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:

Mental Disorders Drugs Market - Global Industry Analysis, Size, Share, Growth, Trends And Forecast, 2012 - 2018
By - Transparency Market Research
A mental disorder is a psychological anomaly that is reflected in the behavior of the patient through distress, disability or any conduct that is not a part of a person’s normal mental development. The causes of mental disorders are varied in nature and many are also unclear. Psychiatric medication and psychotherapy are the two modes of treatment for mental disorder. In psychiatric medication drug classes such as antidepressants, anxiolytics, and antipsychotics are most commonly used.  Improvement in patient security policy, growth in domestic and foreign investment...
Poland: sawn wood market
By - Williams and Marshal Strategy
This report presents a comprehensive overview of the sawn wood market in Poland and its state as of January 2014. It provides detailed analysis of the industry, its dynamics and structure. The purpose of the report is to describe the state of the sawn wood market in Poland, to present actual and retrospective information about the volumes and dynamics of production, imports, exports and consumption, the characteristics of the market for the period 2008-2012 and to build a forecast for the market development until 2018. In the same way, the report presents an elaborate overview of the main...
MTBE Market - Global Industry Analysis, Size, Share, Growth, Trends and Forecast, 2013 - 2019
By - Transparency Market Research
Methyl tertiary butyl ether (MTBE) is a colorless, volatile, flammable liquid majorly used as fuel component. MTBE is manufactured by catalytic reaction of isobutylene with methanol. MTBE is used as an octane buster in engines. It is also used as oxygenate to increase oxygen in gasoline. MTBE offers good blending properties and reduces the content of formaldehyde and benzene in gasoline. Additionally, it replaces lead compounds enhancing the anti-knocking properties of the fuel. It is also used as a laboratory reagent to extract compounds. It is also used as solvent to dissolve gallstones....

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

Order cancellations not a sign of market slump: Airbus
Jul 31, 2014  
Leading aircraft manufacturer Airbus on Wednesday said that the recent wave of order cancellations for its commercial jetliners do not mean that the market has declined. The market is still strong and single-aisle jets deals are likely to return later. A total of 225 Airbus cancellations were reported since the beginning of this year. Of these, Dubai’s Emirates Airlines alone...
Leading Australian Universities Want to Hike Fees, Enroll Fewer Students
Jul 31, 2014  
Australia’s prestigious group of top eight universities, also known as the Group of Eight (Go8), has indicated plans of increasing tuition fees while reducing the number of students they enroll, a reform-backer has predicted. These decisions could be made if the universities are deregulated .  Arguing for this decision, the chairman of Go8, and vice chancellor of the ANU, Ian...
Bristol-Myers Squibb Posts USD 3.9 billion Revenues in 2014 Second Quarter
Jul 30, 2014  
Leading bio-pharmaceutical company Bristol-Myers Squibb has registered revenues of USD 3.9 billion in the 2014 second quarter. This marks a 4% decrease from the same period in 2013. The global revenues of the company (excluding the divested Diabetes Alliance), saw a 7% increase. In this quarter, the company’s results were underlined by steady global sales of nearly each of its...
Novartiss Simbrinza gets approval from the EU
Jul 30, 2014  
Glaucoma patients in Europe can easily obtain Novartis’ Simbrinza eye drops which are a combination of brimonidine tartrate and brinzolamide and it is without a beta-blocker. This eye drop has got the approval of the European Commission. Unlike the inefficient treatment provided by monotherapy, this eye drop aims at treating intraocular pressure or IOP in adults with ocular hypertension...
Second Treaty Between Zealand And Boehringer Ingelheim
Jul 30, 2014  
Denmark based Pharma group Zealand Pharma and Germany’s Boehringer Ingelheim have signed an accord to formulate new medicines for advanced and effective treatment of cardio-metabolic maladies. The collaboration between these two Pharma groups involves a new therapeutic peptide project from the list of preclinical programs of Zealand Pharma.  Both these companies are aiming...