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Big Data in Oil and Gas Market - Global Industry Analysis, Size, Share, Growth, Trends, and Forecast 2018-2026

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    Report

  • 242 Pages
  • June 2018
  • Region: Global
  • Transparency Market Research
  • ID: 4851053

Big Data in Oil and Gas Market: Overview



The Big Data in Oil and Gas market report provides analysis of the Big Data in Oil and Gas market for the period 2016 –2026, wherein the years from 2018 to 2026 is the forecast period and 2017 is considered as the base year. The report covers all the major trends and technologies playing a major role in the growth of the Big Data in Oil and Gas market over the forecast period. It highlights the drivers, restraints, and opportunities expected to influence the market growth during this period. The study provides a holistic perspective on the market’s growth in terms of revenue (in US$ Mn) across different geographical regions, namely North America, Europe, Asia Pacific, Middle East & Africa, and South America.



The market overview section of the report demonstrates market dynamics such as the drivers, restraints, and opportunities that influence the current nature and future status of this market, key trends, regulations and policies, IT spending analysis, ecosystem analysis, Porter’s Five Force Analysis, and PESTEL analysis of the market. A market attractiveness analysis has been provided for every segment in the report, in order to provide a thorough understanding of the overall scenario in the Big Data in Oil and Gas market. The report also provides an overview of various strategies adopted by key players in the market.



Global Big Data in Oil and Gas Market: Scope of the Report



The report segments the market on the basis of component which are software and services. Software in the Big Data Oil and Gas market is further classified into data analytics, data collection, data discovery and visualization, and data management. Services are classified into consulting, system integration, and operation and maintenance. Further, the market is also segmented based on data type as structured, unstructured, and semi-structured. By application, the Big Data in Oil and Gas market is segmented into upstream (conventional, unconventional), midstream, downstream, and administration. The report provides in-depth segment analysis of the global Big Data in Oil and Gas market, thereby providing valuable insights at the macro as well as micro levels.



The report also provides the competitive landscape for the Big Data in Oil and Gas market, thereby listing out all the major players according to their geographic presence, market attractiveness, and recent key developments. The competitive landscape section of the report gives an overview about the market share of several key players for the year 2017. Big Data in Oil and Gas market data estimates are the result of our in-depth secondary research, primary interviews, and in-house expert panel reviews. These market estimates have been analyzed by taking into account the impact of different political, social, economic, technological, and legal factors along with the current market dynamics affecting the market growth. Moreover, the key takeaways section provided at the end of the competitive landscape section would help the operating companies to make the best moves in this market.



This report provides all the essential information required to understand the key developments in the Big Data Oil and Gas market, and growth trends of each segment and region. It also includes basic overview, sales area/geographical presence, revenue, strategy and developments under the company profile section. Also, the report provides insights related to trends and their impact on the market. Furthermore, Porter’s Five Forces analysis explains the five forces, namely buyers bargaining power, suppliers bargaining power, threat of new entrants, threat of substitutes, and degree of competition in the market. This report also provides a comprehensive ecosystem analysis of the Big Data in Oil and Gas market.



Global Big Data in Oil and Gas Market: Research Methodology



The research methodology is a perfect combination of primary research, secondary research, and expert panel reviews. Secondary research sources such as annual reports, company websites, broker reports, financial reports, SEC filings and investor presentations, national government documents, internal and external proprietary databases, statistical databases, relevant patent and regulatory databases, market reports, government publications, statistical databases, World Bank database and industry white papers are referred.



Primary research involves telephonic interviews, e-mail interactions, and face-to-face interviews for detailed and unbiased reviews on the Big Data in Oil and Gas market, across geographies. Primary interviews are usually conducted on an ongoing basis with industry experts and participants in order to get latest market insights and validate the existing data and analysis. Primary interviews offer first-hand information on important factors such as market trends, market size, competitive landscape, growth trends and outlook etc. These factors help to validate and strengthen secondary research findings and also help to develop the analysis team’s expertise and market understanding. Moreover, the data collected and analyzed from secondary and primary research is again discussed and examined by our expert panel.



Global Big Data in Oil and Gas Market: Competitive Dynamics



Accenture, Datawatch, Drillinginfo, Inc., Hitachi Vantara Corporation, HortonWorks, Inc., IBM Corporation, MapR Technologies, Inc., Microsoft Corporation, Oracle Corporation, SAP SE, SAS Institute, Inc., Cloudera, Inc., Palantir Solutions, Capgemini SE, and OSIsoft LLC are some of the key players that have been profiled in this study. Details such as financials, business strategies, key competitors, recent developments, and other such strategic information pertaining to these players have been provided as part of company profiling.


Table of Contents

1. Preface
1.1. Market Scope
1.2. Market Segmentation
1.3. Key Research Objectives
2. Assumptions and Research Methodology
2.1. Market Taxonomy - Segment Definitions
2.2. Research Methodology
2.2.1. List of Primary and Secondary Sources
2.3. Key Assumptions for Data Modeling
3. Executive Summary : Global Big Data in Oil & Gas
4. Market Overview
4.1. Introduction
4.2. Global Market - Macro Economic Factors Overview
4.3. Porter’s Five Forces Analysis - Global Big Data in Oil & Gas
4.4. Technology/Product Roadmap
4.5. Ecosystem Analysis - Global Big Data in Oil & Gas
4.6. Market Dynamics
4.6.1. Drivers
4.6.2. Restraints
4.6.3. Opportunities
4.6.4. Impact Analysis of Drivers & Restraints
4.7. Regulations and Policies - By Region
4.8. Big Data Spending Analysis (2017)
4.8.1. IT spending CAPEX analysis for Oil & Gas
4.8.2. IT Spending, by Type (US$ Mn)
4.8.2.1. Hardware
4.8.2.2. Software/Platform
4.8.2.3. Services
4.9. Impact of IoT on Big Data Analytics in Oil & Gas
4.10. Big Data Source Analysis (US$ Mn)
4.10.1. Environmental
4.10.2. Oceanographic
4.10.3. Geological Data
4.10.4. Economic
4.10.5. Others
4.11. Market Outlook
4.12. Competitive Scenario and Trends
4.12.1. List of New Entrants
4.12.2. Mergers & Acquisitions, Expansions
5. Global Big Data in Oil & Gas Analysis and Forecast, by Component
5.1. Overview
5.2. Big Data in Oil & Gas Size (US$ Mn) Forecast, By Component, 2016 - 2026
5.2.1. Software
5.2.1.1. Data Analytics
5.2.1.2. Data Collection
5.2.1.3. Data Discovery and Visualization
5.2.1.4. Data Management
5.2.2. Services
5.2.2.1. Consulting
5.2.2.2. System Integration
5.2.2.3. Operation and Maintenance
5.3. Market Attractiveness by Component
6. Global Big Data in Oil & Gas Analysis and Forecast, by Data Type
6.1. Overview & Definition
6.2. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Data Type, 2016 - 2026
6.2.1. Structured Data
6.2.2. Unstructured Data
6.2.3. Semi-Structured Data
6.3. Market Attractiveness by Data Type
7. Global Big Data in Oil & Gas Analysis and Forecast, by Application
7.1. Overview
7.2. Key Trends
7.3. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Application, 2016 - 2026
7.3.1. Upstream
7.3.1.1. Conventional
7.3.1.2. Unconventional
7.3.2. Midstream
7.3.3. Downstream
7.3.4. Administration
7.4. Market Attractiveness by Application
8. Global Big Data in Oil & Gas Analysis and Forecast, by Region
8.1. Key Findings
8.2. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Region, 2016 - 2026
8.2.1. North America
8.2.2. Europe
8.2.3. Asia Pacific
8.2.4. Middle East & Africa
8.2.5. South America
8.3. Market Attractiveness by Region
9. North America Big Data in Oil & Gas Analysis and Forecast
9.1. Key Findings
9.2. Key Trends
9.3. Big Data in Oil & Gas Size (US$ Mn) Forecast, by Component, 2016 - 2026
9.3.1. Software
9.3.1.1. Data Analytics
9.3.1.2. Data Collection
9.3.1.3. Data Discovery and Visualization
9.3.1.4. Data Management
9.3.2. Services
9.3.2.1. Consulting
9.3.2.2. System Integration
9.3.2.3. Operation and Maintenance
9.4. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Data Type, 2016 - 2026
9.4.1. Structured Data
9.4.2. Unstructured Data
9.4.3. Semi-Structured Data
9.5. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Application, 2016 - 2026
9.5.1. Upstream
9.5.1.1. Conventional
9.5.1.2. Unconventional
9.5.2. Midstream
9.5.3. Downstream
9.5.4. Administration
9.6. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Country, 2016 - 2026
9.6.1. The U.S.
9.6.2. Canada
9.6.3. Rest of North America
9.7. Market Attractiveness Analysis
9.7.1. by Component
9.7.2. by Data Type
9.7.3. by Application
9.7.4. by Country
10. Europe Big Data in Oil & Gas Analysis and Forecast
10.1. Key Findings
10.2. Key Trends
10.3. Big Data in Oil & Gas Size (US$ Mn) Forecast, by Component, 2016 - 2026
10.3.1. Software
10.3.1.1. Data Analytics
10.3.1.2. Data Collection
10.3.1.3. Data Discovery and Visualization
10.3.1.4. Data Management
10.3.2. Services
10.3.2.1. Consulting
10.3.2.2. System Integration
10.3.2.3. Operation and Maintenance
10.4. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Data Type, 2016 - 2026
10.4.1. Structured Data
10.4.2. Unstructured Data
10.4.3. Semi-Structured Data
10.5. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Application, 2016 - 2026
10.5.1. Upstream
10.5.1.1. Conventional
10.5.1.2. Unconventional
10.5.2. Midstream
10.5.3. Downstream
10.5.4. Administration
10.6. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Country, 2016 - 2026
10.6.1. Germany
10.6.2. France
10.6.3. UK
10.6.4. Rest of Europe
10.7. Market Attractiveness Analysis
10.7.1. by Component
10.7.2. by Data Type
10.7.3. by Application
10.7.4. by Country
11. Asia Pacific Big Data in Oil & Gas Analysis and Forecast
11.1. Key Findings
11.2. Key Trends
11.3. Big Data in Oil & Gas Size (US$) Forecast, by Component, 2016 - 2026
11.3.1. Software
11.3.1.1. Data Analytics
11.3.1.2. Data Collection
11.3.1.3. Data Discovery and Visualization
11.3.1.4. Data Management
11.3.2. Services
11.3.2.1. Consulting
11.3.2.2. System Integration
11.3.2.3. Operation and Maintenance
11.4. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Data Type, 2016 - 2026
11.4.1. Structured Data
11.4.2. Unstructured Data
11.4.3. Semi-Structured Data
11.5. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Application, 2016 - 2026
11.5.1. Upstream
11.5.1.1. Conventional
11.5.1.2. Unconventional
11.5.2. Midstream
11.5.3. Downstream
11.5.4. Administration
11.6. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Country, 2016 - 2026
11.6.1. China
11.6.2. Japan
11.6.3. India
11.6.4. Australia
11.6.5. Rest of Asia Pacific
11.7. Market Attractiveness Analysis
11.7.1. by Component
11.7.2. by Data Type
11.7.3. by Application
11.7.4. by Country
12. Middle East & Africa (MEA) Big Data in Oil & Gas Analysis and Forecast
12.1. Key Findings
12.2. Key Trends
12.3. Big Data in Oil & Gas Size (US$) Forecast, by Component, 2016 - 2026
12.3.1. Software
12.3.1.1. Data Analytics
12.3.1.2. Data Collection
12.3.1.3. Data Discovery and Visualization
12.3.1.4. Data Management
12.3.2. Services
12.3.2.1. Consulting
12.3.2.2. System Integration
12.3.2.3. Operation and Maintenance
12.4. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Data Type, 2016 - 2026
12.4.1. Structured Data
12.4.2. Unstructured Data
12.4.3. Semi-Structured Data
12.5. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Application, 2016 - 2026
12.5.1. Upstream
12.5.1.1. Conventional
12.5.1.2. Unconventional
12.5.2. Midstream
12.5.3. Downstream
12.5.4. Administration
12.6. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Country, 2016 - 2026
12.6.1. GCC
12.6.2. South Africa
12.6.3. Rest of MEA
12.7. Market Attractiveness Analysis
12.7.1. by Component
12.7.2. by Data Type
12.7.3. by Application
12.7.4. by Country
13. South America Big Data in Oil & Gas Analysis and Forecast
13.1. Key Findings
13.2. Key Trends
13.3. Big Data in Oil & Gas Size (US$ Mn) Forecast, by Component, 2016 - 2026
13.3.1. Software
13.3.1.1. Data Analytics
13.3.1.2. Data Collection
13.3.1.3. Data Discovery and Visualization
13.3.1.4. Data Management
13.3.2. Services
13.3.2.1. Consulting
13.3.2.2. System Integration
13.3.2.3. Operation and Maintenance
13.4. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Data Type, 2016 - 2026
13.4.1. Structured Data
13.4.2. Unstructured Data
13.4.3. Semi-Structured Data
13.5. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Application, 2016 - 2026
13.5.1. Upstream
13.5.1.1. Conventional
13.5.1.2. Unconventional
13.5.2. Midstream
13.5.3. Downstream
13.5.4. Administration
13.6. Big Data in Oil & Gas Size (US$ Mn and Terabytes) Forecast, by Country, 2016 - 2026
13.6.1. Brazil
13.6.2. Rest of South America
13.7. Market Attractiveness Analysis
13.7.1. by Component
13.7.2. by Data Type
13.7.3. by Application
13.7.4. by Industry
13.7.5. by Country
14. Competition Landscape
14.1. Market Player - Competition Matrix
14.2. Market Revenue Share Analysis (%), by Company (2017)
15. Company Profiles(Details - Basic Overview, , Key Competitors, Revenue, Strategy, Recent Developments)
15.1. Accenture\
15.1.1. Overview
15.1.2. SWOT
15.1.3. Financial Overview
15.1.4. Strategy
15.2. Datameer
15.2.1. Overview
15.2.2. SWOT
15.2.3. Financial Overview
15.2.4. Strategy
15.3. Datawatch
15.3.1. Overview
15.3.2. SWOT
15.3.3. Financial Overview
15.3.4. Strategy
15.4. Drillinginfo Inc.
15.4.1. Overview
15.4.2. SWOT
15.4.3. Financial Overview
15.4.4. Strategy
15.5. General Electric
15.5.1. Overview
15.5.2. SWOT
15.5.3. Financial Overview
15.5.4. Strategy
15.6. Hitachi Vantara Corporation
15.6.1. Overview
15.6.2. SWOT
15.6.3. Financial Overview
15.6.4. Strategy
15.7. Hortonworks Inc.
15.7.1. Overview
15.7.2. SWOT
15.7.3. Financial Overview
15.7.4. Strategy
15.8. International Business Machines Corporation
15.8.1. Overview
15.8.2. SWOT
15.8.3. Financial Overview
15.8.4. Strategy
15.9. MapR Technologies, Inc.
15.9.1. Overview
15.9.2. SWOT
15.9.3. Financial Overview
15.9.4. Strategy
15.10. Microsoft Corporation
15.10.1. Overview
15.10.2. SWOT
15.10.3. Financial Overview
15.10.4. Strategy
15.11. Northwest Analytics Inc.
15.11.1. Overview
15.11.2. SWOT
15.11.3. Financial Overview
15.11.4. Strategy
15.12. Oracle Corporation
15.12.1. Overview
15.12.2. SWOT
15.12.3. Financial Overview
15.12.4. Strategy
15.13. OSIsoft
15.13.1. Overview
15.13.2. SWOT
15.13.3. Financial Overview
15.13.4. Strategy
15.14. Palantir Economic Solutions Ltd
15.14.1. Overview
15.14.2. SWOT
15.14.3. Financial Overview
15.14.4. Strategy
15.15. SAP SE
15.15.1. Overview
15.15.2. SWOT
15.15.3. Financial Overview
15.15.4. Strategy
15.16. SAS Institute Inc.
15.16.1. Overview
15.16.2. SWOT
15.16.3. Financial Overview
15.16.4. Strategy
15.17. Capgemini SE
15.17.1. Overview
15.17.2. SWOT
15.17.3. Financial Overview
15.17.4. Strategy
15.18. Cloudera, Inc.
15.18.1. Overview
15.18.2. SWOT
15.18.3. Financial Overview
15.18.4. Strategy
16. Key Takeaways