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Big Data in Extraction and Natural Resource Industries: Mining, Water, Timber, Oil and Gas 2014 - 2019

Published

July 2014

Pages

49

Pricing

Single-user License: $ 995 USD
Company-wide License: $ 2,995 USD
Team License (Up to 5 Users): $ 1,995 USD

Keywords

Big Data, Big Data Analytics, Data Analytics, Big Data Natural Resources, Big Data Mining, Big Data Oil and Gas, Big Data Extraction Industries

Overview

Big Data and natural resources are made for each other and the natural resources industry is positioning itself to put this wealth of information to better use. Big Data is a comparatively untapped asset that organizations in this vertical can exploit once they adopt a shift of mindset and apply the right methods and processes.

In the natural resource industry, Big Data can come from conventional sources, which are equipment monitoring and maintenance records. Data from these sources is generally captured and used as required, but until now, it was not always preserved for long-term use. With the proper infrastructure and tools, natural resources organizations can gain measurable value from all of these data sources. As the quantity of data, the quantity of sources, and the regularity of data updates increases, so too does the value of Big Data.

This research evaluates the challenges and opportunities for leveraging Big Data and Analytics in the extraction and natural resources industries. The report analyzes companies, solutions, issues, and outlook for mining, water, timber, oil and gas including utilities. The report includes a review of the companies that we believe have key market advantages including scale and scope to best leverage Big Data and Analytics within the extraction and natural resources industry. The report also includes a forecast for Big Data revenue 2014 - 2019.

Target Audience

• Telecom services companies
• Big Data and Analytics companies
• Telecom and IT infrastructure companies
• Data infrastructure, cloud, and services companies
• Extraction and natural resources management companies

Companies in Report

• Accenture
• Alcoa
• Alteryx
• Amazon
• Apache
• BHP Billiton
• BP
• CA Technologies
• Cassandra
• Chevron
• Conoco Phillips
• Dow Jones
• eBay
• EMC
• ExxonMobil
• Facebook
• Freeport-McMoran
• Gazprom
• GE Water
• Google
• IBM
• InfoBright
• InsightPricing
• Instagram
• International Telecommunication
• ITT Corporation
• Kuwait Petroleum Corp.
• LinkedIn
• Microsoft
• MongoDB
• National Iranian Oil Co.
• Newmont Mining Corp
• Opera Solutions
• Orkut
• Pegasystems
• Pemex
• Pentaho
• Petrobras
• PetroChina
• Pinterest
• Plum Creek Timber Co.
• Practical Ecommerce
• Rayonier
• Rio Tinto
• Royal Dutch Shell
• Saudi Aramco
• Schneider
• Suez Environnement
• Tableau
• Teck
• Teradata
• Twitter
• Veolia Environnement
• Weyerhaeuser Co.
• WisePricer
• Yahoo

Table of Contents

1.0 EXECUTIVE SUMMARY 5
2.0 INTRODUCTION 7
2.1 BIG DATA OVERVIEW 7
2.2 BIG DATA IN RESOURCE LOGISTICS AND SCM 8
2.3 BIG DATA AND ANALYTICS IN SUPPLY AND DEMAND MANAGEMENT 9
3.0 BIG DATA IN THE RESOURCE SUPPLY SIDE 11
3.1 BIG DATA IN RESOURCE MANAGEMENT 12
3.1.1 WATER MANAGEMENT 13
3.1.2 BIG DATA IN TIMBER AND FOREST MANAGEMENT 15
3.1.3 BIG DATA IN ENERGY AND ELECTRICITY 16
3.2 BIG DATA IN EXTRACTION AND EXPLORATION 17
3.2.1 BIG DATA IN MINING 19
3.2.2 BIG DATA IN OIL AND GAS 20
4.0 BIG DATA IN THE RESOURCE DEMAND SIDE 23
4.1 PREDICTIVE ANALYTICS TO DETERMINE DEMAND 23
4.1.1 STRUCTURED DATA MODELS VS. BIG DATA 25
4.1.2 SOURCES OF DATA 26
4.2 PREDICTIVE ANALYTICS FOR PRICING 28
4.2.1 PREDICTING OPTIMAL PRICE POINT 30
4.2.2 OPTIMIZING PROFITS VS. SMOOTHING DEMAND 31
5.0 LEADING COMPANIES AND SOLUTIONS 32
5.1 WATER COMPANIES 32
5.1.1 VEOLIA ENVIRONNEMENT 32
5.1.2 SUEZ ENVIRONNEMENT 32
5.1.3 ITT CORPORATION 33
5.1.4 GE WATER 34
5.2 TIMBER COMPANIES 34
5.2.1 WEYERHAEUSER CO. 34
5.2.2 PLUM CREEK TIMBER CO. 35
5.2.3 RAYONIER 35
5.3 MINING COMPANIES 36
5.3.1 ALCOA 36
5.3.2 NEWMONT MINING CORP 37
5.3.3 TECK 37
5.3.4 FREEPORT-MCMORAN 37
5.3.5 RIO TINTO 38
5.3.6 BHP BILLITON 38
5.4 OIL AND GAS COMPANIES 39
5.4.1 SAUDI ARAMCO 39
5.4.2 GAZPROM 39
5.4.3 NATIONAL IRANIAN OIL CO. 39
5.4.4 EXXONMOBIL 40
5.4.5 PETROCHINA 40
5.4.6 BP 40
5.4.7 ROYAL DUTCH SHELL 41
5.4.8 PEMEX 41
5.4.9 CHEVRON 42
5.4.10 KUWAIT PETROLEUM CORP. 42
5.4.11 CONOCO PHILLIPS 42
5.4.12 PETROBRAS 42
6.0 THE FUTURE OF BIG DATA IN PHYSICAL RESOURCES 44
7.0 CONCLUSIONS AND RECOMMENDATIONS 46

Figures

Figure 1: Usage of Big Data in Resources 11
Figure 2: Smart Solution Options in Organizations 12
Figure 3 : Energy Consumption in a Production Line 16
Figure 4: Big Data Exploration 18
Figure 5: Mining Equipment Analytics 19
Figure 6: Big Data in Oil and Gas 20
Figure 7: Big Data Model 24
Figure 8: Mapping the Predictive Analytics 28
Figure 9: Color to Display and Product Differentiation 30
Figure 10: Global Big Data Revenue 2014 - 2019 48
Figure 11: Regional Big Data Revenue 2014 - 2019 48