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Report Details
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

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

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

Do

Table Of Contents

Big Data in Oil and Gas Market 2015-2025: Forecasts by Spending Type (Hardware, Software, Services and Salaries) and Application Area (Upstream, Midstream, Downstream and Administration)
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 and 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 and Productivity
2.7.3 Trust Issues and 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 and 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 and 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 and Gas Upstream Forecast 2015-2025
5.1.1 Big Data in Oil and Gas Conventional Upstream Forecast 2015-2025
5.1.2 Big Data in Oil and Gas Unconventional Upstream Forecast 2015-2025
5.2 Big Data in Oil and Gas Midstream Forecast 2015-2025
5.3 Big Data in Oil and Gas Downstream Forecast 2015-2025
5.4 Big Data in Oil and 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 and Gas Industry
7.4.3 The Most Prominent Sources of Data Used by Oil and Gas Companies
7.4.4 The Types of Oil and 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 and 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 and 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 and Gas
7.5.5 Datawatch's Products for the Oil and 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 and Gas
7.6.4 Do Energy Companies Need to Reskill Employees to Use Big Data?
7.6.5 The Importance of Oil and 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 and Gas
7.7.2 The Impact of the Oil Price Fall on Big Data Spending
7.7.3 The Types of Oil and 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 and 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 and Gas
7.8.8 Restraints on Big Data Investment by Oil and Gas Companies
7.8.9 Differences Between Big Data Adoption in Oil and 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 and 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 and Gas to Qlik's Business
7.9.7 Restraints on Big Data Investment by Oil and 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 and 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) ($m, AGR (%)
Table 3.4 Drivers and Restraints in the Big Data in Oil and Gas Market
Table 3.5 Visiongain's Anticipated WTI Oil Price, 2015, 2016, 2017, 2018-2021, 2022-2025 ($/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 and 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 and Gas: Global Market Breakdown
Figure 1.2 Big Data in Oil and 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 (%)

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