【レポートの概要（一部）】
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
【レポート販売概要】
■ タイトル：世界の石油・ガス産業におけるビックデータ市場■ 英文：Big Data in Oil & Gas Market 2015-2025: Forecasts by Spending Type (Hardware, Software, Services & Salaries) and Application Area (Upstream, Midstream, Downstream & Administration)
■ 発行日：2015年9月
■ 調査会社：visiongain
■ 商品コード：VGAIN5100103
■ 調査対象地域：グローバル
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