ResearchMoz

The Big Data Market: 2014 - 2020 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Signals and Systems Telecom
Published Date » 2014-06-11
No. Of Pages » 289
 
 Big Data originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.  Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to R&D.  Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum...
Table of Content

Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Historical Revenue & Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned

Chapter 2: An Overview of Big Data
2.1 What is Big Data?
2.2 Approaches to Big Data Processing
2.2.1 Hadoop
2.2.2 NoSQL
2.2.3 MPAD (Massively Parallel Analytic Databases)
2.2.4 Others & Analytic Technologies
2.3 Key Characteristics of Big Data
2.3.1 Volume
2.3.2 Velocity
2.3.3 Variety
2.3.4 Value
2.4 Market Growth Drivers
2.4.1 Awareness of Benefits
2.4.2 Maturation of Big Data Platforms
2.4.3 Continued Investments by Web Giants, Governments & Enterprises
2.4.4 Growth of Data Volume, Velocity & Variety
2.4.5 Vendor Commitments & Partnerships
2.4.6 Technology Trends Lowering Entry Barriers
2.5 Market Barriers
2.5.1 Lack of Analytic Specialists
2.5.2 Uncertain Big Data Strategies
2.5.3 Organizational Resistance to Big Data Adoption
2.5.4 Technical Challenges: Scalability & Maintenance
2.5.5 Security & Privacy Concerns

Chapter 3: Vertical Opportunities & Use Cases for Big Data
3.1 Automotive, Aerospace & Transportation
3.1.1 Predictive Warranty Analysis
3.1.2 Predictive Aircraft Maintenance & Fuel Optimization
3.1.3 Air Traffic Control
3.1.4 Transport Fleet Optimization
3.2 Banking & Securities
3.2.1 Customer Retention & Personalized Product Offering
3.2.2 Risk Management
3.2.3 Fraud Detection
3.2.4 Credit Scoring
3.3 Defense & Intelligence
3.3.1 Intelligence Gathering
3.3.2 Energy Saving Opportunities in the Battlefield
3.3.3 Preventing Injuries on the Battlefield
3.4 Education
3.4.1 Information Integration
3.4.2 Identifying Learning Patterns
3.4.3 Enabling Student-Directed Learning
3.5 Healthcare & Pharmaceutical
3.5.1 Managing Population Health Efficiently
3.5.2 Improving Patient Care with Medical Data Analytics
3.5.3 Improving Clinical Development & Trials
3.5.4 Improving Time to Market
3.6 Smart Cities & Intelligent Buildings
3.6.1 Energy Optimization & Fault Detection
3.6.2 Intelligent Building Analytics
3.6.3 Urban Transportation Management
3.6.4 Optimizing Energy Production
3.6.5 Water Management
3.6.6 Urban Waste Management
3.7 Insurance
3.7.1 Claims Fraud Mitigation
3.7.2 Customer Retention & Profiling
3.7.3 Risk Management
3.8 Manufacturing & Natural Resources
3.8.1 Asset Maintenance & Downtime Reduction
3.8.2 Quality & Environmental Impact Control
3.8.3 Optimized Supply Chain
3.8.4 Exploration & Identification of Wells & Mines
3.8.5 Maximizing the Potential of Drilling
3.8.6 Production Optimization
3.9 Web, Media & Entertainment
3.9.1 Audience & Advertising Optimization
3.9.2 Channel Optimization
3.9.3 Recommendation Engines
3.9.4 Optimized Search
3.9.5 Live Sports Event Analytics
3.9.6 Outsourcing Big Data Analytics to Other Verticals
3.10 Public Safety & Homeland Security
3.10.1 Cyber Crime Mitigation
3.10.2 Crime Prediction Analytics
3.10.3 Video Analytics & Situational Awareness
3.11 Public Services
3.11.1 Public Sentiment Analysis
3.11.2 Fraud Detection & Prevention
3.11.3 Economic Analysis
3.12 Retail & Hospitality
3.12.1 Customer Sentiment Analysis
3.12.2 Customer & Branch Segmentation
3.12.3 Price Optimization
3.12.4 Personalized Marketing
3.12.5 Optimized Supply Chain
3.13 Telecommunications
3.13.1 Network Performance & Coverage Optimization
3.13.2 Customer Churn Prevention
3.13.3 Personalized Marketing
3.13.4 Location Based Services
3.13.5 Fraud Detection
3.14 Utilities & Energy
3.14.1 Customer Retention
3.14.2 Forecasting Energy
3.14.3 Billing Analytics
3.14.4 Predictive Maintenance
3.14.5 Turbine Placement Optimization
3.15 Wholesale Trade
3.15.1 In-field Sales Analytics
3.15.2 Monitoring the Supply Chain

Chapter 4: Big Data Industry Roadmap & Value Chain
4.1 Big Data Industry Roadmap
4.1.1 2010 – 2013: Initial Hype and the Rise of Analytics
4.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions
4.1.3 2018 – 2020 & Beyond: Large Scale Proliferation of Scalable Machine Learning
4.2 The Big Data Value Chain
4.2.1 Hardware Providers
4.2.1.1 Storage & Compute Infrastructure Providers
4.2.1.2 Networking Infrastructure Providers
4.2.2 Software Providers
4.2.2.1 Hadoop & Infrastructure Software Providers
4.2.2.2 SQL & NoSQL Providers
4.2.2.3 Analytic Platform & Application Software Providers
4.2.2.4 Cloud Platform Providers
4.2.3 Professional Services Providers
4.2.4 End-to-End Solution Providers
4.2.5 Vertical Enterprises

Chapter 5: Big Data Analytics
5.1 What are Big Data Analytics?
5.2 The Importance of Analytics
5.3 Reactive vs. Proactive Analytics
5.4 Customer vs. Operational Analytics
5.5 Technology & Implementation Approaches
5.5.1 Grid Computing
5.5.2 In-Database Processing
5.5.3 In-Memory Analytics
5.5.4 Machine Learning & Data Mining
5.5.5 Predictive Analytics
5.5.6 NLP (Natural Language Processing)
5.5.7 Text Analytics
5.5.8 Visual Analytics
5.5.9 Social Media, IT & Telco Network Analytics
5.6 Vertical Market Case Studies
5.6.1 Amazon – Delivering Cloud Based Big Data Analytics
5.6.2 Facebook – Using Analytics to Monetize Users with Advertising
5.6.3 WIND Mobile – Using Analytics to Monitor Video Quality
5.6.4 Coriant Analytics Services – SaaS Based Big Data Analytics for Telcos
5.6.5 Boeing – Analytics for the Battlefield
5.6.6 The Walt Disney Company – Utilizing Big Data and Analytics in Theme Parks

Chapter 6: Standardization & Regulatory Initiatives
6.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group
6.2 NIST (National Institute of Standards and Technology) – Big Data Working Group
6.3 OASIS –Technical Committees
6.4 ODaF (Open Data Foundation)
6.5 Open Data Center Alliance
6.6 CSA (Cloud Security Alliance) – Big Data Working Group
6.7 ITU (International Telecommunications Union)
6.8 ISO (International Organization for Standardization) and Others

Chapter 7: Market Analysis & Forecasts
7.1 Global Outlook of the Big Data Market
7.2 Submarket Segmentation
7.2.1 Storage and Compute Infrastructure
7.2.2 Networking Infrastructure
7.2.3 Hadoop & Infrastructure Software
7.2.4 SQL
7.2.5 NoSQL
7.2.6 Analytic Platforms & Applications
7.2.7 Cloud Platforms
7.2.8 Professional Services
7.3 Vertical Market Segmentation
7.3.1 Automotive, Aerospace & Transportation
7.3.2 Banking & Securities
7.3.3 Defense & Intelligence
7.3.4 Education
7.3.5 Healthcare & Pharmaceutical
7.3.6 Smart Cities & Intelligent Buildings
7.3.7 Insurance
7.3.8 Manufacturing & Natural Resources
7.3.9 Media & Entertainment
7.3.10 Public Safety & Homeland Security
7.3.11 Public Services
7.3.12 Retail & Hospitality
7.3.13 Telecommunications
7.3.14 Utilities & Energy
7.3.15 Wholesale Trade
7.3.16 Other Sectors
7.4 Regional Outlook
7.5 Asia Pacific
7.5.1 Country Level Segmentation
7.5.2 Australia
7.5.3 China
7.5.4 India
7.5.5 Japan
7.5.6 South Korea
7.5.7 Pakistan
7.5.8 Thailand
7.5.9 Indonesia
7.5.10 Malaysia
7.5.11 Taiwan
7.5.12 Philippines
7.5.13 Singapore
7.5.14 Rest of Asia Pacific
7.6 Eastern Europe
7.6.1 Country Level Segmentation
7.6.2 Czech Republic
7.6.3 Poland
7.6.4 Russia
7.6.5 Rest of Eastern Europe
7.7 Latin & Central America
7.7.1 Country Level Segmentation
7.7.2 Argentina
7.7.3 Brazil
7.7.4 Mexico
7.7.5 Rest of Latin & Central America
7.8 Middle East & Africa
7.8.1 Country Level Segmentation
7.8.2 South Africa
7.8.3 UAE
7.8.4 Qatar
7.8.5 Saudi Arabia
7.8.6 Israel
7.8.7 Rest of the Middle East & Africa
7.9 North America
7.9.1 Country Level Segmentation
7.9.2 USA
7.9.3 Canada
7.10 Western Europe
7.10.1 Country Level Segmentation
7.10.2 Denmark
7.10.3 Finland
7.10.4 France
7.10.5 Germany
7.10.6 Italy
7.10.7 Spain
7.10.8 Sweden
7.10.9 Norway
7.10.10 UK
7.10.11 Rest of Western Europe

Chapter 8: Vendor Landscape
8.1 1010data
8.2 Accenture
8.3 Actian Corporation
8.4 Actuate Corporation
8.5 AeroSpike
8.6 Alpine Data Labs
8.7 Alteryx
8.8 AWS (Amazon Web Services)
8.9 Attivio
8.10 Basho
8.11 Booz Allen Hamilton
8.12 InfiniDB
8.13 Capgemini
8.14 Cellwize
8.15 CenturyLink
8.16 Cisco Systems
8.17 Cloudera
8.18 Comptel
8.19 Contexti
8.20 Couchbase
8.21 CSC (Computer Science Corporation)
8.22 Datameer
8.23 DataStax
8.24 DDN (DataDirect Network)
8.25 Dell
8.26 Deloitte
8.27 Digital Reasoning
8.28 EMC Corporation
8.29 Facebook
8.30 Fractal Analytics
8.31 Fujitsu
8.32 Fusion-io
8.33 GE (General Electric)
8.34 GoodData Corporation
8.35 Google
8.36 Guavus
8.37 HDS (Hitachi Data Systems)
8.38 Hortonworks
8.39 HP
8.40 IBM
8.41 Informatica Corporation
8.42 Information Builders
8.43 Intel
8.44 Jaspersoft
8.45 Juniper Networks
8.46 Kognitio
8.47 Lavastorm Analytics
8.48 LucidWorks
8.49 MapR
8.50 MarkLogic
8.51 Microsoft
8.52 MicroStrategy
8.53 MongoDB (formerly 10gen)
8.54 Mu Sigma
8.55 NTT Data
8.56 Neo Technology
8.57 NetApp
8.58 Opera Solutions
8.59 Oracle
8.60 Palantir Technologies
8.61 ParStream
8.62 Pentaho
8.63 Platfora
8.64 Pivotal Software
8.65 PwC
8.66 QlikTech
8.67 Quantum Corporation
8.68 Rackspace
8.69 RainStor
8.70 Revolution Analytics
8.71 Salesforce.com
8.72 Sailthru
8.73 SAP
8.74 SAS Institute
8.75 SGI
8.76 SiSense
8.77 Software AG/Terracotta
8.78 Splunk
8.79 Sqrrl
8.80 Supermicro
8.81 Tableau Software
8.82 Talend
8.83 TCS (Tata Consultancy Services)
8.84 Teradata
8.85 Think Big Analytics
8.86 TIBCO Software
8.87 Tidemark
8.88 VMware (EMC Subsidiary)
8.89 WiPro
8.90 Zettics

Chapter 9: Expert Opinion – Interview Transcripts
9.1 Comptel
9.2 Lavastorm Analytics
9.3 ParStream
9.4 Sailthru

Chapter 10: Conclusion & Strategic Recommendations
10.1 Big Data Technology: Beyond Data Capture & Analytics
10.2 Transforming IT from a Cost Center to a Profit Center
10.3 Can Privacy Implications Hinder Success?
10.4 Will Regulation have a Negative Impact on Big Data Investments?
10.5 Battling Organization & Data Silos
10.6 Software vs. Hardware Investments
10.7 Vendor Share: Who Leads the Market?
10.8 Big Data Driving Wider IT Industry Investments
10.9 Assessing the Impact of IoT & M2M
10.10 Recommendations
10.10.1 Big Data Hardware, Software & Professional Services Providers
10.10.2 Enterprises

List of Tables

NA

List of Figures


Figure 1: Big Data Industry Roadmap
Figure 2: The Big Data Value Chain
Figure 3: Reactive vs. Proactive Analytics
Figure 4: Global Big Data Revenue: 2010 - 2020 ($ Million)
Figure 5: Global Big Data Revenue by Submarket: 2010 - 2020 ($ Million)
Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2010 - 2020 ($ Million)
Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2010 - 2020 ($ Million)
Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2010 - 2020 ($ Million)
Figure 9: Global Big Data SQL Submarket Revenue: 2010 - 2020 ($ Million)
Figure 10: Global Big Data NoSQL Submarket Revenue: 2010 - 2020 ($ Million)
Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2010 - 2020 ($ Million)
Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2010 - 2020 ($ Million)
Figure 13: Global Big Data Professional Services Submarket Revenue: 2010 - 2020 ($ Million)
Figure 14: Global Big Data Revenue by Vertical Market: 2010 - 2020 ($ Million)
Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2010 - 2020 ($ Million)
Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2010 - 2020 ($ Million)
Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2010 - 2020 ($ Million)
Figure 18: Global Big Data Revenue in the Education Sector: 2010 - 2020 ($ Million)
Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2010 - 2020 ($ Million)
Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2010 - 2020 ($ Million)
Figure 21: Global Big Data Revenue in the Insurance Sector: 2010 - 2020 ($ Million)
Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2010 - 2020 ($ Million)
Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2010 - 2020 ($ Million)
Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2010 - 2020 ($ Million)
Figure 25: Global Big Data Revenue in the Public Services Sector: 2010 - 2020 ($ Million)
Figure 26: Global Big Data Revenue in the Retail & Hospitality Sector: 2010 - 2020 ($ Million)
Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2010 - 2020 ($ Million)
Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2010 - 2020 ($ Million)
Figure 29: Global Big Data Revenue in the Wholesale Trade Sector: 2010 - 2020 ($ Million)
Figure 30: Global Big Data Revenue in Other Vertical Sectors: 2010 - 2020 ($ Million)
Figure 31: Big Data Revenue by Region: 2010 - 2020 ($ Million)
Figure 32: Asia Pacific Big Data Revenue: 2010 - 2020 ($ Million)
Figure 33: Asia Pacific Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 34: Australia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 35: China Big Data Revenue: 2010 - 2020 ($ Million)
Figure 36: India Big Data Revenue: 2010 - 2020 ($ Million)
Figure 37: Japan Big Data Revenue: 2010 - 2020 ($ Million)
Figure 38: South Korea Big Data Revenue: 2010 - 2020 ($ Million)
Figure 39: Pakistan Big Data Revenue: 2010 - 2020 ($ Million)
Figure 40: Thailand Big Data Revenue: 2010 - 2020 ($ Million)
Figure 41: Indonesia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 42: Malaysia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 43: Taiwan Big Data Revenue: 2010 - 2020 ($ Million)
Figure 44: Philippines Big Data Revenue: 2010 - 2020 ($ Million)
Figure 45: Singapore Big Data Revenue: 2010 - 2020 ($ Million)
Figure 46: Big Data Revenue in the Rest of Asia Pacific: 2010 - 2020 ($ Million)
Figure 47: Eastern Europe Big Data Revenue: 2010 - 2020 ($ Million)
Figure 48: Eastern Europe Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 49: Czech Republic Big Data Revenue: 2010 - 2020 ($ Million)
Figure 50: Poland Big Data Revenue: 2010 - 2020 ($ Million)
Figure 51: Russia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 52: Big Data Revenue in the Rest of Eastern Europe: 2010 - 2020 ($ Million)
Figure 53: Latin & Central America Big Data Revenue: 2010 - 2020 ($ Million)
Figure 54: Latin & Central America Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 55: Argentina Big Data Revenue: 2010 - 2020 ($ Million)
Figure 56: Brazil Big Data Revenue: 2010 - 2020 ($ Million)
Figure 57: Mexico Big Data Revenue: 2010 - 2020 ($ Million)
Figure 58: Big Data Revenue in the Rest of Latin & Central America: 2010 - 2020 ($ Million)
Figure 59: Middle East & Africa Big Data Revenue: 2010 - 2020 ($ Million)
Figure 60: Middle East & Africa Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 61: South Africa Big Data Revenue: 2010 - 2020 ($ Million)
Figure 62: UAE Big Data Revenue: 2010 - 2020 ($ Million)
Figure 63: Qatar Big Data Revenue: 2010 - 2020 ($ Million)
Figure 64: Saudi Arabia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 65: Israel Big Data Revenue: 2010 - 2020 ($ Million)
Figure 66: Big Data Revenue in the Rest of the Middle East & Africa: 2010 - 2020 ($ Million)
Figure 67: North America Big Data Revenue: 2010 - 2020 ($ Million)
Figure 68: North America Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 69: USA Big Data Revenue: 2010 - 2020 ($ Million)
Figure 70: Canada Big Data Revenue: 2010 - 2020 ($ Million)
Figure 71: Western Europe Big Data Revenue: 2010 - 2020 ($ Million)
Figure 72: Western Europe Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 73: Denmark Big Data Revenue: 2010 - 2020 ($ Million)
Figure 74: Finland Big Data Revenue: 2010 - 2020 ($ Million)
Figure 75: France Big Data Revenue: 2010 - 2020 ($ Million)
Figure 76: Germany Big Data Revenue: 2010 - 2020 ($ Million)
Figure 77: Italy Big Data Revenue: 2010 - 2020 ($ Million)
Figure 78: Spain Big Data Revenue: 2010 - 2020 ($ Million)
Figure 79: Sweden Big Data Revenue: 2010 - 2020 ($ Million)
Figure 80: Norway Big Data Revenue: 2010 - 2020 ($ Million)
Figure 81: UK Big Data Revenue: 2010 - 2020 ($ Million)
Figure 82: Big Data Revenue in the Rest of Western Europe: 2010 - 2020 ($ Million)
Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services ($ Million): 2010 - 2020
Figure 84: Big Data Vendor Market Share (%)
Figure 85: Global IT Expenditure Driven by Big Data Investments: 2010 - 2020 ($ Million)
Figure 86: Global M2M Connections by Access Technology (Millions): 2011 - 2020

Upcoming Reports:

Atopic Dermatitis Treatment Market - Global Industry Analysis, Size, Share, Growth, Trends And Forecast, 2013 - 2019
By - Transparency Market Research
Atopic dermatitis (AD) is a type of skin disorder in which the skin becomes dry, itchy and inflamed. Scratching of the skin leads to redness, swelling, scaling, cracking, weeping and crusting. Though, this condition is most common in infants and children, also affects adult population. AD is considered as idiopathic, a disease without a known cause, and is likely to be caused by both genetic as well as environmental factors. Many persons with AD have been found with a family history of atopy. People living in dry climate are more likely to get affected. Generally, people with AD...
Chocolate Market - North America Industry Analysis, Size, Share, Growth, Trends And Forecast, 2013 - 2019
By - Transparency Market Research
In sweets, chocolates are considered to be the most favorite treat for everyone. Chocolate can be defined as a processed sweetened food, having a major content of cocoa. It includes various types; such as with maximum cocoa content, is the dark chocolate, milk chocolates has the major content of milk and other different flavor. The demand of chocolate with consumer’s preference will have the direct impact on chocolate market in North America. United States has the maximum share in terms of volume for chocolate in North American market. The chocolate market is segmented as...
Beverage Ingredients Market - Global Industry Analysis, Size, Share, Growth, Trends and Forecast, 2013 - 2019
By - Transparency Market Research
Beverages are important part of balanced and healthy life. They provide energy, quench thirst, and provide hydration. The key ingredients in non-alcoholic beverages include high fructose corn syrup, caffeine and low calorie sweetener. Consumers beverage choice are no longer restricted to refreshment purpose, but rather are influenced by various factors. Hence the beverage market is more focusing on ingredients to satisfy the demand of its consumers. The most widely consumed beverage is tea.  The key drivers for beverage ingredients market include increase in young...

Research Assistance

We will be happy to help you find what you need.
Please call us or write us:

866-997-4948 (Us-Canada Toll Free)
Tel: +1-518-618-1030
Email: sales@researchmoz.us
Select License type:

Share this report

Related News

A Health Tech Incubator Started by GE Healthcare
Oct 30, 2014  
For the very first time, a health tech start up campus has been established by GE Healthcare at its headquarters at Helsinki, Finland. This area already has about twenty such healthcare start ups that are working on cloud services, wireless technologies, apps, and sensors that aim at improving healthcare. The main rationale behind such a step is providing opportunities for further...
Net Earnings of DuPont Increases 52% despite Agricultural Turmoil
Oct 29, 2014  
DuPont - The Wilmington, Del-based leading chemical company faced decline of three percent sales to $7.5 billion in the quarter ending of September 30. The company’s third-quarter earnings were increased by 52 percent to $433 million albeit the slow global growth and agricultural downside.   The company witnessed the biggest drags from chemical sales with eight percent...
Appeal for Sanitization in Construction Industry through Government Regulations: CIOB Ghana
Oct 29, 2014  
Rockson Dogbegah - The Chairman of the Chartered Institute of Building Ghana commented that the construction industry needs a thorough cleaning up and assurance of proper growth. Mr. Dogbegah also said that a few factors are extremely important for a proper development in the construction industry. These include keen adherence to standards, supreme sense of professionalism, and sheer pursuance...
Facebook Hints of Rise in Expenses in 2015, Shares Drop
Oct 29, 2014  
Facebook has announced on Tuesday that it is prepping for a dramatic rise in spending in the year 2015. This warning has come combined with a projected slowdown in the company’s revenues after revenues were analyzed at the end of the third quarter of the year. The plans of such hefty spending in the next financial year by the company are indicative of stress in the investor...
Samsung Records Poorest Profits in Q3 2014
Oct 29, 2014  
In the quarter ending September 2014, Samsung Electronics Co. Ltd registered the smallest earnings ever in two years. The drop in earnings can be easily attributed to heavy competition from new Chinese devices and the Apple phones. The net income stood at US$4 billion for the South Korea-based company. Apple has been Samsung’s strongest contender ever since the latter entered the...