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市場調查報告書
商品編碼
2047058

石油天然氣巨量資料市場－全球產業規模、佔有率、趨勢、機會與預測：按組件、應用、資料類型、地區和競爭對手分類，2021-2031年

Big Data in Oil & Gas Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Components, By Application, By Data Type, By Region & Competition, 2021-2031F
出版日期: | 出版商: TechSci Research | 英文 185 Pages | 商品交期: 2-3個工作天內

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簡介目錄

全球石油和天然氣產業的巨量資料市場預計將從 2025 年的 132.1 億美元大幅成長至 2031 年的 318.7 億美元，複合年成長率將達到 15.81%。

該市場旨在透過高度聚合和分析來自地震探勘、鑽井測井和生產設施等來源的大量結構化和非結構化數據，最佳化關鍵營運決策。該市場的主要成長要素包括：預測性維護對於預防意外設備故障的重要性、提高儲存採收率的努力以及降低開採成本的挑戰。根據國際能源總署 (IEA) 預測，到 2024 年，全球上游油氣產業的投資將達到 5,700 億美元，這凸顯了營運商必須透過數據驅動的效率提升來保護和最大化利用巨額資本。

市場概覽
預測期 2027-2031
市場規模：2025年 132.1億美元
市場規模：2031年 318.7億美元
複合年成長率：2026-2031年 15.81%
成長最快的細分市場 結構化
最大的市場 北美洲

市場促進因素

對提高營運效率和最佳化成本的日益成長的需求是推動石油和天然氣行業加速採用巨量資料分析的主要動力。隨著可開採蘊藏量的減少，營運商被迫利用複雜的演算法來簡化複雜的鑽井和生產流程，從而減少資本投資並最大限度地提高現有資產的產出。這些提高營運效率的努力使得企業越來越依賴人工智慧 (AI) 平台，這些平台可以處理地質和營運數據以支援即時決策。例如，2025 年 11 月的報告指出，雪佛龍公司的 AI 驅動平台 APOLO 將二疊紀盆地的鑽井和完井效率提高了 30% 以上。同時，物聯網感測器的廣泛普及以及由此產生的大量數據正在改變行業格局，為巨量資料市場的擴張創造了有利環境。現代油田密集部署了各種測量儀器，持續傳輸Terabyte的性能數據。因此，需要強大的分析解決方案來解讀這些訊息，從而獲得預測性洞察並實現有效的資產管理。從領先服務供應商的財務表現可以看出，數位轉型的規模顯而易見。 SLB 2024 年全年數位化收入達 24.4 億美元，年增 20%；貝克休斯工業與能源技術部門（涵蓋數位化解決方案）2024 年的訂單達 130 億美元。

市場挑戰

全球油氣產業巨量資料市場成長面臨的主要障礙之一是難以將現代分析能力整合到現有的成熟傳統基礎設施中。這種互通性的缺失通常會導致嚴重的資料孤島，關鍵的營運資訊仍然孤立地儲存在老舊的監控與資料收集（SCADA）系統和分散的部門資料庫中。因此，能源公司難以整合用於高階預測建模和即時決策的高品質、一致性資料集，而這些正是定義市場價值主張的關鍵所在。缺乏統一的資料架構，巨量資料在最佳化開採流程和降低成本方面的潛力將受到嚴重阻礙，迫使營運商依賴零散的信息，而非對其資產的整體情況。這種分散化直接阻礙了市場發展勢頭，導致數位提案進程停滯不前，並延遲了數據項目的投資回報。當營運商無法將新的數位平台與使用了數十年的設備無縫連接時，實施巨量資料解決方案就成為一項極其複雜且資源密集的任務。根據石油工程師協會 (SPE) 2024 年的一項調查，約 37% 的能源行業專業人士認為其所在機構“數字化落後”，並指出與更靈活的競爭對手相比，無法有效實現工作流程的現代化和整合是主要障礙。因此，該行業的龐大群體未能充分採用巨量資料分析，導致目標市場有限，並減緩了該行業整體技術採用速度。

市場趨勢

數位雙胞胎技術在資產模擬領域的廣泛應用，正從根本上改變營運商管理複雜海上和陸上設施生命週期的方式。與依賴孤立感測器資料的傳統監控不同，數位雙胞胎能夠創建動態虛擬副本，整合即時運行資料和工程模型，從而模擬未來性能並預測結構風險。這項技術使工程師能夠在實際實施前，在虛擬環境中測試運行調整方案，顯著降低資本密集決策的風險，並延長老舊基礎設施的使用壽命。為了凸顯這項技術的營運規模，《海上能源》雜誌在2025年1月報道稱，BP已確認在全球20個設施部署Aize的數位雙胞胎視覺化軟體，以整合工程和運行數據。此外，隨著監管壓力和氣候變遷計劃的日益增加，產業被迫從估計值轉向精確測量排放數據，數據驅動的永續性和ESG分析正迅速成為一項關鍵的營運支柱。各公司正擴大將衛星影像、無人機勘測和地面感測器網路整合到一個集中式資料湖中，以檢測甲烷洩漏並以極高的精度檢驗碳排放強度。這項轉變對於維持社會認可度以及滿足要求檢驗環境審計的嚴格新報告框架至關重要。為了凸顯這項監測挑戰的規模，GHGSat於2025年4月發布的《2024年甲烷排放報告》顯示，其衛星星系全年在全球範圍內探測到超過2萬個高濃度甲烷羽流，其中54%來自石油和天然氣行業。

目錄

第1章概述

第2章：調查方法

第3章執行摘要

第4章：客戶心聲

第5章：全球石油天然氣巨量資料市場展望

  • 市場規模及預測
    • 按金額
  • 市佔率及預測
    • 按組件（硬體、軟體、服務）
    • 依用途（上游、中游、下游）
    • 依資料類型（結構化、非結構化、半結構化）
    • 按地區
    • 按公司（2025 年）
  • 市場地圖

第6章：北美油氣產業巨量資料市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 北美洲：國別分析
    • 美國
    • 加拿大
    • 墨西哥

第7章：歐洲油氣產業巨量資料市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 歐洲：國別分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙

第8章：亞太地區石油天然氣產業巨量資料市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 亞太地區：國別分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

第9章：中東和非洲石油天然氣產業巨量資料市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 中東與非洲：國別分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非

第10章：南美石油天然氣產業巨量資料市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 南美洲：國別分析
    • 巴西
    • 哥倫比亞
    • 阿根廷

第11章 市場動態

  • 促進因素
  • 任務

第12章 市場趨勢與發展

  • 併購
  • 產品發布
  • 近期趨勢

第13章：全球石油天然氣巨量資料市場：SWOT分析

第14章：波特五力分析

  • 產業競爭
  • 新進入者的潛力
  • 供應商的議價能力
  • 顧客權力
  • 替代品的威脅

第15章 競爭格局

  • Accenture PLC
  • Cisco Systems, Inc.
  • Dell Technologies Inc
  • Hewlett Packard Enterprise Company
  • International Business Machines Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • SAP SE
  • SAS Institute Inc.
  • Teradata Corporation
  • Hitachi Vantara LLC

第16章 策略建議

第17章：關於研究公司及免責聲明

簡介目錄
Product Code: 15336

The Global Big Data in Oil & Gas Market is projected to expand significantly, from USD 13.21 Billion in 2025 to USD 31.87 Billion by 2031, demonstrating a 15.81% Compound Annual Growth Rate (CAGR). This market involves the advanced aggregation and analysis of vast structured and unstructured datasets, obtained from sources like seismic surveys, drilling logs, and production machinery, all geared towards optimizing key operational decisions. The market's primary support stems from the crucial need for predictive maintenance to prevent unplanned equipment failures, the push for enhanced reservoir recovery rates, and the imperative to reduce extraction costs. According to the International Energy Agency, the USD 570 billion global upstream oil and gas investment in 2024 underscores the immense capital that operators must protect and maximize through data-driven efficiency.

Market Overview
Forecast Period2027-2031
Market Size 2025USD 13.21 Billion
Market Size 2031USD 31.87 Billion
CAGR 2026-203115.81%
Fastest Growing SegmentStructured
Largest MarketNorth America

Market Driver

The increasing demand for operational efficiency and cost optimization serves as the primary impetus for the accelerated adoption of big data analytics in the oil and gas sector. As easily accessible reserves diminish, operators are compelled to utilize advanced algorithms to streamline complex drilling and production workflows, thereby lowering capital expenditures and maximizing output from existing assets. This drive for leaner operations increasingly relies on artificial intelligence platforms that process geological and operational data to inform real-time decision-making; for example, Chevron's AI-driven APOLO platform improved drill and completion efficiencies by over 30% in the Permian Basin, as reported in November 2025. Concurrently, the widespread proliferation of IoT sensors and the subsequent generation of massive data are reshaping the industry's technological landscape, creating a fertile environment for big data market expansion. Modern oilfields are densely instrumented, continuously transmitting terabytes of performance data, which necessitates robust analytics solutions to interpret this information for predictive insights and effective asset management. The scale of this digital transformation is evident in financial results from leading service providers; SLB's full-year digital revenue grew 20% year-over-year to reach USD 2.44 billion in 2024, and Baker Hughes' Industrial & Energy Technology segment, encompassing digital solutions, recorded USD 13.0 billion in orders for 2024.

Market Challenge

A formidable barrier to the growth of the Global Big Data in Oil & Gas Market is the technical difficulty associated with integrating modern analytics with existing, entrenched legacy infrastructure. This lack of interoperability typically results in significant data silos, where crucial operational information remains isolated within aging supervisory control and data acquisition (SCADA) systems or fragmented departmental databases. Consequently, energy companies struggle to consolidate the cohesive, high-quality datasets required for the advanced predictive modeling and real-time decision-making that define the market's value proposition. Without a unified data architecture, the full potential of big data to optimize extraction processes and reduce costs is severely bottlenecked, compelling operators to rely on fragmented insights rather than a holistic view of their assets. This fragmentation directly impedes market momentum by stalling digital transformation initiatives and delaying the return on investment for data projects. When operators cannot seamlessly connect new digital platforms with decades-old machinery, the implementation of big data solutions becomes prohibitively complex and resource-intensive. According to the Society of Petroleum Engineers (SPE) in 2024, approximately 37% of energy industry professionals identified their organizations as "digital laggards," primarily citing the inability to effectively modernize and integrate workflows as a key hurdle compared to more agile competitors. This substantial segment of the industry is thus prevented from fully adopting big data analytics, thereby limiting the total addressable market and decelerating the overall pace of technological deployment within the sector.

Market Trends

The widespread adoption of Digital Twin Technology for Asset Simulation is fundamentally transforming how operators manage the lifecycle of complex offshore and onshore facilities. Unlike traditional monitoring that relies on isolated sensor feeds, digital twins create dynamic virtual replicas that integrate real-time operational data with engineering models to simulate future performance and predict structural risks. This capability enables engineers to test operational adjustments in a virtual environment before physical implementation, significantly de-risking capital-intensive decisions and extending the useful life of aging infrastructure. Reinforcing the operational scale of this technology, BP confirmed the deployment of Aize digital twin visualization software across twenty of its global facilities to unify engineering and operational data, as reported by Offshore Energy in January 2025. Furthermore, the emergence of Data-Driven Sustainability and ESG Analytics is rapidly becoming a critical operational pillar, driven by increasing regulatory pressure and climate commitments that are forcing the industry to transition from estimated to precisely measured emissions data. Companies are increasingly integrating satellite imagery, drone surveys, and ground-sensor networks into centralized data lakes to detect fugitive methane leaks and verify carbon intensity with granular precision. This shift is essential for maintaining a social license to operate and meeting stringent new reporting frameworks that demand verifiable environmental audits. Highlighting the magnitude of this monitoring challenge, GHGSat's April 2025 '2024 Methane Emissions Report' indicated that the firm's satellite constellation detected over 20,000 high-emission methane plumes globally during the year, with the oil and gas sector accounting for 54% of these detected events.

Key Market Players

  • Accenture PLC
  • Cisco Systems, Inc.
  • Dell Technologies Inc
  • Hewlett Packard Enterprise Company
  • International Business Machines Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • SAP SE
  • SAS Institute Inc.
  • Teradata Corporation
  • Hitachi Vantara LLC

Report Scope

In this report, the Global Big Data in Oil & Gas Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Big Data in Oil & Gas Market, By Components

  • Hardware
  • Software
  • Service

Big Data in Oil & Gas Market, By Application

  • Upstream
  • Midstream
  • Downstream

Big Data in Oil & Gas Market, By Data Type

  • Structured
  • Unstructured
  • Semi-Structured

Big Data in Oil & Gas Market, By Region

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East & Africa
    • South Africa
    • Saudi Arabia
    • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Big Data in Oil & Gas Market.

Available Customizations:

Global Big Data in Oil & Gas Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Big Data in Oil & Gas Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Components (Hardware, Software, Service)
    • 5.2.2. By Application (Upstream, Midstream, Downstream)
    • 5.2.3. By Data Type (Structured, Unstructured, Semi-Structured)
    • 5.2.4. By Region
    • 5.2.5. By Company (2025)
  • 5.3. Market Map

6. North America Big Data in Oil & Gas Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Components
    • 6.2.2. By Application
    • 6.2.3. By Data Type
    • 6.2.4. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Big Data in Oil & Gas Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Components
        • 6.3.1.2.2. By Application
        • 6.3.1.2.3. By Data Type
    • 6.3.2. Canada Big Data in Oil & Gas Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Components
        • 6.3.2.2.2. By Application
        • 6.3.2.2.3. By Data Type
    • 6.3.3. Mexico Big Data in Oil & Gas Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Components
        • 6.3.3.2.2. By Application
        • 6.3.3.2.3. By Data Type

7. Europe Big Data in Oil & Gas Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Components
    • 7.2.2. By Application
    • 7.2.3. By Data Type
    • 7.2.4. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Big Data in Oil & Gas Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Components
        • 7.3.1.2.2. By Application
        • 7.3.1.2.3. By Data Type
    • 7.3.2. France Big Data in Oil & Gas Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Components
        • 7.3.2.2.2. By Application
        • 7.3.2.2.3. By Data Type
    • 7.3.3. United Kingdom Big Data in Oil & Gas Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Components
        • 7.3.3.2.2. By Application
        • 7.3.3.2.3. By Data Type
    • 7.3.4. Italy Big Data in Oil & Gas Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Components
        • 7.3.4.2.2. By Application
        • 7.3.4.2.3. By Data Type
    • 7.3.5. Spain Big Data in Oil & Gas Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Components
        • 7.3.5.2.2. By Application
        • 7.3.5.2.3. By Data Type

8. Asia Pacific Big Data in Oil & Gas Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Components
    • 8.2.2. By Application
    • 8.2.3. By Data Type
    • 8.2.4. By Country
  • 8.3. Asia Pacific: Country Analysis
    • 8.3.1. China Big Data in Oil & Gas Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Components
        • 8.3.1.2.2. By Application
        • 8.3.1.2.3. By Data Type
    • 8.3.2. India Big Data in Oil & Gas Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Components
        • 8.3.2.2.2. By Application
        • 8.3.2.2.3. By Data Type
    • 8.3.3. Japan Big Data in Oil & Gas Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Components
        • 8.3.3.2.2. By Application
        • 8.3.3.2.3. By Data Type
    • 8.3.4. South Korea Big Data in Oil & Gas Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Components
        • 8.3.4.2.2. By Application
        • 8.3.4.2.3. By Data Type
    • 8.3.5. Australia Big Data in Oil & Gas Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Components
        • 8.3.5.2.2. By Application
        • 8.3.5.2.3. By Data Type

9. Middle East & Africa Big Data in Oil & Gas Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Components
    • 9.2.2. By Application
    • 9.2.3. By Data Type
    • 9.2.4. By Country
  • 9.3. Middle East & Africa: Country Analysis
    • 9.3.1. Saudi Arabia Big Data in Oil & Gas Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Components
        • 9.3.1.2.2. By Application
        • 9.3.1.2.3. By Data Type
    • 9.3.2. UAE Big Data in Oil & Gas Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Components
        • 9.3.2.2.2. By Application
        • 9.3.2.2.3. By Data Type
    • 9.3.3. South Africa Big Data in Oil & Gas Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Components
        • 9.3.3.2.2. By Application
        • 9.3.3.2.3. By Data Type

10. South America Big Data in Oil & Gas Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Components
    • 10.2.2. By Application
    • 10.2.3. By Data Type
    • 10.2.4. By Country
  • 10.3. South America: Country Analysis
    • 10.3.1. Brazil Big Data in Oil & Gas Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Components
        • 10.3.1.2.2. By Application
        • 10.3.1.2.3. By Data Type
    • 10.3.2. Colombia Big Data in Oil & Gas Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Components
        • 10.3.2.2.2. By Application
        • 10.3.2.2.3. By Data Type
    • 10.3.3. Argentina Big Data in Oil & Gas Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Components
        • 10.3.3.2.2. By Application
        • 10.3.3.2.3. By Data Type

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

  • 12.1. Merger & Acquisition (If Any)
  • 12.2. Product Launches (If Any)
  • 12.3. Recent Developments

13. Global Big Data in Oil & Gas Market: SWOT Analysis

14. Porter's Five Forces Analysis

  • 14.1. Competition in the Industry
  • 14.2. Potential of New Entrants
  • 14.3. Power of Suppliers
  • 14.4. Power of Customers
  • 14.5. Threat of Substitute Products

15. Competitive Landscape

  • 15.1. Accenture PLC
    • 15.1.1. Business Overview
    • 15.1.2. Products & Services
    • 15.1.3. Recent Developments
    • 15.1.4. Key Personnel
    • 15.1.5. SWOT Analysis
  • 15.2. Cisco Systems, Inc.
  • 15.3. Dell Technologies Inc
  • 15.4. Hewlett Packard Enterprise Company
  • 15.5. International Business Machines Corporation
  • 15.6. Microsoft Corporation
  • 15.7. Oracle Corporation
  • 15.8. SAP SE
  • 15.9. SAS Institute Inc.
  • 15.10. Teradata Corporation
  • 15.11. Hitachi Vantara LLC

16. Strategic Recommendations

17. About Us & Disclaimer