a service used for big data analytics in healthcare applications. Amazon HealthLake features interoperability and automated learning for data sorting and identification.

Nov-2019: Accenture announced the launch of myNav. The myNav is a cloud-based platform used for simulating a variety of cloud solutions. The myNav features multiple variable evaluations used for providing correct solutions to organizations.

Sep-2019: IBM added new features to Cloud Pak for Data. The new features include AI-powered global search, Automated metadata generation used for classifying and verifying data, AI-powered risk detection of unstructured data, and enhanced connectivity with InfoSphere Advanced Data Preparation.

Jan-2019: Accenture unveiled SynOps. SynOps is an operating engine used for driving business transformation by enhancing data coordination. The SynOps features Combine human and machine intelligence, Work harmony, diverse data analysis, and Accenture Insights Platform.

Acquisition and Mergers:

Mar-2023: Accenture announced the acquisition of Flutura, an industrial AI company based in Bangalore. The acquisition enhances Accenture's industrial AI services for bettering the performance of refineries and plants.

Jul-2022: IBM took over Databand.ai, a data observability provider headquartered in Israel. This acquisition enables IBM to serve its customers in a better way by providing them with a complete portfolio of services for IT in machine learning and data applications.

Jun-2022: Oracle announced the acquisition of Cerner, a healthcare information systems supplier. The acquisition provides Oracle with Cerner's portfolio of services and extends its reach in the healthcare sector by allowing them to provide automation solutions to their customers in the healthcare sector.

Jun-2021: Hitachi Vantara took over Io-Tahoe, a data management solutions provider based in the UK. The acquisition enhances Lumada DataOps Suite by integrating it with Io-Tahoe's AI-powered data management software for driving business transformation. Furthermore, the acquisitions enhance Hitachi Vantara's ability to serve its customers in a better way by providing them with better solutions for business transformation.

Dec-2020: IBM acquired Instana, an organization performance monitoring platform. This acquisition allows IBM to provide its customers with leading AI-enabled automation powers. Furthermore, this acquisition enhances IBM's Watson AIOps offering.

May-2020: Hitachi Vantara acquired Waterline Data, Inc., a data cataloging solutions provider. The acquisition strengthens Hitachi Vantara's DataOps solution and enables the company to serve its customers in a better way by providing them with solutions for managing their data assets in multiple environments.

Scope of the Study

Market Segments covered in the Report:

By Deployment

  • Cloud
    • Public Cloud
    • Private Cloud
    • Hybrid Cloud
  • On-premise

By Service Model

  • Agile Development
  • DevOps
  • Lean Manufacturing

By Component

  • Platform
  • Services

By Vertical

  • BFSI
  • IT & Telecommunication
  • Retail
  • Media & Entertainment
  • Healthcare & Dental
  • Energy & Utilities
  • Manufacturing
  • Government
  • Others

By Geography

  • North America
    • US
    • Canada
    • Mexico
    • Rest of North America
  • Europe
    • Germany
    • UK
    • France
    • Russia
    • Spain
    • Italy
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Singapore
    • Malaysia
    • Rest of Asia Pacific
  • LAMEA
    • Brazil
    • Argentina
    • UAE
    • Saudi Arabia
    • South Africa
    • Nigeria
    • Rest of LAMEA

Companies Profiled

  • Microsoft Corporation
  • IBM Corporation
  • Oracle Corporation
  • Amazon Web Services, Inc. (Amazon.com, Inc.)
  • Informatica, LLC
  • Teradata Corporation
  • Wipro Limited
  • Accenture PLC
  • SAS Institute, Inc.
  • Hitachi Vantara LLC (Hitachi Ltd.)

Unique Offerings from KBV Research

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  • Highest number of market tables and figures
  • Subscription based model available
  • Guaranteed best price
  • Assured post sales research support with 10% customization free

Table of Contents

Chapter 1. Market Scope & Methodology

  • 1.1 Market Definition
  • 1.2 Objectives
  • 1.3 Market Scope
  • 1.4 Segmentation
    • 1.4.1 Global DataOps Platform Market, by Deployment
    • 1.4.2 Global DataOps Platform Market, by Service Model
    • 1.4.3 Global DataOps Platform Market, by Component
    • 1.4.4 Global DataOps Platform Market, by Vertical
    • 1.4.5 Global DataOps Platform Market, by Geography
  • 1.5 Methodology for the research

Chapter 2. Market Overview

  • 2.1 Introduction
    • 2.1.1 Overview
      • 2.1.1.1 Market composition & scenario
  • 2.2 Key Factors Impacting the Market
    • 2.2.1 Market Drivers
    • 2.2.2 Market Restraints

Chapter 3. Competition Analysis - Global

  • 3.1 KBV Cardinal Matrix
  • 3.2 Recent Industry Wide Strategic Developments
    • 3.2.1 Partnerships, Collaborations and Agreements
    • 3.2.2 Product Launches and Product Expansions
    • 3.2.3 Acquisition and Mergers
  • 3.3 Market Share Analysis, 2021
  • 3.4 Top Winning Strategies
    • 3.4.1 Key Leading Strategies: Percentage Distribution (2019-2023)
    • 3.4.2 Key Strategic Move: (Product Launches and Product Expansions: 2019, Jan - 2023, May) Leading Players

Chapter 4. Global DataOps Platform Market by Deployment

  • 4.1 Global Cloud Market by Region
  • 4.2 Global DataOps Platform Market by Cloud Type
    • 4.2.1 Global Public Cloud Market by Region
    • 4.2.2 Global Private Cloud Market by Region
    • 4.2.3 Global Hybrid Cloud Market by Region
  • 4.3 Global On-premise Market by Region

Chapter 5. Global DataOps Platform Market by Service Model

  • 5.1 Global Agile Development Market by Region
  • 5.2 Global DevOps Market by Region
  • 5.3 Global Lean Manufacturing Market by Region

Chapter 6. Global DataOps Platform Market by Component

  • 6.1 Global Platform Market by Region
  • 6.2 Global Services Market by Region

Chapter 7. Global DataOps Platform Market by Vertical

  • 7.1 Global BFSI Market by Region
  • 7.2 Global IT & Telecommunication Market by Region
  • 7.3 Global Retail Market by Region
  • 7.4 Global Media & Entertainment Market by Region
  • 7.5 Global Healthcare & Dental Market by Region
  • 7.6 Global Energy & Utilities Market by Region
  • 7.7 Global Manufacturing Market by Region
  • 7.8 Global Government Market by Region
  • 7.9 Global Others Market by Region

Chapter 8. Global DataOps Platform Market by Region

  • 8.1 North America DataOps Platform Market
    • 8.1.1 North America DataOps Platform Market by Deployment
      • 8.1.1.1 North America Cloud Market by Country
      • 8.1.1.2 North America DataOps Platform Market by Cloud Type
        • 8.1.1.2.1 North America Public Cloud Market by Country
        • 8.1.1.2.2 North America Private Cloud Market by Country
        • 8.1.1.2.3 North America Hybrid Cloud Market by Country
      • 8.1.1.3 North America On-premise Market by Country
    • 8.1.2 North America DataOps Platform Market by Service Model
      • 8.1.2.1 North America Agile Development Market by Country
      • 8.1.2.2 North America DevOps Market by Country
      • 8.1.2.3 North America Lean Manufacturing Market by Country
    • 8.1.3 North America DataOps Platform Market by Component
      • 8.1.3.1 North America Platform Market by Country
      • 8.1.3.2 North America Services Market by Country
    • 8.1.4 North America DataOps Platform Market by Vertical
      • 8.1.4.1 North America BFSI Market by Country
      • 8.1.4.2 North America IT & Telecommunication Market by Country
      • 8.1.4.3 North America Retail Market by Country
      • 8.1.4.4 North America Media & Entertainment Market by Country
      • 8.1.4.5 North America Healthcare & Dental Market by Country
      • 8.1.4.6 North America Energy & Utilities Market by Country
      • 8.1.4.7 North America Manufacturing Market by Country
      • 8.1.4.8 North America Government Market by Country
      • 8.1.4.9 North America Others Market by Country
    • 8.1.5 North America DataOps Platform Market by Country
      • 8.1.5.1 US DataOps Platform Market
        • 8.1.5.1.1 US DataOps Platform Market by Deployment
        • 8.1.5.1.2 US DataOps Platform Market by Service Model
        • 8.1.5.1.3 US DataOps Platform Market by Component
        • 8.1.5.1.4 US DataOps Platform Market by Vertical
      • 8.1.5.2 Canada DataOps Platform Market
        • 8.1.5.2.1 Canada DataOps Platform Market by Deployment
        • 8.1.5.2.2 Canada DataOps Platform Market by Service Model
        • 8.1.5.2.3 Canada DataOps Platform Market by Component
        • 8.1.5.2.4 Canada DataOps Platform Market by Vertical
      • 8.1.5.3 Mexico DataOps Platform Market
        • 8.1.5.3.1 Mexico DataOps Platform Market by Deployment
        • 8.1.5.3.2 Mexico DataOps Platform Market by Service Model
        • 8.1.5.3.3 Mexico DataOps Platform Market by Component
        • 8.1.5.3.4 Mexico DataOps Platform Market by Vertical
      • 8.1.5.4 Rest of North America DataOps Platform Market
        • 8.1.5.4.1 Rest of North America DataOps Platform Market by Deployment
        • 8.1.5.4.2 Rest of North America DataOps Platform Market by Service Model
        • 8.1.5.4.3 Rest of North America DataOps Platform Market by Component
        • 8.1.5.4.4 Rest of North America DataOps Platform Market by Vertical
  • 8.2 Europe DataOps Platform Market
    • 8.2.1 Europe DataOps Platform Market by Deployment
      • 8.2.1.1 Europe Cloud Market by Country
      • 8.2.1.2 Europe DataOps Platform Market by Cloud Type
        • 8.2.1.2.1 Europe Public Cloud Market by Country
        • 8.2.1.2.2 Europe Private Cloud Market by Country
        • 8.2.1.2.3 Europe Hybrid Cloud Market by Country
      • 8.2.1.3 Europe On-premise Market by Country
    • 8.2.2 Europe DataOps Platform Market by Service Model
      • 8.2.2.1 Europe Agile Development Market by Country
      • 8.2.2.2 Europe DevOps Market by Country
      • 8.2.2.3 Europe Lean Manufacturing Market by Country
    • 8.2.3 Europe DataOps Platform Market by Component
      • 8.2.3.1 Europe Platform Market by Country
      • 8.2.3.2 Europe Services Market by Country
    • 8.2.4 Europe DataOps Platform Market by Vertical
      • 8.2.4.1 Europe BFSI Market by Country
      • 8.2.4.2 Europe IT & Telecommunication Market by Country
      • 8.2.4.3 Europe Retail Market by Country
      • 8.2.4.4 Europe Media & Entertainment Market by Country
      • 8.2.4.5 Europe Healthcare & Dental Market by Country
      • 8.2.4.6 Europe Energy & Utilities Market by Country
      • 8.2.4.7 Europe Manufacturing Market by Country
      • 8.2.4.8 Europe Government Market by Country
      • 8.2.4.9 Europe Others Market by Country
    • 8.2.5 Europe DataOps Platform Market by Country
      • 8.2.5.1 Germany DataOps Platform Market
        • 8.2.5.1.1 Germany DataOps Platform Market by Deployment
        • 8.2.5.1.2 Germany DataOps Platform Market by Service Model
        • 8.2.5.1.3 Germany DataOps Platform Market by Component
        • 8.2.5.1.4 Germany DataOps Platform Market by Vertical
      • 8.2.5.2 UK DataOps Platform Market
        • 8.2.5.2.1 UK DataOps Platform Market by Deployment
        • 8.2.5.2.2 UK DataOps Platform Market by Service Model
        • 8.2.5.2.3 UK DataOps Platform Market by Component
        • 8.2.5.2.4 UK DataOps Platform Market by Vertical
      • 8.2.5.3 France DataOps Platform Market
        • 8.2.5.3.1 France DataOps Platform Market by Deployment
        • 8.2.5.3.2 France DataOps Platform Market by Service Model
        • 8.2.5.3.3 France DataOps Platform Market by Component
        • 8.2.5.3.4 France DataOps Platform Market by Vertical
      • 8.2.5.4 Russia DataOps Platform Market
        • 8.2.5.4.1 Russia DataOps Platform Market by Deployment
        • 8.2.5.4.2 Russia DataOps Platform Market by Service Model
        • 8.2.5.4.3 Russia DataOps Platform Market by Component
        • 8.2.5.4.4 Russia DataOps Platform Market by Vertical
      • 8.2.5.5 Spain DataOps Platform Market
        • 8.2.5.5.1 Spain DataOps Platform Market by Deployment
        • 8.2.5.5.2 Spain DataOps Platform Market by Service Model
        • 8.2.5.5.3 Spain DataOps Platform Market by Component
        • 8.2.5.5.4 Spain DataOps Platform Market by Vertical
      • 8.2.5.6 Italy DataOps Platform Market
        • 8.2.5.6.1 Italy DataOps Platform Market by Deployment
        • 8.2.5.6.2 Italy DataOps Platform Market by Service Model
        • 8.2.5.6.3 Italy DataOps Platform Market by Component
        • 8.2.5.6.4 Italy DataOps Platform Market by Vertical
      • 8.2.5.7 Rest of Europe DataOps Platform Market
        • 8.2.5.7.1 Rest of Europe DataOps Platform Market by Deployment
        • 8.2.5.7.2 Rest of Europe DataOps Platform Market by Service Model
        • 8.2.5.7.3 Rest of Europe DataOps Platform Market by Component
        • 8.2.5.7.4 Rest of Europe DataOps Platform Market by Vertical
  • 8.3 Asia Pacific DataOps Platform Market
    • 8.3.1 Asia Pacific DataOps Platform Market by Deployment
      • 8.3.1.1 Asia Pacific Cloud Market by Country
      • 8.3.1.2 Asia Pacific DataOps Platform Market by Cloud Type
        • 8.3.1.2.1 Asia Pacific Public Cloud Market by Country
        • 8.3.1.2.2 Asia Pacific Private Cloud Market by Country
        • 8.3.1.2.3 Asia Pacific Hybrid Cloud Market by Country
      • 8.3.1.3 Asia Pacific On-premise Market by Country
    • 8.3.2 Asia Pacific DataOps Platform Market by Service Model
      • 8.3.2.1 Asia Pacific Agile Development Market by Country
      • 8.3.2.2 Asia Pacific DevOps Market by Country
      • 8.3.2.3 Asia Pacific Lean Manufacturing Market by Country
    • 8.3.3 Asia Pacific DataOps Platform Market by Component
      • 8.3.3.1 Asia Pacific Platform Market by Country
      • 8.3.3.2 Asia Pacific Services Market by Country
    • 8.3.4 Asia Pacific DataOps Platform Market by Vertical
      • 8.3.4.1 Asia Pacific BFSI Market by Country
      • 8.3.4.2 Asia Pacific IT & Telecommunication Market by Country
      • 8.3.4.3 Asia Pacific Retail Market by Country
      • 8.3.4.4 Asia Pacific Media & Entertainment Market by Country
      • 8.3.4.5 Asia Pacific Healthcare & Dental Market by Country
      • 8.3.4.6 Asia Pacific Energy & Utilities Market by Country
      • 8.3.4.7 Asia Pacific Manufacturing Market by Country
      • 8.3.4.8 Asia Pacific Government Market by Country
      • 8.3.4.9 Asia Pacific Others Market by Country
    • 8.3.5 Asia Pacific DataOps Platform Market by Country
      • 8.3.5.1 China DataOps Platform Market
        • 8.3.5.1.1 China DataOps Platform Market by Deployment
        • 8.3.5.1.2 China DataOps Platform Market by Service Model
        • 8.3.5.1.3 China DataOps Platform Market by Component
        • 8.3.5.1.4 China DataOps Platform Market by Vertical
      • 8.3.5.2 Japan DataOps Platform Market
        • 8.3.5.2.1 Japan DataOps Platform Market by Deployment
        • 8.3.5.2.2 Japan DataOps Platform Market by Service Model
        • 8.3.5.2.3 Japan DataOps Platform Market by Component
        • 8.3.5.2.4 Japan DataOps Platform Market by Vertical
      • 8.3.5.3 India DataOps Platform Market
        • 8.3.5.3.1 India DataOps Platform Market by Deployment
        • 8.3.5.3.2 India DataOps Platform Market by Service Model
        • 8.3.5.3.3 India DataOps Platform Market by Component
        • 8.3.5.3.4 India DataOps Platform Market by Vertical
      • 8.3.5.4 South Korea DataOps Platform Market
        • 8.3.5.4.1 South Korea DataOps Platform Market by Deployment
        • 8.3.5.4.2 South Korea DataOps Platform Market by Service Model
        • 8.3.5.4.3 South Korea DataOps Platform Market by Component
        • 8.3.5.4.4 South Korea DataOps Platform Market by Vertical
      • 8.3.5.5 Singapore DataOps Platform Market
        • 8.3.5.5.1 Singapore DataOps Platform Market by Deployment
        • 8.3.5.5.2 Singapore DataOps Platform Market by Service Model
        • 8.3.5.5.3 Singapore DataOps Platform Market by Component
        • 8.3.5.5.4 Singapore DataOps Platform Market by Vertical
      • 8.3.5.6 Malaysia DataOps Platform Market
        • 8.3.5.6.1 Malaysia DataOps Platform Market by Deployment
        • 8.3.5.6.2 Malaysia DataOps Platform Market by Service Model
        • 8.3.5.6.3 Malaysia DataOps Platform Market by Component
        • 8.3.5.6.4 Malaysia DataOps Platform Market by Vertical
      • 8.3.5.7 Rest of Asia Pacific DataOps Platform Market
        • 8.3.5.7.1 Rest of Asia Pacific DataOps Platform Market by Deployment
        • 8.3.5.7.2 Rest of Asia Pacific DataOps Platform Market by Service Model
        • 8.3.5.7.3 Rest of Asia Pacific DataOps Platform Market by Component
        • 8.3.5.7.4 Rest of Asia Pacific DataOps Platform Market by Vertical
  • 8.4 LAMEA DataOps Platform Market
    • 8.4.1 LAMEA DataOps Platform Market by Deployment
      • 8.4.1.1 LAMEA Cloud Market by Country
      • 8.4.1.2 LAMEA DataOps Platform Market by Cloud Type
        • 8.4.1.2.1 LAMEA Public Cloud Market by Country
        • 8.4.1.2.2 LAMEA Private Cloud Market by Country
        • 8.4.1.2.3 LAMEA Hybrid Cloud Market by Country
      • 8.4.1.3 LAMEA On-premise Market by Country
    • 8.4.2 LAMEA DataOps Platform Market by Service Model
      • 8.4.2.1 LAMEA Agile Development Market by Country
      • 8.4.2.2 LAMEA DevOps Market by Country
      • 8.4.2.3 LAMEA Lean Manufacturing Market by Country
    • 8.4.3 LAMEA DataOps Platform Market by Component
      • 8.4.3.1 LAMEA Platform Market by Country
      • 8.4.3.2 LAMEA Services Market by Country
    • 8.4.4 LAMEA DataOps Platform Market by Vertical
      • 8.4.4.1 LAMEA BFSI Market by Country
      • 8.4.4.2 LAMEA IT & Telecommunication Market by Country
      • 8.4.4.3 LAMEA Retail Market by Country
      • 8.4.4.4 LAMEA Media & Entertainment Market by Country
      • 8.4.4.5 LAMEA Healthcare & Dental Market by Country
      • 8.4.4.6 LAMEA Energy & Utilities Market by Country
      • 8.4.4.7 LAMEA Manufacturing Market by Country
      • 8.4.4.8 LAMEA Government Market by Country
      • 8.4.4.9 LAMEA Others Market by Country
    • 8.4.5 LAMEA DataOps Platform Market by Country
      • 8.4.5.1 Brazil DataOps Platform Market
        • 8.4.5.1.1 Brazil DataOps Platform Market by Deployment
        • 8.4.5.1.2 Brazil DataOps Platform Market by Service Model
        • 8.4.5.1.3 Brazil DataOps Platform Market by Component
        • 8.4.5.1.4 Brazil DataOps Platform Market by Vertical
      • 8.4.5.2 Argentina DataOps Platform Market
        • 8.4.5.2.1 Argentina DataOps Platform Market by Deployment
        • 8.4.5.2.2 Argentina DataOps Platform Market by Service Model
        • 8.4.5.2.3 Argentina DataOps Platform Market by Component
        • 8.4.5.2.4 Argentina DataOps Platform Market by Vertical
      • 8.4.5.3 UAE DataOps Platform Market
        • 8.4.5.3.1 UAE DataOps Platform Market by Deployment
        • 8.4.5.3.2 UAE DataOps Platform Market by Service Model
        • 8.4.5.3.3 UAE DataOps Platform Market by Component
        • 8.4.5.3.4 UAE DataOps Platform Market by Vertical
      • 8.4.5.4 Saudi Arabia DataOps Platform Market
        • 8.4.5.4.1 Saudi Arabia DataOps Platform Market by Deployment
        • 8.4.5.4.2 Saudi Arabia DataOps Platform Market by Service Model
        • 8.4.5.4.3 Saudi Arabia DataOps Platform Market by Component
        • 8.4.5.4.4 Saudi Arabia DataOps Platform Market by Vertical
      • 8.4.5.5 South Africa DataOps Platform Market
        • 8.4.5.5.1 South Africa DataOps Platform Market by Deployment
        • 8.4.5.5.2 South Africa DataOps Platform Market by Service Model
        • 8.4.5.5.3 South Africa DataOps Platform Market by Component
        • 8.4.5.5.4 South Africa DataOps Platform Market by Vertical
      • 8.4.5.6 Nigeria DataOps Platform Market
        • 8.4.5.6.1 Nigeria DataOps Platform Market by Deployment
        • 8.4.5.6.2 Nigeria DataOps Platform Market by Service Model
        • 8.4.5.6.3 Nigeria DataOps Platform Market by Component
        • 8.4.5.6.4 Nigeria DataOps Platform Market by Vertical
      • 8.4.5.7 Rest of LAMEA DataOps Platform Market
        • 8.4.5.7.1 Rest of LAMEA DataOps Platform Market by Deployment
        • 8.4.5.7.2 Rest of LAMEA DataOps Platform Market by Service Model
        • 8.4.5.7.3 Rest of LAMEA DataOps Platform Market by Component
        • 8.4.5.7.4 Rest of LAMEA DataOps Platform Market by Vertical

Chapter 9. Company Profiles

  • 9.1 Microsoft Corporation
    • 9.1.1 Company Overview
    • 9.1.2 Financial Analysis
    • 9.1.3 Segmental and Regional Analysis
    • 9.1.4 Research & Development Expenses
    • 9.1.5 Recent strategies and developments:
      • 9.1.5.1 Partnerships, Collaborations, and Agreements:
      • 9.1.5.2 Product Launches and Product Expansions:
    • 9.1.6 SWOT Analysis
  • 9.2 IBM Corporation
    • 9.2.1 Company Overview
    • 9.2.2 Financial Analysis
    • 9.2.3 Regional & Segmental Analysis
    • 9.2.4 Research & Development Expenses
    • 9.2.5 Recent strategies and developments:
      • 9.2.5.1 Product Launches and Product Expansions:
      • 9.2.5.2 Acquisition and Mergers:
    • 9.2.6 SWOT Analysis
  • 9.3 Amazon Web Services, Inc. (Amazon.com, Inc.)
    • 9.3.1 Company Overview
    • 9.3.2 Financial Analysis
    • 9.3.3 Segmental Analysis
    • 9.3.4 Recent strategies and developments:
      • 9.3.4.1 Partnerships, Collaborations, and Agreements:
      • 9.3.4.2 Product Launches and Product Expansions:
    • 9.3.5 SWOT Analysis
  • 9.4 Oracle Corporation
    • 9.4.1 Company Overview
    • 9.4.2 Financial Analysis
    • 9.4.3 Segmental and Regional Analysis
    • 9.4.4 Research & Development Expense
    • 9.4.5 Recent strategies and developments:
      • 9.4.5.1 Partnerships, Collaborations, and Agreements:
      • 9.4.5.2 Product Launches and Product Expansions:
      • 9.4.5.3 Acquisition and Mergers:
    • 9.4.6 SWOT Analysis
  • 9.5 Informatica, LLC
    • 9.5.1 Company Overview
    • 9.5.2 Financial Analysis
    • 9.5.3 Regional Analysis
    • 9.5.4 Research & Development Expenses
    • 9.5.5 Recent strategies and developments:
      • 9.5.5.1 Product Launches and Product Expansions:
  • 9.6 Teradata Corporation
    • 9.6.1 Company Overview
    • 9.6.2 Financial Analysis
    • 9.6.3 Regional Analysis
    • 9.6.4 Research & Development Expense
    • 9.6.5 Recent strategies and developments:
      • 9.6.5.1 Product Launches and Product Expansions:
    • 9.6.6 SWOT Analysis
  • 9.7 Wipro Limited
    • 9.7.1 Company Overview
    • 9.7.2 Financial Analysis
    • 9.7.3 Segmental and Regional Analysis
    • 9.7.4 Research & Development Expenses
    • 9.7.5 Recent strategies and developments:
      • 9.7.5.1 Partnerships, Collaborations, and Agreements:
    • 9.7.6 SWOT Analysis
  • 9.8 Accenture PLC
    • 9.8.1 Company Overview
    • 9.8.2 Financial Analysis
    • 9.8.3 Segmental and Regional Analysis
    • 9.8.4 Research & Development Expenses
    • 9.8.5 Recent strategies and developments:
      • 9.8.5.1 Product Launches and Product Expansions:
      • 9.8.5.2 Acquisition and Mergers:
    • 9.8.6 SWOT Analysis
  • 9.9 SAS Institute, Inc.
    • 9.9.1 Company Overview
  • 9.10. Hitachi Vantara LLC (Hitachi Ltd.)
    • 9.10.1 Company Overview
    • 9.10.2 Financial Analysis
    • 9.10.3 Segmental and Regional Analysis
    • 9.10.4 Research & Development Expenses
    • 9.10.5 Recent strategies and developments:
      • 9.10.5.1 Product Launches and Product Expansions:
      • 9.10.5.2 Acquisition and Mergers:
    • 9.10.6 SWOT Analysis
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