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市場調查報告書

石油、天然氣大數據市場:支出種類 、應用領域分類支出預測

Big Data in Oil & Gas Market 2015-2025: Forecasts by Spending Type (Hardware, Software, Services & Salaries) and Application Area (Upstream, Midstream, Downstream & Administration)

出版商 Visiongain Ltd 商品編碼 339744
出版日期 內容資訊 英文 251 Pages
商品交期: 最快1-2個工作天內
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石油、天然氣大數據市場:支出種類 、應用領域分類支出預測 Big Data in Oil & Gas Market 2015-2025: Forecasts by Spending Type (Hardware, Software, Services & Salaries) and Application Area (Upstream, Midstream, Downstream & Administration)
出版日期: 2015年09月17日 內容資訊: 英文 251 Pages
簡介

石油、天然氣產業與金融行銷等其他產業相比引進大數據的時間較晚。但上游部門已開始接受大數據的操作改善、預計市場今後10年將安定成長。石油、天然氣產業大數據支出預計2015年可達總額35億美元。

本報告針對石油、天然氣產業大數據市場進行調查、提供石油、天然氣大數據預測、石油企業目前大數據支出詳情、與其他產業相比較晚引進大數據的理由、石油價格對石油、天然氣大數據影響、現在使用大數據的石油、天然氣企業、主要企業、與2次市場成長預測分析。

第1章 報告概要

第2章 石油、天然氣市場大數據介紹

  • 大數據大型生態
  • 石油、天然氣使用大數據產品
  • 全球資料領域規模
  • 大數據與大數據分析定義
  • 各大數據種類定義
  • 其他主要大數據用語、程式定義
  • 大數據分析商業範例
  • 大數據專家:數據學者登場
  • 大數據處理管線
  • 大數據分析:主要技術
  • 石油、天然氣大數據

第3章 石油、天然氣市場全球大數據市場

  • 石油、天然氣市場全球大數據
  • 石油、天然氣產業大數據引進率
  • 石油、天然氣產業大數據使用促進因素、阻礙因素
  • 石油、天然氣產業大數據引進影響與因素
  • 石油、天然氣產業大數據使用範例
  • 使用大數據產品的石油、天然氣企業列表

第4章 石油、天然氣大數據分析:產品種類分類

  • 全球收益與薪資的公司內支出預測
    • 收益
    • 薪資
    • 市場
    • 分析
  • 全球收益預測與2次市場預測
    • 硬體收益預測
    • 軟體收益預測
    • 服務收益預測
    • 薪資 (公司內支出) 預測

第5章 SWOT分析

  • 石油、天然氣上游大數據預測
  • 石油、天然氣中游大數據預測
  • 石油、天然氣下游大數據預測
  • 石油、天然氣管理大數據預測

第6章 SWOT分析

第7章 專家見解

  • Talend
  • Trifacta
  • Visier
  • Ayata
  • Datawatch Corporation
  • Datameer
  • DDN
  • TIBCO
  • Qlik

第8章 主要企業

  • IBM
  • HP
  • Teradata
  • Dell
  • Oracle
  • SAP
  • EMC
  • Cisco Systems
  • PwC
  • Microssoft
  • Accenture
  • Palantir
  • Fusion-io
  • SAS Institute
  • Splunk
  • Deloitte
  • NetApp
  • 日立
  • Opera Solutions
  • CSC
  • 大數據市場其他企業
  • 有石油、天然氣經驗的小型大數據企業

第9章 結論、建議

  • 結論與建議
  • 給石油、天然氣企業的結論與建議
  • 給大數據企業結論與建議

第10章 用語集

圖表

目錄
Product Code: ENE0032

The oil & gas industry has been slow to adopt big data when compared to other industries, such as finance or marketing. However, the market is set for strong growth over the next ten years as oil & gas companies begin to come to terms with how big data can improve their operations, particularly in the upstream sector. Visiongain assesses that spending by the oil & gas industry on big data will total $3.51bn in 2015.

Visiongain's 250 page report will ensure that you keep informed and ahead of your competitors. Gain that competitive advantage.

The report will answer questions such as:

  • What are the prospects for big data in oil & gas?
  • How much is currently being spent by oil & gas companies on big data and how are they using it?
  • Why has oil & gas been slower to adopt big data than other industries?
  • How are oil prices affecting the market for big data in oil & gas?
  • Which oil & gas companies are currently using big data?
  • Who are the key players offering big data hardware, software and services?
  • Which application submarket (upstream, midstream and downstream) will see the greatest growth over the next ten years?

How will you benefit from this report?

  • This report you will keep your knowledge base up to speed. Don't get left behind
  • This report will allow you to reinforce strategic decision-making based upon definitive and reliable market data
  • You will learn how to exploit new technological trends
  • You will be able to realise your company's full potential within the market
  • You will better understand the competitive landscape and identify potential new business opportunities and partnerships

Five reasons why you must order and read this report today:

1) The report provides in-depth analysis and spending forecasts for four big data in oil & gas submarkets, broken down by spending type, from 2015-2025:

  • Hardware
  • Software
  • Services
  • Salaries

image1

2) The report also offers in-depth analysis and spending forecasts for four big data in oil & gas submarkets, broken down by application area, from 2015-2025:

  • Upstream
    • Conventional
    • Unconventional
  • Midstream
  • Downstream
  • Administration

image2

3) The report also includes transcripts of 9 in-depth interviews with companies involved in the market for big data in oil & gas, providing expert insight alongside visiongain's forecasts and analysis:

  • DDN
  • Tibco
  • Datameer
  • Datawatch
  • Visier
  • Trifacta
  • Ayata
  • Talend
  • Qlik

4) Understand the market from both the demand and supply side, with analysis, forecasts and recommendations tailored towards:

  • Companies supplying, or thinking of supplying, big data services and products to the oil & gas industry
  • Oil & gas companies investing in, or thinking of investing in, big data services and products

5) View profiles of the leading companies in the field of big data, details of companies offering big data solutions to the oil & gas industry, and information on the oil & gas companies currently utilising big data.

Competitive advantage

This independent, 250 page report guarantees you will remain better informed than your competitors. With 118 tables and figures examining the big data in oil & gas market space, the report gives you an immediate, one-stop breakdown of your market PLUS spending forecasts, as well as analysis, from 2015-2025, keeping your knowledge that one step ahead of your rivals.

Who should read this report?

  • Companies offering big data products and services
  • Oil & gas companies contemplating how big data can improve their business
  • CEOs
  • COOs
  • CIOs
  • Business development managers
  • Marketing managers
  • Technologists
  • Suppliers
  • Investors
  • Banks
  • Government agencies
  • Contractors

Don't miss out.

This report is essential reading for you or anyone in the oil & gas sector with an interest in big data or for those offering big data products or services. Purchasing this report today will help you to recognise those important market opportunities and understand the possibilities there. Order the Big Data in Oil & Gas 2015-2025 report now. We look forward to receiving your order.

Table of Contents

1. Report Overview

  • 1.1 Global Big Data in Oil and Gas Market Overview
  • 1.2 Market Segmentation
  • 1.3 Why Read This Report?
  • 1.4 How This Report Delivers
  • 1.5 Key Questions Answered by This Analytical Report Include:
  • 1.6 Who is This Report For?
  • 1.7 Market Definitions
    • 1.7.1 Big Data
    • 1.7.2 Oil and Gas Industry
    • 1.7.3 Subdivisions within the Global Oil and Gas Industry
  • 1.8 Methodology
  • 1.9 Frequently Asked Questions (FAQ)
  • 1.10 Associated Visiongain Reports
  • 1.11 About Visiongain

2. Introduction to the Big Data in Oil & Gas Market

  • 2.1 The Larger Big Data Ecosystem
  • 2.2 Using Big Data Products in Oil and Gas
  • 2.3 Size of the Worldwide Data Universe
  • 2.4 Defining the Terms Big Data and Big Data Analytics
  • 2.5 Defining Different Types of Big Data
  • 2.6 Defining Other Key Big Data Terms and Programs
  • 2.7 Business Case for Big Data Analytics (In all industries)
    • 2.7.1 Enterprise Application for Big Data Analytics
    • 2.7.2 Big Data as a Catalyst for Innovation & Productivity
    • 2.7.3 Trust Issues & Security Concerns with Regards to Big Data Outsourcing
    • 2.7.4 Challenges of Big Data
  • 2.8 Big Data Professionals: Rise of the Data Scientist
  • 2.9 Big Data Processing Pipeline
    • 2.9.1 Big Data Processing Pipeline - Major Steps
    • 2.9.2 Big Data Processing Pipeline - Common Challenges
  • 2.10 Big Data Analytics - Key Technologies
    • 2.10.1 Apache Hadoop
    • 2.10.2 NoSQL Database
  • 2.11 Big Data in Oil and Gas

3. The Global Big Data in Oil & Gas Market

  • 3.1 Global Big Data in Oil and Gas Forecast 2015-2025
  • 3.2 Rate of Adoption of Big Data into the Oil and Gas Industry
  • 3.3 Drivers and Restraints of the Use of Big Data in the Oil and Gas Industry
    • 3.3.1 Drivers of the Use of Big Data
      • 3.3.1.1 Expanding Volume, Variety and Variability of Data Produced in the Oil and Gas Industry
      • 3.3.1.2 Expansion in the Production and Exploration for Unconventional Oil and Gas
      • 3.3.1.3 Growing Number of Leading Players Utilizing this Technology
      • 3.3.1.4 Health, Safety, and Environmental Regulation
      • 3.3.1.5 Expanding Availability of Big Data Trained Workers
      • 3.3.1.6 High Costs for Conventional Storage
      • 3.3.1.6 Open Data Movement
    • 3.3.2 Restraints on the Use of Big Data in the Oil and Gas Industry
      • 3.3.2.1 Understanding the Value in Big Data for Oil and Gas Companies
      • 3.3.2.2 Internal Silos Act As Barrier to Adoption
      • 3.3.2.3 Legal Issues Around Data
      • 3.3.2.4 Impacts of the Baby Boomer Generation in C-Level Positions
      • 3.3.2.5 Company Culture and the "Race to be Second"
      • 3.3.2.6 Data Security Concerns
      • 3.3.2.7 High Competition for New Data Scientists and Data Science Skills Shortage
  • 3.4 Other Factors Influencing the Adoption of Big Data in the Oil and Gas Industry
    • 3.4.1 Oil Price Collapse Analysis and Forecast
    • 3.4.2 Supply-Side Factors
      • 3.4.2.1 Tight Oil
      • 3.4.2.2 Libya
      • 3.4.2.3 OPEC
    • 3.4.3 Demand-Side Factors
      • 3.4.3.1 Chinese and Indian Growth
      • 3.4.3.2 Western Stagnation
    • 3.4.4 Other Major Variables that Impact the Oil Price
      • 3.4.4.1 North Africa
      • 3.4.4.2 Russia
      • 3.4.4.3 US Shale Resilience
      • 3.4.4.4 Iraq
      • 3.4.4.5 International Incidents
      • 3.4.4.6 Iran
    • 3.4.5 Visiongain's Oil Price Assumptions and Forecast
    • 3.4.6 Multiple Impacts of the Oil Price Collapse on Big Data Adoption
  • 3.5 Use Cases for Big Data in the Oil and Gas Industry
    • 3.5.1 The Internet of Things and the Digital Oilfield
    • 3.5.2 Specific Uses
      • 3.5.2.1 Upstream
      • 3.5.2.2 Exploration & Production
      • 3.5.2.3 Conventional
      • 3.5.2.4 Unconventional
      • 3.5.2.5 Midstream
      • 3.5.2.6 Downstream
      • 3.5.6.7 Administration
  • 3.6 List of Oil and Gas Companies Using Big Data Products

4. Big Data in Oil and Gas Breakdown by Product Type

  • 4.1 Global Revenues and In-house Spending on Salaries Forecast
    • 4.1.1 Revenues
    • 4.1.2 Salaries
    • 4.1.3 The Market
    • 4.1.4 Analysis
  • 4.2 Global Revenues Forecast and Submarket Forecasts 2015-2025
    • 4.2.1 Hardware Revenues Forecast 2015-2025
    • 4.2.2 Software Revenues Forecast 2015-2025
    • 4.2.3 Services Revenue Forecast 2015-2025
    • 4.2.4 Salaries (In-House Spending) Forecast 2015-2025

5. Big Data in Oil and Gas Breakdown by Usage Type

  • 5.1 Big Data in Oil & Gas Upstream Forecast 2015-2025
    • 5.1.1 Big Data in Oil & Gas Conventional Upstream Forecast 2015-2025
    • 5.1.2 Big Data in Oil & Gas Unconventional Upstream Forecast 2015-2025
  • 5.2 Big Data in Oil & Gas Midstream Forecast 2015-2025
  • 5.3 Big Data in Oil & Gas Downstream Forecast 2015-2025
  • 5.4 Big Data in Oil & Gas Administration Forecast 2015-2025

6. SWOT Analysis

7. Expert Opinion

  • 7.1 Talend
    • 7.1.1 About Talend and Data Integration
    • 7.1.2 Examples of Big Data Usage in Renewable Energy
    • 7.1.3 Three Key Areas of Big Data Usage in Oil and Gas Companies
    • 7.1.4 Emerging Collaboration Between Companies Around Data
    • 7.1.5 Restraints to Adoption of Big Data Products in Oil and Gas
    • 7.1.6 Impact of the Oil Price Collapse on the Adoption of Big Data
    • 7.1.7 Staffing and Other Additional Costs Associated with Adopting Big Data
  • 7.2 Trifacta
    • 7.2.1 The Development of Big Data, Business Intelligence, and Trifacta
    • 7.2.2 Applications for Big Data in Oil and Gas
    • 7.2.3 Examples of Big Data Analytics for Oil and Gas
    • 7.2.4 Where are Oil and Gas Companies Using Big Data Technology?
    • 7.2.5 Typical Size of Oil and Gas Company Presently Employing Big Data Products
    • 7.2.6 Data Ownership Issues
    • 7.2.7 Impact of the Oil Price Collapse on Adoption of Big Data
    • 7.2.8 Rate of Adoption of Big Data in Oil and Gas
    • 7.2.9 Big Data Adoption in Other Industries
  • 7.3 Visier
    • 7.3.1 Visier's Big Data HR Solutions
    • 7.3.2 The Great Crew Change and Managing Human Capital in Oil and Gas
    • 7.3.3 Examples of Big Data Application in Oil and Gas HR
    • 7.3.4 Impact of the Oil Price Collapse on Big Data Uptake in HR in Oil and Gas
    • 7.3.5 Drivers, Restraints, and Next Steps for Big Data in Oil and Gas HR
  • 7.4 Ayata
    • 7.4.1 Ayata's Involvement in the Big Data Industry
    • 7.4.2 Ayata's Offerings to the Oil & Gas Industry
    • 7.4.3 The Most Prominent Sources of Data Used by Oil & Gas Companies
    • 7.4.4 The Types of Oil & Gas Customers Using Big Data
    • 7.4.5 The Impact of the Oil Price Fall on Big Data Spending
    • 7.4.6 The Attitude of the Oil & Gas Industry Towards Big Data
  • 7.5 Datawatch Corporation
    • 7.5.1 Datawatch's Involvement in the Big Data Industry
    • 7.5.2 The Users of Big Data in the Oil & Gas Industry
    • 7.5.3 The Impact of the Oil Price Fall on Big Data Spending
    • 7.5.4 Challenges to the Implementation of Big Data in Oil & Gas
    • 7.5.5 Datawatch's Products for the Oil & Gas Market
  • 7.6 Datameer
    • 7.6.1 Datawatch's Involvement in the Big Data Industry
    • 7.6.2 Upstream vs. Midstream vs. Downstream
    • 7.6.3 Challenges to the Implementation of Big Data in Oil & Gas
    • 7.6.4 Do Energy Companies Need to Reskill Employees to Use Big Data?
    • 7.6.5 The Importance of Oil & Gas to Datameer's Business
    • 7.6.6 The Impact of the Oil Price Fall on Big Data Adoption
  • 7.7 DDN
    • 7.7.1 DDN's Involvement in the Market for Big Data in Oil & Gas
    • 7.7.2 The Impact of the Oil Price Fall on Big Data Spending
    • 7.7.3 The Types of Oil & Gas Companies Embracing Big Data
    • 7.7.4 Issues with Sharing Data Internally
    • 7.7.5 Limitations with Using Hadoop
    • 7.7.6 Case Studies for Successful Big Data Use in Oil & Gas
  • 7.8 TIBCO
    • 7.8.1 TIBCO's Involvement in the Big Data Industry
    • 7.8.2 Upstream vs. Midstream vs. Downstream
    • 7.8.3 The Impact of the Oil Price Fall on Big Data Spending
    • 7.8.4 Fast Data and the Digital Nervous System
    • 7.8.5 Defining Big Data
    • 7.8.6 Geographical Interest in Big Data
    • 7.8.7 Prospects for Big Data in Oil & Gas
    • 7.8.8 Restraints on Big Data Investment by Oil & Gas Companies
    • 7.8.9 Differences Between Big Data Adoption in Oil & Gas Compared to Other Industries
  • 7.9 Qlik
    • 7.9.1 Qlik's Involvement in the Big Data Industry
    • 7.9.2 Upstream vs. Midstream vs. Downstream
    • 7.9.3 The Types of Oil & Gas Companies Using Big Data
    • 7.9.4 Anticipated Developments Over the Next Ten Years
    • 7.9.5 The Impact of the Oil Price Fall on Big Data
    • 7.9.6 The Importance of Oil & Gas to Qlik's Business
    • 7.9.7 Restraints on Big Data Investment by Oil & Gas Companies
    • 7.9.8 Do Energy Companies Need to Reskill Employees to Use Big Data?

8. Leading Companies in Big Data Overall, and Leading Companies in Big Data and Oil and Gas

  • 8.1 IBM Company Overview
    • 8.1.1 IBM Smart Analytics System
  • 8.2 HP Company Overview
  • 8.3 Teradata Company Overview
    • 8.3.1 Teradata Big Data Analytics Offering - Teradata Unified Data Architecture
  • 8.4 Dell Company Overview
    • 8.4.1 Kitenga Analytics Suite
  • 8.5 Oracle Company Overview
    • 8.5.1 Oracle Big Data Analytics Solution
  • 8.6 SAP Company Overview
    • 8.6.1 SAP Big Data Analytics Offering
  • 8.7 EMC Company Overview
    • 8.7.1 EMC Products and Services
  • 8.8 Cisco Systems Company Overview
  • 8.9 PwC Company Overview
  • 8.10 Microsoft Company Overview
    • 8.10.1 Microsoft Big Data Analytics - Offerings and Advantages
  • 8.11 Accenture Company Overview
    • 8.11.1 Accenture Big Data Offering
    • 8.11.2 Accenture Big Data Services
  • 8.12 Palantir Technologies Company Overview
    • 8.12.1 Palantir Technologies Big Data Focus
    • 8.12.2 Palantir Products
    • 8.12.3 Palantir Customers and Focus
    • 8.12.4 Palantir Big Data Analytics Services
  • 8.13 Fusion-io Company Overview
    • 8.13.1 Fusion-io Customers and Market Standing
  • 8.14 SAS Institute Company Overview
    • 8.14.1 SAS Analytics Portfolio Analysis
  • 8.15 Splunk Company Overview
  • 8.16 Deloitte Company Overview
    • 8.16.1 Big Data Analytics Offerings
  • 8.17 NetApp Company Overview
    • 8.17.1 NetApp Open Solution for Hadoop
  • 8.18 Hitachi Company Overview
    • 8.18.1 Hitachi Big Data Analytics Offering
  • 8.19 Opera Solutions Company Overview
    • 8.19.1 Opera Solutions Big Data Analytics Offerings
  • 8.20 CSC Company Overview
    • 8.20.1 CSC Big Data Analytics Offerings Analysis
  • 8.21 Additional Players in the Big Data Market
  • 8.22 Smaller Big Data Companies with Experience of Oil and Gas Companies

9. Conclusions and Recommendations

  • 9.1 General Conclusions and Recommendations
  • 9.2 Conclusions and Recommendations for Oil and Gas Companies
  • 9.3 Conclusions and Recommendations for Big Data Companies

10. Glossary

List of Tables

  • Table 1.1 Global Market for Big Data in Oil and Gas 2015-2025 (CAPEX and OPEX) ($m, AGR %, CAGR %)
  • Table 1.2 Example Breakdown of Big Data Usage in Oil and Gas by Type ($m, AGR %)
  • Table 2.1 Key Variables in Defining Big Data
  • Table 2.2 Key Types of Big Data
  • Table 2.3 Big Data Challenges
  • Table 2.4 Big Data Processing Pipeline - Major Steps
  • Table 2.5 Big Data Processing Pipeline - Common Challenges
  • Table 2.6 Apache Hadoop Modules
  • Table 2.7 Apache Hadoop Strengths & Limitations
  • Table 2.8 NoSQL vs. SQL Database Summary
  • Table 2.9 Additional Big Data Technologies
  • Table 3.1 Global Big Data in Oil and Gas Market Forecast 2015-2025 (CAPEX and OPEX) ($m, AGR %)
  • Table 3.2 Global Big Data in Oil and Gas Market Forecast 2015-2025 Broken Down by Spending Type (Hardware, Software, Services, Salaries) (CAPEX and OPEX) ($m, AGR %)
  • Table 3.3 Global Big Data in Oil and Gas Market 2015-2025 Broken Down by Usage Type (Upstream, Midstream, Downstream, Administration) (CAPEX and OPEX) ($m, AGR (%)
  • Table 3.4 Drivers and Restraints in the Big Data in Oil & Gas Market
  • Table 3.5 Visiongain's Anticipated WTI Oil Price, 2015, 2016, 2017, 2018-2021, 2022-2025 ($/bbl)
  • Table 4.1 Global Big Data Company Revenues from Business with Oil and Gas Companies and Internal Oil Company Spending on Employees with Big Data Skills-Sets 2015-2025 ($m, AGR %)
  • Table 4.2 Global Big Data Company Revenues from Business with Oil and Gas Companies 2015-2025 (Total, Hardware, Software, Services) ($m, AGR %)
  • Table 4.3 Global Big Data Company Hardware Revenues from Business with Oil and Gas Companies 2015-2025 ($m, AGR %, CAGR %)
  • Table 4.4 Global Big Data Company Software Revenues from Business with Oil and Gas Companies 2015-2025 ($m, AGR %, CAGR %)
  • Table 4.5 Global Big Data Company Services Revenues from Business with Oil and Gas Companies 2015-2025 ($m, AGR %, CAGR %)
  • Table 4.6 Oil and Gas Company In-House Salary Spending on Big Data-Skilled Employees 2015-2025 ($m, AGR %, CAGR %)
  • Table 5.1 Big Data in Oil and Gas Market 2015-2025 Broken Down by Usage Type (Upstream, Midstream, Downstream, Administration) (CAPEX and OPEX) ($m, AGR (%)
  • Table 5.2 Big Data in Upstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Table 5.3 Big Data in Conventional Upstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Table 5.4 Big Data in Unconventional Upstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Table 5.5 Big Data in Midstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Table 5.6 Big Data in Downstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Table 5.7 Big Data in Administration Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Table 6.1 SWOT Analysis of Big Data in Oil & Gas
  • Table 8.1 IBM Company Overview 2014 (Total Revenue, HQ, Ticker, Website)
  • Table 8.2 IBM Big Data Platform - Key Capabilities
  • Table 8.3 IBM Big Data Platform - Supporting Services
  • Table 8.4 IBM Smart Analytics System Summary
  • Table 8.5 HP Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.6 HAVEn Key Summary (Advantages, Description)
  • Table 8.7 HAVEn - Technical Specifications
  • Table 8.8 HAVEn Solutions
  • Table 8.9 Teradata Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.10 Teradata Unified Data Architecture
  • Table 8.11 Dell Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.12 Kitenga Analytics Suite - Features and Benefits
  • Table 8.13 Oracle Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.14 SAP Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.15 SAP Big Data Offerings
  • Table 8.16 EMC Company Overview 2014 (Total Revenue, HQ, Ticker, Website)
  • Table 8.17 EMC Big Data Analytics Solutions
  • Table 8.18 EMC Big Data Analytics Solutions
  • Table 8.19 Cisco Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.20 Cisco Big Data Offerings
  • Table 8.21 PwC Company Overview 2014 (Total Revenue, HQ, Ticker, Website)
  • Table 8.22 PwC Big Data Offering
  • Table 8.23 Microsoft Company Overview 2014 (Total Revenue, HQ, Ticker, Website)
  • Table 8.24 Microsoft Big Data Analysis Summary
  • Table 8.25 Accenture Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.26 Accenture Big Data Services
  • Table 8.27 Palantir Technologies Company Overview 2014 (Total Revenue, HQ, Website)
  • Table 8.28 Palantir Big Data Focus
  • Table 8.29 Palantir Products
  • Table 8.30 Palantir Insurance Analytics
  • Table 8.31 Fusion-io Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.32 SAS Institute Company Overview 2014 (Total Revenue, HQ, Ticker, Website)
  • Table 8.33 SAS Analytics Portfolio
  • Table 8.34 Splunk Company Overview 2014 (Total Revenue, HQ, Ticker, Website)
  • Table 8.35 Splunk Big Data Analytics Offerings
  • Table 5.36 Deloitte Company Overview 2014 (Total Revenue, HQ, Contact, Website)
  • Table 8.37 Deloitte's Analytics Services
  • Table 8.38 NetApp Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.39 Hitachi Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.40 Hitachi Big Data Analytics Offering - Features and Benefits
  • Table 8.41 Opera Solutions Company Overview 2014 (Total Revenue, HQ, Website)
  • Table 8.42 Operas Solutions Big Data Analytics Solutions and Services
  • Table 8.43 CSC Company Overview 2014 (Total Revenue, HQ, Ticker, Contact, Website)
  • Table 8.44 CSC Big Data Analytics Offerings
  • Table 8.45 Additional Players in the Overall Big Data Market
  • Table 8.46 Companies Working Specifically with the Oil and Gas Sector

List of Figures

  • Figure 1.1 Big Data in Oil & Gas: Global Market Breakdown
  • Figure 1.2 Big Data in Oil & Gas: Global Use Area Breakdown
  • Figure 2.1 Big Data Processing Pipeline - Major Steps and Common Challenges Diagram
  • Figure 2.2 Big Data Visualisation
  • Figure 3.1 Global Big Data in Oil and Gas Market 2015-2025 (CAPEX and OPEX) ($m, AGR %)
  • Figure 3.2 Global Big Data in Oil and Gas Market 2015-2025 Broken Down by Spending Type (Hardware, Software, Services, Salaries) (CAPEX and OPEX) ($m)
  • Figure 3.3 Global Big Data in Oil and Gas Market 2015-2025 Broken Down by Usage Type (Upstream, Midstream, Downstream, Administration) (CAPEX and OPEX) ($m)
  • Figure 3.4 Illustration of "S-curve" Technology Adoption Trends
  • Figure 3.5 Illustration of Adoption of Technology Model (Diffusion of Innovation) - Adoption Rate and Cumulative Adoption
  • Figure 3.6 Illustration of Adoption of Technology Model (Diffusion of Innovation) including Moore's Chasm - Adoption Rate and Cumulative Adoption
  • Figure 3.7 WTI and Brent Oil Prices 2003-2015 ($/bbl)
  • Figure 3.8 Weekly WTI and Brent Oil Prices (July 2014 - August 2015) ($/bbl)
  • Figure 3.9 Chinese and Indian Annual GDP Growth 2005-2014e (%)
  • Figure 3.10 US Refined Product Consumption January 2014 to June 2015 Four-Week Average (Mbpd)
  • Figure 3.11 Visiongain's Anticipated WTI Oil Price, 2015, 2016, 2017, 2018-2021, 2022-2025 ($/bbl)
  • Figure 4.1 Global Big Data Company Revenues from Business with Oil and Gas Companies and Internal Oil Company Spending on Employees with Big Data Skills-Sets 2015-2025 ($m)
  • Figure 4.2 Global Big Data Company Revenues from Business with Oil and Gas Companies and Internal Oil Company Spending on Employees with Big Data Skills-Sets Comparative Spending, 2015, 2020, 2025 (%)
  • Figure 4.3 Global Big Data Company Revenues from Business with Oil and Gas Companies 2015-2025 (Total) ($m, AGR %)
  • Figure 4.4 Global Big Data Company Revenues from Business with Oil and Gas Companies 2015-2025 (Hardware, Software, Services) ($m)
  • Figure 4.5 Global Big Data Company Revenues from Business with Oil and Gas Companies Market Shares (2015, 2020, 2025) (Hardware, Software, Services) (% of Total Revenues)
  • Figure 4.6 Evolution (out of 100%) of the Market Shares of the Types of Revenues- Hardware, Software, and Services, Between 2015 and 2025
  • Figure 4.7 Global Big Data Company Hardware Revenues from Business with Oil and Gas Companies 2015-2025 ($m, AGR %)
  • Figure 4.8 Hardware Market Share of Total Market (Hardware, Software, Services, Salaries) 2015, 2020, 2025 (%)
  • Figure 4.9 Global Big Data Company Software Revenues from Business with Oil and Gas Companies 2015-2025 ($m, AGR %)
  • Figure 4.10 Software Market Share of Total Market (Hardware, Software, Services, Salaries) 2015, 2020, 2025 (%)
  • Figure 4.11 Global Big Data Company Services Revenues from Business with Oil and Gas Companies 2015-2025 ($m, AGR %)
  • Figure 4.12 Services Market Share of Total Market (Hardware, Software, Services, Salaries) 2015, 2020, 2025 (%)
  • Figure 4.13 Oil and Gas Company In-House Salary Spending on Big Data-Skilled Employees 2015-2025 ($m, AGR %)
  • Figure 4.14 Salaries Market Share of Total Market (Hardware, Software, Services, Salaries) 2015, 2020, 2025 (%)
  • Figure 5.1 Big Data in Oil and Gas Market 2015-2025 Broken Down by Usage Type (Upstream, Midstream, Downstream, Administration) (CAPEX and OPEX) ($m)
  • Figure 5.2 Big Data in Upstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %)
  • Figure 5.3 Big Data in Upstream Oil and Gas Market Share (of Upstream, Midstream, Downstream, Administration) 2015, 2020, 2025 (%)
  • Figure 5.4 Big Data in Conventional Upstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Figure 5.5 Big Data in Conventional Upstream Oil and Gas Market Share (Upstream Conventional and Upstream Unconventional) 2015, 2020, 2025 (%)
  • Figure 5.6 Big Data in Unconventional Upstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %, CAGR %)
  • Figure 5.7 Big Data in Unconventional Upstream Oil and Gas Market Share (Upstream Conventional and Upstream Unconventional) 2015, 2020, 2025 (%)
  • Figure 5.8 Big Data in Midstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %)
  • Figure 5.9 Big Data in Midstream Oil and Gas Market Share (of Upstream, Midstream, Downstream, Administration) 2015, 2020, 2025 (%)
  • Figure 5.10 Big Data in Downstream Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %)
  • Figure 5.11 Big Data in Downstream Oil and Gas Market Share (of Upstream, Midstream, Downstream, Administration) 2015, 2020, 2025 (%)
  • Figure 5.12 Big Data in Administration Oil and Gas Spending Forecast (OPEX and CAPEX) 2015-2025 ($m, AGR %)
  • Figure 5.13 Big Data in Administration Oil and Gas Market Share (of Upstream, Midstream, Downstream, Administration) 2015, 2020, 2025 (%)

Companies Listed

  • 1010data
  • 10gen
  • Accenture
  • Accion Labs, Inc.
  • Actian
  • Actuate
  • Acunu
  • Aerospike
  • Alacer Technology Solutions
  • Alibaba
  • Alteryx
  • Altiscale
  • Amazon
  • Anadarko Corp Continental Resources
  • Apache Corp
  • Apache Software Foundation
  • Apixio
  • Aspera
  • Atos S.A.
  • Attivio
  • Avanade
  • Avata
  • Ayasdi
  • Ayata
  • Baker Hughes
  • Basho
  • BHP Billiton
  • BIConcepts IT Consulting GmbH
  • Big Data Partnership
  • Bigstep
  • Bloomberg
  • Blue Coat
  • BlueKai
  • Booz Allen Hamilton
  • BP
  • BPSolutions
  • Brightlight Consulting, Inc.
  • BTRG
  • Buckley Data Group LLC
  • Calpont
  • Capgemini
  • Centrifuge Systems
  • CGI
  • Chesapeake
  • Chevron
  • Cisco
  • ClickFox
  • Cloudera
  • Concord
  • ConocoPhillips
  • Contexti
  • Corva
  • Couchbase
  • Crowdflower
  • CSC
  • Daman Consulting
  • DataCrunchers
  • Dataguise
  • Datameer
  • DataPop
  • Datasift
  • Dataspora
  • DataStax
  • Datawatch
  • DataXu
  • DDN
  • Dell
  • Deloitte
  • Devon Energy
  • Digital Reasoning
  • Drilling Info
  • EcoSolutions Technology Inc.
  • EMC
  • Encore Software Services
  • Eni
  • EOG
  • EP Energy
  • Expan
  • ExxonMobil
  • F5 Networks
  • Facebook
  • Factual
  • Findability
  • Fluidinfo
  • Focus Business Solutions
  • Fractal Analytics
  • Fugro
  • Fujitsu Ltd.
  • FUSE Information Management
  • Fusion-io
  • Gartner
  • GasSecure
  • GE
  • General Sentiment
  • GlassHouse Systems Inc.
  • Global Consulting Solutions LLC
  • Gnip
  • GoldBot Consulting
  • GoodData
  • Google
  • GroundMetrics
  • GTRI
  • Guavus
  • Hadapt
  • Halliburton
  • Hess
  • Hewlett-Packard
  • Hexaware Technologies Inc
  • Hitachi
  • Hortonworks
  • HPCC Systems
  • Huawei
  • Hyperpublic
  • Hyve Solutions
  • i2
  • IBM
  • IDC
  • IHS
  • Infochimps
  • Infomotion GmbH
  • Informatica
  • Information Control Corporation
  • Ingrain
  • Intel
  • Intelligent Communication (Intelcom)
  • IQ Associates
  • iSoftStone Information Technology(Group) Co., Ltd
  • ISS Inc.
  • Jaspersoft
  • Jibes Data Analytic
  • John Wood Group Plc
  • Juniper Networks
  • Kaggle
  • Karmasphere
  • Kinetic Global Markets
  • Klarna
  • Knowesis Technology
  • Kognitio
  • Lattice Engines
  • Leap Commerce
  • Level Seven
  • Lighthouse
  • Lilien LLC
  • Lincube Group AB
  • Linn Energy
  • Logica
  • LucidWorks
  • MapR
  • Marathon Oil
  • MarkLogic
  • McKinsey & Company
  • Metamarkets
  • Microsoft
  • Microstrategy
  • Middlecon AB
  • mLogica
  • MuSigma
  • Neo Technology
  • NES
  • NewsCred
  • NewVantage
  • Nexenta
  • nfrastructure
  • nPario
  • OakStream Systems LLC
  • Offspring Solutions LLC
  • Oman Oil Company
  • OpenHeatMap
  • Opera Solutions
  • Oracle
  • Palantir
  • Palantir Technologies
  • ParAccel
  • Paradigm
  • Pentaho
  • Perficient
  • Persistent Systems
  • Pervasive Software
  • PetroChina
  • PetroDE
  • Pivotal
  • Precog
  • PROTEUS Technologies
  • PwC
  • Qlik
  • Quantum
  • Quid
  • R Square, Inc.
  • Rackspace
  • RainStor
  • ReadyForZero
  • Recommind
  • Recorded Future
  • Red Hat
  • Reply
  • RES
  • RetailNext
  • Revolution Analytics
  • Rosetta Resources
  • Royal Dutch Shell
  • Salesforce
  • Samsung
  • Saudi Aramco
  • SaveWave
  • Schlumberger
  • SciSpike
  • Seagate
  • Sendmail
  • SGI
  • Shanghai EC Data Information Technology Co., Ltd.
  • Sharpe Engineering
  • Shell
  • Siemens
  • Sierra Oil and Gas
  • Silixa
  • Sinopec works with
  • SiSense
  • Sociocast
  • SoftSol
  • Software AG/Terracotta
  • Sonatrach
  • Splunk
  • Statoil
  • Stormpulse
  • Stream Integration
  • Sulia
  • Super Micro
  • Sybase
  • Systech Solutions
  • Systex
  • Tableau Software
  • Tachyus
  • Talend
  • Talisman Energy
  • TamGroup
  • Tata Consultancy
  • TCS
  • Teradata
  • Teralytics AG
  • Terradata Corporation
  • TerraEchos
  • The Trade Desk
  • Think Big Analytics
  • Thomson Reuters
  • Tibco
  • Total
  • TracID
  • Trifacta
  • Tullow Oil
  • Verdande Technology
  • Visier
  • VMware
  • Voci Technologies Incorporated
  • WANdisco
  • WaveStrong
  • Wavii
  • Weatherford
  • Welldog
  • WiPro
  • WISE MEN
  • Wonga
  • Xerox
  • Yahoo!
  • ZestFinance

Government Agencies and Other Organisations Mentioned in this Report

  • European Union (EU)
  • Manhattan Institute
  • Open Data Institute
  • Stanford University
  • United States Patent and Trademark Office (USPTO)
  • University of California, Berkeley
  • University of Chicago
  • University of Washington
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