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