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エンタープライズモビリティ 2015年:BYOD、クラウド、ソーシャル、ビッグデータおよびアプリケーション管理 (調査パッケージ)

Enterprise Mobility 2015: BYOD, Cloud, Social, Big Data and Application Management

発行 Mind Commerce 商品コード 289915
出版日 ページ情報 英文 952 pages
納期: 即日から翌営業日
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本日の銀行送金レート: 1USD=115.27円で換算しております。
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エンタープライズモビリティ 2015年:BYOD、クラウド、ソーシャル、ビッグデータおよびアプリケーション管理 (調査パッケージ) Enterprise Mobility 2015: BYOD, Cloud, Social, Big Data and Application Management
出版日: 2015年10月01日 ページ情報: 英文 952 pages
概要

現在のエンタープライズモビリティを推進する主な要因には、Bring Your Own Device (BYOD)、モバイルデバイス管理 (MDM) およびアプリケーション管理が含まれます。ビッグデータも同様に、企業が内部・外部のアプリケーションそれぞれの最適化を目指し、従業員・顧客態度の双方についてより良く理解しようとする際、役割を果たします。

当レポートパッケージは、エンタープライズおよびクラウド環境におけるBYOD、クラウドアプリケーション、クラウドにおけるソーシャルビジネスサービス、ビッグデータ、モバイルアプリケーションおよびデータアズアサービス (DaaS) 市場に関するレポートをセットでお届けするもので、概略以下の構成でお届けいたします。

本調査パッケージに含まれるレポート

  • クラウドアプリケーション市場 2015-2020年
  • データアズアサービス (DaaS) 市場・予測 2015-2020年
  • ビッグデータ市場:ビジネス事例、市場分析および予測 2015-2020年
  • クラウドにおけるソーシャルビジネスサービス:市場分析・予測 2015-2020年
  • エンタープライズアプリケーションおよびクラウド環境におけるBYOD:市場課題および機会分析 2015-2020年
  • モバイルアプリケーション市場 2015年:将来の発展・チャンスの市場分析および評価

目次

エンタープライズアプリケーションおよびクラウド環境におけるBYOD:市場課題および機会分析 2015-2020年

第1章 エグゼクティブサマリー

第2章 イントロダクション

第3章 BYODの動向

第4章 エンタープライズにおけるBYOD

第5章 エンタープライズ部門におけるBYODアプリケーション

第6章 クラウド環境におけるBYOD

第7章 エンタープライズMDM

第8章 モバイル仮想化

第9章 SMACコンバージェンスにおけるBYODの動向

第10章 ゲーミフィケーションおよびBYOD

第11章 エンタープライズの課題

第12章 BYODの実行

第13章 BYODプロジェクト管理

第14章 CIOおよびIT部門にBYODが及ぼす影響

第15章 BYOD市場の予測

第16章 BYOD:地域別

第17章 BYODベンダー分析

第18章 MDMベンダー分析

第19章 事例分析

第20章 結論・提言

図表

クラウドアプリケーション市場 2015-2020年

第1章 エグゼクティブサマリー

第2章 クラウドコンピューティングの概要

第3章 クラウドサービス分析

第4章 クラウドにおける垂直産業

第5章 クラウドアプリケーションサービス市場の予測

第6章 クラウドアプリケーションサービスベンダーの分析

第7章 キャリアクラウドの機会

第8章 結論・提言

図表

クラウドにおけるソーシャルビジネスサービス:市場分析・予測 2015-2020年

第1章 エグゼクティブサマリー

第2章 概要

第3章 技術、アプリケーションおよびプロバイダー

第4章 市場予測

第5章 エコシステム分析

第6章 ベンダー分析

第7章 成功シナリオのケーススタディ分析

第8章 結論・提言

図表

ビッグデータ市場:ビジネス事例、市場分析および予測 2015-2020年

第1章 イントロダクション

第2章 ビッグデータ技術およびビジネス事例

第3章 ビッグデータ向けの主な投資部門

第4章 ビッグデータのバリューチェーン

第5章 ビッグデータ分析

第6章 標準化・規制イニシアチブ

第7章 ビッグデータ市場における主要企業

第8章 市場分析

図表

モバイルアプリケーション市場 2015年:将来の発展・チャンスの市場分析および評価

第1章 イントロダクション

第2章 モバイルアプリケーションの概要

第3章 モバイルプラットフォーム (オペレーティングシステム)

第4章 モバイルプログラミング

第5章 アプリケーション開発プラットフォーム

第6章 主な開発コンセプト

第7章 モバイルアプリケーション市場

第8章 アプリケーションストアのケーススタディ

第9章 市場規模

第10章 モバイルゲーム分析

第11章 ウェアラブルデバイスアプリケーションおよび将来のアプリケーション

第12章 キャリア・ベンダーの採用

第13章 アプリケーションパブリッシャー分析

第14章 モバイルアプリケーションの将来

図表

データアズアサービス (DaaS) 市場・予測 2015-2020年

第1章 イントロダクション

第2章 DaaS技術

第3章 DaaS市場

第4章 DaaS戦略

第5章 DaaS型アプリケーション

第6章 DaaS市場展望・将来

第7章 結論

第8章 付録

図表

このページに掲載されている内容は最新版と異なる場合があります。詳細はお問い合わせください。

目次

Overview:

One of the biggest challenges for enterprise today is optimizing mobility to realize business strategies and integrate disparate technologies in a blended manner. Major factors driving enterprise mobility today include Bring Your Own Device (BYOD), enterprise Cloud management, social networking, and application management.

Data management is also a major challenge for enterprise as corporations seek to optimize business operations and customer behaviors to better direct enterprise resources. Big Data and Analytics, Data as a Service (DaaS), and Cloud systems are all important areas for the entire enterprise ecosystem including CSPs, software providers, and ICT platform and infrastructure providers.

This research package includes:

  • Cloud Application Marketplace 2015 - 2020
  • Data as a Service (DaaS) Market and Forecasts 2015 - 2020
  • Big Data Market: Business Case, Market Analysis & Forecasts 2015 - 2020
  • Social Business Services in the Cloud: Market Analysis and Forecast 2015 - 2020
  • BYOD in Enterprise Applications and Cloud Environment: Market Challenge and Opportunity Analysis 2015 - 2020
  • Mobile Application Marketplace 2015: Market Analysis and Assessment of Future Evolution and Opportunities

This research evaluates the challenges, opportunities, and market outlook for BYOD in enterprise and cloud environments. This research assesses the potential users of cloud service and includes a SWOT analysis, cloud social vendor analysis, market trends, and industry forecasts. The report also analyzes the technology and solution providers in certain key areas including marketing automation, social media monitoring, enterprise collaboration, web experience management, information governance, digital commerce, CRM and customer support, ECM and File sharing, and workforce management. This includes evaluation of the key benefits, challenges, trends and development process impacting the market for cloud-based applications.

This research also provides an in-depth assessment of the global Big Data market, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Table of Contents

BYOD in Enterprise Applications and Cloud Environment: Market Challenge and Opportunity Analysis 2015-2020

1.0. EXECUTIVE SUMMARY

2.0. INTRODUCTION

  • 2.1. CONSUMERIZATION OF IT AND BYOD
  • 2.2. MDM AND EMM
  • 2.3. MOBILE VIRTUALIZATION
  • 2.4. CYOD/HYOD/COPE VS. BYOD
  • 2.5. MAM, MIM, AND IDENTITY MANAGEMENT

3.0. BYOD TRENDS

  • 3.1. HIGH PENETRATION OF MOBILE
  • 3.2. MOBILITY OF WORKFORCE
  • 3.3. FLEXIBLE WORK ENVIRONMENT
  • 3.4. AVAILABILITY AND PERFORMANCE OF MOBILE DEVICE
  • 3.5. BYOD PRODUCTIVITY
  • 3.6. BYOD SECURITY
  • 3.7. OPEX REDUCTION
  • 3.8. EMPLOYEE EMPOWERMENT
  • 3.9. MOBILE OS
  • 3.10. MANDATING BYOD AS APPROACH
  • 3.11. MOBILE DEVICE MANAGEMENT
  • 3.12. MODERNIZING LEGACY APPLICATION
  • 3.13. VIRTUAL WORKING ENVIRONMENT
  • 3.14. WEARABLE TECHNOLOGY
  • 3.15. ENTERPRISE IT
  • 3.16. TABLET COMPUTING

4.0. BYOD IN ENTERPRISE

  • 4.1. WHY BYOD FOR ENTERPRISE MOBILITY
  • 4.2. BENEFITS OF BYOD
  • 4.3. BYOD OBSTACLES
    • 4.3.1. REDUCE EMPLOYEE'S PRODUCTIVITY
    • 4.3.2. LICENSING
    • 4.3.3. COST RISK
  • 4.4. STRATEGY TO SECURE ENTERPRISE
    • 4.4.1. CONTAINERIZATION
    • 4.4.2. MAM
    • 4.4.3. IDENTITY AND ACCESS MANAGEMENT
  • 4.5. BYOD IN SMBS
  • 4.6. OTHER CONSIDERATIONS FOR SMBS
    • 4.6.1. NOTIFICATION
    • 4.6.2. BEST PRACTICES ADOPTION
    • 4.6.3. TRANSPARENT POLICY
    • 4.6.4. EMPLOYEE EDUCATION
    • 4.6.5. DATA SEGREGATION
    • 4.6.6. SOCIAL FACTOR
    • 4.6.7. PREVENT DATA LEAKAGE
    • 4.6.8. IT SUPPORT
  • 4.7. LOCATION IMPACT AND SOLUTION
    • 4.7.1. LOCAID: FULL SCALE ENTERPRISE LOCATION SOLUTION

5.0. BYOD APPLICATIONS IN ENTERPRISE SECTORS

  • 5.1. TRAVEL AGENCY
  • 5.2. HEALTHCARE INDUSTRY
  • 5.3. GOVERNMENT AGENCY
  • 5.4. WORKPLACE ENVIRONMENT
  • 5.5. ROLE OF MNOS

6.0. BYOD IN CLOUD ENVIRONMENT

  • 6.1. CLOUD BYOD ADOPTION
  • 6.2. ENTERPRISE MIGRATION TO CLOUD
  • 6.3. ENTERPRISE BENEFITS OF CLOUD SERVICE
  • 6.4. CLOUD GROWTH DRIVERS
  • 6.5. DATA SECURITY IN CLOUD
  • 6.6. CLOUD APP SECURITY CHALLENGE
  • 6.7. PROTECTING CORPORATE ASSETS
  • 6.8. CASE STUDY
    • 6.8.1. DOMINO'S PIZZA
    • 6.8.2. LONDON AIRPORT

7.0. ENTERPRISE MDM

  • 7.1. MDM OR EMM
    • 7.1.1. DEVICE MANAGEMENT
    • 7.1.2. APPLICATION MANAGEMENT
    • 7.1.3. NETWORK MANAGEMENT
    • 7.1.4. DATA MANAGEMENT
  • 7.2. RECOMMENDED FEATURES FOR MDM
  • 7.3. MDM PROJECTIONS AND TREND 2015-2020
    • 7.3.1. ENTERPRISE PERCEPTION ON BYOD IN WORKPLACE
    • 7.3.2. ENTERPRISE BELIEF OVER MOBILE WORKFORCE
    • 7.3.3. WORKPLACE MOBILITY TREND OF PROFESSIONALS 2015-2020
    • 7.3.4. EMPLOYEE RATIO OF PERSONAL DEVICES 2015-2020
    • 7.3.5. WHAT EMPLOYEE FEELINGS ABOUT USING PERSONAL DEVICES FOR WORK
    • 7.3.6. COMPANIES ON MOBILE WORKFORCE ADOPTION 2015-2020
    • 7.3.7. COMPANY CONCERN OVER ALLOWING PERSONAL DEVICES IN WORKPLACE
    • 7.3.8. MOBILE WORKFORCE ADOPTION SCENARIO
    • 7.3.9. PERSONAL MOBILE DEVICES IN TODAY'S WORKPLACE
    • 7.3.10. MOBILE MALWARE THREAT & MOBILE WORKFORCE DILEMMA
    • 7.3.11. HOW COMPANIES MANAGE MOBILITY WITH SOLID STRATEGY
  • 7.4. VENDOR ANALYSIS

8.0. MOBILE VIRTUALIZATION

9.0. BYOD TREND IN SMAC CONVERGENCE

10.0. GAMIFICATION AND BYOD

11.0. ENTERPRISE CHALLENGES

  • 11.1. POLICY VS. REGULATORY
    • 11.1.1. DEFINE WHAT WILL BE ALLOWED AND WHAT IS NOT
    • 11.1.2. SET CLEAR AND WRITTEN POLICIES
    • 11.1.3. DETERMINE WHOSE DATA IS?
    • 11.1.4. WHO OWNS THE HARDWARE?
  • 11.2. SECURITY VS. INFRASTRUCTURE
  • 11.3. COST VS. REVENUE
  • 11.4. WORKING CULTURE CHALLENGES
  • 11.5. SECURITY RISK OF BYOD DEPLOYMENT
  • 11.6. REASONS OF SECURITY RISKS
    • 11.6.1. OUTDATED SOFTWARE
    • 11.6.2. INABILITY TO PREVENT INSTALLATION OF SPECIFIC MOBILE APPS
    • 11.6.3. CONSUMERIZATION OF CLOUD STORAGE

12.0. BYOD IMPLEMENTATION

  • 12.1. IDENTIFYING RIGHT APPROACH
  • 12.2. EVALUATING BYOD DEPLOYMENT FACTORS
  • 12.3. EVALUATING HYBRID BYOD
  • 12.4. MINIMIZING MOBILE SECURITY RISKS
  • 12.5. EVALUATING MOBILE DEVICE OWNERSHIP
  • 12.6. COBIT 5 FRAMEWORK

13.0. BYOD PROJECT MANAGEMENT

  • 13.1. PROJECT TEAM
  • 13.2. BYOD ROADMAP
  • 13.3. POLICY GUIDELINE
  • 13.4. RIGHT INFRASTRUCTURE
  • 13.5. DATA OWNERSHIP
  • 13.6. ACCESS POLICY
  • 13.7. ROBUST SUPPORT
  • 13.8. FINANCIAL ELEMENTS

14.0. BYOD IMPLICATION FOR CIO AND IT DEPARTMENT

  • 14.1. BROKER OF CHOICE
  • 14.2. COST SAVER
  • 14.3. SECURITY ENABLER

15.0. BYOD MARKET PROJECTIONS

  • 15.1. GLOBAL CONNECTED DEVICE PROJECTION 2015-2020
  • 15.2. M2M VS. PERSONAL HANDHELD CONNECTION 2015-2020
  • 15.3. CONNECTED DEVICE IN REGIONS 2015-2020
  • 15.4. GLOBAL MOBILE DATA TRAFFIC 2015-2019
  • 15.5. MOBILE DATA TRAFFIC BY REGION 2015-2019
  • 15.6. GLOBAL BYOD DEVICES IN WORKPLACE 2015-2020
  • 15.7. BYOD DEVICES BY MOBILE OS 2015-2020
    • 15.7.1. WHY APPLE IS STILL NUMBER ONE
  • 15.8. WORKPLACE BYOD BY TYPES OF DEVICES 2015-2020
  • 15.9. BYOD ADOPTION IN ENTERPRISE 2015-2020
  • 15.10. GLOBAL CONNECTED DEVICES BY NETWORK 2015-2029
  • 15.11. GLOBAL CONNECTED TRAFFIC SOURCE 2015-2019
  • 15.12. GLOBAL CONNECTED CLOUD TRAFFIC 2015-2019
  • 15.13. GLOBAL CELLULAR VS. OFFLOAD TRAFFIC 2015-2019
  • 15.14. ENTERPRISE SUPPORT PLAN FOR BYOD
  • 15.15. DURATION OF BYOD POLICY IN ENTERPRISE
  • 15.16. PERCENT OF MOBILE BUDGET SPENT ON BYOD BY COUNTRY
  • 15.17. DESKTOP VIRTUALIZATION STRATEGY IMPLEMENTATION

16.0. BYOD BY REGION

  • 16.1. GROWTH MARKET VS. MATURE MARKET
    • 16.1.1. LACK OF BYOD MANAGEMENT
    • 16.1.2. AVERAGE OF NUMBER OF BYOD DEVICE 2014
    • 16.1.3. BYOD BENEFITS 2014
  • 16.2. EUROPE ANALYSIS
    • 16.2.1. UNITED KINGDOM
    • 16.2.2. FRANCE
    • 16.2.3. GERMANY
    • 16.2.4. RUSSIA
  • 16.3. APAC ANALYSIS
    • 16.3.1. CHINA
    • 16.3.2. INDIA
  • 16.4. LATIN AMERICA
    • 16.4.1. BRAZIL
    • 16.4.2. MEXICO

17.0. BYOD VENDOR ANALYSIS

  • 17.1. HP
    • 17.1.1. SWOT ANALYSIS
  • 17.2. DELL
    • 17.2.1. MOBILE ACCESS SOLUTION SET
    • 17.2.2. CLOUD EXPERIENCE SOLUTION SET
    • 17.2.3. NETWORK OPTIMIZATION SOLUTION SET
    • 17.2.4. APPLICATION MODERNIZATION AND DEVELOPMENT SOLUTION SET
    • 17.2.5. SWOT ANALYSIS
  • 17.3. HUAWEI
    • 17.3.1. SWOT ANALYSIS
  • 17.4. RUCKS AND AEROHIVE
    • 17.4.1. SWOT ANALYSIS
  • 17.5. ARUBA 105
    • 17.5.1. SWOT ANALYSIS
  • 17.6. CISCO
    • 17.6.1. CISCO MERAKI
    • 17.6.2. SWOT ANALYSIS
  • 17.7. CITRIX 110
    • 17.7.1. SWOT ANALYSIS
  • 17.8. ENTERASYS: MOBILE IDENTITY ACCESS MANAGEMENT
    • 17.8.1. SWOT ANALYSIS
  • 17.9. ARMOR5
    • 17.9.1. COMPANY DESCRIPTION
    • 17.9.2. CLOUD BASED SOLUTION
    • 17.9.3. ZERO TOUCH DEPLOYMENT MODEL
    • 17.9.4. BEST SECURITY: REDUCE ATTACK SURFACE
    • 17.9.5. SWOT ANALYSIS
  • 17.10. DOMO
    • 17.10.1. COMPANY DESCRIPTION
    • 17.10.2. BUSINESS INTELLIGENCE (BI) VS. BIG DATA & ANALYTICS
    • 17.10.3. BI DASHBOARD AND EXECUTIVE MANAGEMENT PLATFORM
    • 17.10.4. SWOT ANALYSIS
  • 17.11. DIVIDE BY ENTERPROID
    • 17.11.1. COMPANY DESCRIPTION
    • 17.11.2. DIVIDE PLATFORM
    • 17.11.3. VIRTUALIZATION & DEVICE MANAGEMENT
    • 17.11.4. WORK-LIFE BALANCE & DIVIDE PLATFORM
    • 17.11.5. SWOT ANALYSIS
  • 17.12. MOBILESPACES
    • 17.12.1. COMPANY DESCRIPTION
    • 17.12.2. ENTERPRISE APP SORE, EMPLOYEE PRIVACY AND BYOD SOLUTION
    • 17.12.3. SWOT ANALYSIS
  • 17.13. MOCANA
    • 17.13.1. COMPANY DESCRIPTION
    • 17.13.2. SOLUTION APPROACH
    • 17.13.3. PRODUCT & SERVICE PARADIGM
    • 17.13.4. INDUSTRY SERVICE HORIZON
    • 17.13.5. SOLUTION FRAMEWORK
    • 17.13.6. MOBILE APP PROTECTION & BYOD
    • 17.13.7. APP WRAPPING SOLUTION AND MOCANA PARTNERSHIP WITH APPERIAN
    • 17.13.8. SWOT ANALYSIS FOR MAP
  • 17.14. APPERIAN
    • 17.14.1. COMPANY DESCRIPTION
    • 17.14.2. MAM PLATFORM AND EASE
    • 17.14.3. SWOT ANALYSIS
    • 17.15. ROAMBI
    • 17.15.1. COMPANY DESCRIPTION
    • 17.15.2. ROAMBI FLOW

18.0. MDM VENDOR ANALYSIS

  • 18.1. AIRWATCH
  • 18.2. APPLE PROFILE MANAGER
  • 18.3. BOXTONE
  • 18.4. CENTRIFY
  • 18.5. GOOD TECHNOLOGY
  • 18.6. IBM
  • 18.7. LANDESK
  • 18.8. MICROSOFT
  • 18.9. MOBILEIRON
  • 18.10. SAP
  • 18.11. SYMANTEC
  • 18.12. ZENPRISE/CITRIX
  • 18.13. FIBERLINK
  • 18.14. MOTOROLA SOLUTIONS
  • 18.15. BLACKBERRY
  • 18.16. TREND MICRO
  • 18.17. SOTI
  • 18.18. ABSOLUTE SOFTWARE
  • 18.19. ANTENNA SOFTWARE
  • 18.20. KONY
  • 18.21. MCAFEE
  • 18.22. SMITH MICRO SOFTWARE
  • 18.23. SOPHOS
  • 18.24. TANGOE
  • 18.25. WAVELINK
  • 18.26. JUNIPER NETWORKS
  • 18.27. CSC

19.0. CASE ANALYSIS

  • 19.1. MICROSOFT AZURE ACTIVE DIRECTORY CASE
    • 19.1.1. BENEFITS ACHIEVED BY COMPANIES
  • 19.2. IBM
  • 19.3. DIGID
  • 19.4. DIMENSION DATA

20.0. CONCLUSIONS AND RECOMMENDATIONS

  • 20.1. RECOMMENDED CHECKLIST TO SELECT BYOD SOLUTION PROVIDER

Figures

  • Figure 1: Driving Forces of Mobility Adoption of Enterprises
  • Figure 2: BYOD Opportunities Allocation 2015-2017
  • Figure 3: Activity cycle of Enterprise Mobility
  • Figure 4: Deployment Difficulties of Enterprise Mobility
  • Figure 5: Steps for Implementing BYOD in SMBs
  • Figure 6: Growth Predictions of Government Agencies Using BYOD 2015-2018
  • Figure 7: Percent of Workers not willing to Work from Home
  • Figure 8: Strategic Consideration of BYOD Organization
  • Figure 9: Decision Making Factor for Cloud BYOD Adoption
  • Figure 10: BYOD Applications Available via Cloud 2015-2018
  • Figure 11: Enterprise Function Migration to Cloud Services
  • Figure 12: Enterprise Benefits Derived from Cloud Adoption
  • Figure 13: Personal Devices in Workplace as Corporate Security Risk 2015
  • Figure 14: Professionals use 2 Devices vs. 3 or More Devices 2015-2020
  • Figure 15: Employee Personal Devices vs. Ownership Status 2015-2020
  • Figure 16: Right vs. Want to Break Company Rule vs. Dislike MDM on Device 2015
  • Figure 17: Company Allow Personal Devices vs. MDM 2015-2020
  • Figure 18: Company Device Policy Breakdown 2015
  • Figure 19: Companies Who Allow Personal Devices in Workplace 2015
  • Figure 20: Personal Mobile Devices in Workplace 2015
  • Figure 21: Companies Managing Mobility in Different Ways 2015
  • Figure 22: VMware's Horizon Mobile and Horizon Mobile Manager
  • Figure 23: Employees using BYOD Devices 2015
  • Figure 24: Obstacles for BYOD Adoption 2015
  • Figure 25: Security Issues and Impact on BYOD Decision Making 2015
  • Figure 26: Mobile Security Options for BYOD and HYOD
  • Figure 27: Connected Device by Region 2015-2020
  • Figure 28: Global Mobile Data Traffic Exabytes per Month 2015-2019
  • Figure 29: Mobile Data Traffic Exabytes per Month by Region 2015-2019
  • Figure 30: Global BYOD Devices 2015-2020
  • Figure 31: Percentage of OS Used in BYOD Environment 2015-2020
  • Figure 32: Workplace BYOD (Smartphone vs. Tablet & Wearable) 2015-2020
  • Figure 33: BYOD Adoption (Large vs. Medium &. Small Enterprise) 2015-2020
  • Figure 34: Global Connected Device by Technology 2015-2019
  • Figure 35: Global Connected Traffic Source 2015-2019
  • Figure 36: Global Connected Traffic by Cloud vs. Non-Cloud 2015-2019
  • Figure 37: Global Connected Traffic by Cellular Source vs. Offload 2015-2019
  • Figure 38: Percent of Enterprise Support Plan for BYOD Adoption
  • Figure 39: Length of Time BYOD Policy in Enterprise by Percent of Enterprise
  • Figure 40: Percent of Mobile Budget spent on BYOD by Country
  • Figure 41: Desktop Virtualization Strategy Implementation by Region
  • Figure 42: BYOD in Developed vs. Developing Markets
  • Figure 43: Lack of BYOD Management Issues
  • Figure 44: Average Number of Connected Devices per Knowledge Worker
  • Figure 45: BYOD Benefits to the Company by Country
  • Figure 46: IMC BYOD Solution of HP
  • Figure 47: SWOT Analysis for Dell BYOD Solution
  • Figure 48: Different Phase for BYOD
  • Figure 49: Huawei BYOD Solution Component
  • Figure 50: Secure and Productive BYOD for the Enterprise
  • Figure 51: Main Components for BYOD Solutions
  • Figure 52: Cisco BYOD Solution Framework
  • Figure 53: Cisco BYOD Device Projections 2015-2017
  • Figure 54: Zero Touch Model
  • Figure 55: CloudSpace Architecture
  • Figure 56: Web Virtualization Engine (WVE) Architecture
  • Figure 57: Interactive Dashboard for Enterprise
  • Figure 58: Cross-Platform view of BI Dashboard
  • Figure 59: Divide Screenshot
  • Figure 60: Work-life Separation Interface of Divide
  • Figure 61: Native Apps Screenshot on Smartphone
  • Figure 62: Product & Service
  • Figure 63: Industry Coverage
  • Figure 64: Mocana Solution Framework
  • Figure 65: MAP Framework
  • Figure 66: EASE Admin Portal
  • Figure 67: Roambi Flow

Tables

  • Table 1: Comparative Analysis of 5 MDM Vendors-Part 1
  • Table 2: Comparative Analysis of 5 MDM Vendors-Part 2
  • Table 3: Global Connected Device 2015-2020
  • Table 4: M2M and Personal Handheld Connections 2015-2020
  • Table 5: SWOT Analysis for Huawei BYOD Solution
  • Table 6: SWOT Analysis for Aerohive BYOD Solution
  • Table 7: SWOT Analysis for Aruba BYOD Solution
  • Table 8: SWOT Analysis for Citrix BYOD Solution
  • Table 9: SWOT Analysis for Enterasys BYOD Solution
  • Table 10: Three Years Cost vs. Saving of Transactiv with Windows Azure
  • Table 11: Success Story of IBM using BYOD
  • Table 12: Questions to Answer before Finalizing BYOD Solution Provider

Cloud Application Marketplace 2015-2020

1.0. EXECUTIVE SUMMARY

2.0. OVERVIEW OF CLOUD COMPUTING

  • 2.1. UNDERSTANDING CLOUD COMPUTING
    • 2.1.1. CLOUD COMPUTING SERVICES
  • 2.2. CLOUD FOUNDATIONS
    • 2.2.1. CATEGORIES OF CLOUD COMPUTING DEPLOYMENT MODEL
    • 2.2.2. GRID COMPUTING
    • 2.2.3. GRID COMPUTING MARKET SEGMENTATION
  • 2.3. CLOUD TECHNOLOGIES AND ARCHITECTURE
    • 2.3.1. SOFTWARE DEFINED NETWORKING (SDN)
    • 2.3.2. SDN DEPLOYMENT MODELS
    • 2.3.3. VIRTUALIZATION (SERVER VS. HARDWARE VS. DESKTOP VS. STORAGE)
  • 2.4. CLOUD COMPUTING AND VIRTUALIZATION
  • 2.5. MOVING BEYOND CLOUD COMPUTING
    • 2.5.1. A 'GLOCAL' CLOUD
  • 2.6. RISE OF THE CLOUD-BASED NETWORKED ENTERPRISE
  • 2.7. GENERAL CLOUD SERVICE ENABLERS
    • 2.7.1. WIRELESS BROADBAND CONNECTIVITY
    • 2.7.2. SECURITY SOLUTIONS
    • 2.7.3. PRESENCE AND LOCATION
  • 2.8. PERSONAL CLOUD SERVICE ENABLERS
    • 2.8.1. IDENTITY MANAGEMENT
    • 2.8.2. PREFERENCE MANAGEMENT

3.0. CLOUD SERVICE ANALYSIS

  • 3.1. CLOUD SERVICE SEGMENTATION
    • 3.1.1. BUSINESS TO BUSINESS (B2B)
    • 3.1.2. BUSINESS TO CONSUMER (B2C)
  • 3.2. CORE CLOUD SERVICES
    • 3.2.1. INFRASTRUCTURE AS A SERVICE (IAAS)
    • 3.2.2. PLATFORM AS A SERVICE (PAAS)
    • 3.2.3. SOFTWARE AS A SERVICE (SAAS)
    • 3.2.4. DIFFERENCES BETWEEN IAAS, SAAS, AND PAAS
  • 3.3. EMERGING MODELS: XAAS (EVERYTHING AS A SERVICE)
    • 3.3.1. BUSINESS PROCESS AS A SERVICE (BPAAS)
    • 3.3.2. COMMUNICATION AS A SERVICE (CAAS)
    • 3.3.3. MONITORING AS A SERVICE (MAAS)
    • 3.3.4. NETWORK-AS-A-SERVICE (NAAS)
    • 3.3.5. STORAGE AS A SERVICE (SAAS)
    • 3.3.6. DATA AS A SERVICE (DAAS)
  • 3.4. DATA AS A SERVICE ECOSYSTEM
    • 3.4.1. THE DRIVERS OF DATA-AS-A-SERVICE
    • 3.4.2. BUSINESS INTELLIGENCE AND DAAS INTEGRATION
    • 3.4.3. THE CLOUD ENABLER DAAS
    • 3.4.4. XAAS DRIVES DAAS
    • 3.4.5. THE DAAS ECOSYSTEM
    • 3.4.6. DAAS ELEMENTS
    • 3.4.7. THE ROLE OF DATA MARTS
    • 3.4.8. BEST PRACTICES IN DAAS
    • 3.4.9. BENEFITS OF DAAS
    • 3.4.10. CHALLENGES OF DATA AS A SERVICE
    • 3.4.11. APIS AND DATABASE
    • 3.4.12. THE NEED FOR FEDERATED DATABASE MODEL
  • 3.5. ENTERPRISE RESOURCE PLANNING IN THE CLOUD
  • 3.6. SUPPLY CHAIN MANAGEMENT IN THE CLOUD

4.0. INDUSTRY VERTICALS IN THE CLOUD

  • 4.1. FINANCE AND BANKING IN THE CLOUD
    • 4.1.1. AGILITY, EFFICIENCY, AND SIMPLIFIED DELIVERY
    • 4.1.2. PRIORITIZING THE CLOUD
  • 4.2. RETAIL IN THE CLOUD
  • 4.3. HEALTHCARE IN THE CLOUD
    • 4.3.1. KEY BENEFITS OF CLOUD TECHNOLOGY
  • 4.4. TELECOMMUNICATIONS IN THE CLOUD
    • 4.4.1. OPPORTUNITIES AND CHALLENGES
    • 4.4.2. SOLUTIONS
  • 4.5. GOVERNMENT AND DEFENSE IN THE CLOUD
    • 4.5.1. PROS AND CONS OF THE FEDERAL CLOUD
  • 4.6. WORKFORCE IN THE CLOUD
    • 4.6.1. HUMAN CAPITAL MANAGEMENT IN THE CLOUD
    • 4.6.2. TRAINING AND EDUCATION IN THE CLOUD
    • 4.6.3. COLLABORATION IN THE CLOUD
    • 4.6.4. OFFICE AUTOMATION IN THE CLOUD
  • 4.7. CUSTOMERS IN THE CLOUD
    • 4.7.1. CUSTOMER RELATIONSHIP IN THE CLOUD
    • 4.7.2. COMMERCE AND PAYMENTS IN THE CLOUD
  • 4.8. EMERGING CLOUD BASED APPLICATIONS
    • 4.8.1. B2B APPLICATIONS
    • 4.8.2. BIG DATA AS A SERVICE (BDAAS)
    • 4.8.3. B2C APPLICATIONS
    • 4.8.4. ENTERTAINMENT IN THE CLOUD: TV, VIDEO, GAMING AND MORE
  • 4.9. THE FUTURE OF CLOUD SERVICES
    • 4.9.1. EVERYTHING AS A SERVICE
    • 4.9.2. HOW XAAS DECREASES COSTS AND MAKES EVERYTHING FIT TOGETHER
  • 4.10. DATA CENTER PROVIDERS
  • 4.11. VIRTUALIZATION: ROLE AND IMPACT
    • 4.11.1. TYPES OF VIRTUALIZATION
    • 4.11.2. HOW VIRTUALIZATION AFFECTS COST STRUCTURES

5.0. CLOUD APPLICATION SERVICE MARKET FORECAST

  • 5.1. CLOUD SERVICE MARKET REVENUE FORECAST 2015-2020
  • 5.2. CLOUD SERVICE MARKET REVENUE BY TPES 2015-2020
  • 5.3. CLOUD SERVICE MARKET REVENUE BY CORE SEGMENTS OR MODELS 2015-2020
  • 5.4. CLOUD SAAS MARKET REVENUE BY SEGMENTS 2015-2020
  • 5.5. CLOUD PAAS MARKET REVENUE BY SEGMENTS 2015-2020
    • 5.5.1. CLOUD PAAS MARKET REVENUE BY SUB-SEGMENTS 2015-2020
  • 5.6. CLOUD IAAS MARKET REVENUE BY SEGMENTS 2015-2020
  • 5.7. PUBLIC CLOUD SERVICES MARKET REVENUE BY SEGMENTS 2015-2020
    • 5.7.1. PUBLIC CLOUD MANAGEMENT & SECURITY SERVICES MARKET REVENUE BY SEGMENTS 2015-2020
    • 5.7.2. PUBLIC CLOUD BPAAS SERVICES MARKET REVENUE BY SEGMENTS 2015-2020
  • 5.8. CLOUD SERVICE MARKET REVENUE BY GEOGRAPHIC REGION 2015-2020
  • 5.9. CLOUD APPLICATION SERVICE REVENUE BY INDUSTRY VERTICAL 2015-2020
  • 5.10. CLOUD APPLICATION ADOPTION TREND AMONG PERCENT OF ORGANIZATIONS BY DEPLOYMENT MODELS 2015-2020
  • 5.11. CLOUD APPLICATION ADOPTION TREND AMONG PERCENT OF ORGANIZATIONS BY INDUSTRY VERTICALS 2015-2020
  • 5.12. CLOUD INVESTMENT PERCENT TO INDUSTRY APPLICATIONS 2015
  • 5.13. BENEFITS OF CLOUD APPLICATION SERVICE ADOPTION OVER IN-HOUSE IT SERVICES
  • 5.14. PRIVATE CLOUD STORAGE SUBSCRIPTION FORECAST 2015-2020

6.0. CLOUD APPLICATION SERVICE VENDOR ANALYSIS

  • 6.1. OFFICE AUTOMATION APPLICATION
    • 6.1.1. ZOHO
    • 6.1.2. TECHINLINE
    • 6.1.3. WINDOWS LIVE MESH
    • 6.1.4. DROPBOX
    • 6.1.5. LOGMEIN
    • 6.1.6. MICROSOFT OFFICE 365
    • 6.1.7. NOODLE
  • 6.2. CRM APPLICATIONS
    • 6.2.1. ADDRESSTWO
    • 6.2.2. ALLCLIENTS
    • 6.2.3. MAXIMIZER
    • 6.2.4. SALESCLOUD FROM SALESFORCE
    • 6.2.5. SALESNEXUS
  • 6.3. DATA CENTER APPLICATIONS
    • 6.3.1. GOOGLE
    • 6.3.2. MICROSOFT
    • 6.3.3. SWITCH SUPER NAP
    • 6.3.4. RANGE INTERNATIONAL INFORMATION HUB
  • 6.4. CORE CLOUD SERVICE PROVIDERS
    • 6.4.1. AMAZON
    • 6.4.2. VERIZON
    • 6.4.3. IBM
    • 6.4.4. SALESFORCE.COM
    • 6.4.5. CSC
    • 6.4.6. CENTURYLINK
    • 6.4.7. SAVVIS
    • 6.4.8. JOYENT
    • 6.4.9. MICROSOFT
    • 6.4.10. RACKSPACE
    • 6.4.11. FUJITSU
    • 6.4.12. HP
  • 6.5. CLOUD NETWORK OPERATORS
    • 6.5.1. CHINA MOBILE LIMITED
    • 6.5.2. VODAFONE GROUP
    • 6.5.3. TELENOR GROUP
    • 6.5.4. AMERICA MOVIL
  • 6.6. ENTERPRISE CLOUD APPLICATION
    • 6.6.1. SALESFORCE.COM
    • 6.6.2. BOX
    • 6.6.3. CRASHPLAN
    • 6.6.4. AMAZON WEB SERVICES
    • 6.6.5. EASY VISTA

7.0. CARRIER CLOUD OPPORTUNITY

  • 7.1. CLOUD INFRASTRUCTURE AND SERVICES IN TELECOMMUNICATIONS
    • 7.1.1. CLOUD RAN
  • 7.2. MOBILE CONSUMER CLOUD SERVICES
    • 7.2.1. CONSUMER MOBILITY AND THE CLOUD: STATISTICS AND FORECASTS
  • 7.3. COMMERCIAL CONSIDERATIONS
    • 7.3.1. WHAT CONSUMERS WILL STORE IN AND ACCESS FROM THE CLOUD
    • 7.3.2. WHAT DEVICES CONSUMERS WILL USE TO ACCESS THE CLOUD
    • 7.3.3. WHERE AND HOW CONSUMERS WILL ACCESS THE CLOUD
    • 7.3.4. WHAT COMPANIES DO CONSUMERS IDENTIFY WITH CLOUD SERVICES
    • 7.3.5. CONSUMER WILLINGNESS TO PAY FOR PERSONAL CLOUD SERVICES
  • 7.4. KEY CONCERNS AND SOLUTIONS FOR PERSONAL CLOUD SERVICES
    • 7.4.1. LTE AND ANYWHERE, ANYTIME, ANY DEVICE ACCESS
    • 7.4.2. LTE DRIVES CLOUD GROW ACCELERATION VIA USER GENERATED CONTENT (UGC)
    • 7.4.3. DIGITAL RIGHTS MANAGEMENT (DRM)
    • 7.4.4. NETWORK AND DEVICE OPTIMIZATION
    • 7.4.5. CLOUD DATA SECURITY
    • 7.4.6. IDENTITY MANAGEMENT FOR CLOUD SERVICES
    • 7.4.7. CLOUD SERVICES BROKERING AND CLOUD MEDIATION
  • 7.5. MOBILE NETWORK OPERATOR VAS APPLICATION VS. OTT APPLICATIONS
  • 7.6. TELECOM APIS AND THE CLOUD
    • 7.6.1. ROLE OF API'S IN THE CLOUD
    • 7.6.2. ENTERPRISE API PROVIDERS AND CLOUD SERVICES
    • 7.6.3. TELECOM API'S AND THE CLOUD
  • 7.7. GREATER MOBILE CLOUD COMPUTING
    • 7.7.1. BYOC (BRING YOUR OWN CLOUD) AND INCREASED SECURITY
  • 7.8. CARRIER CLOUD SERVICE STRATEGY
    • 7.8.1. CONSUMER CLOUD SERVICES KEY TO GROWTH IN CARRIER DATA SERVICES
    • 7.8.2. CARRIER CLOUD SERVICES TO DRIVE VALUE-ADDED SERVICES GROWTH
    • 7.8.3. PERSONAL CLOUD SERVICES TO IMPROVE CARRIER TOP LINE REVENUE AND PROFITS
  • 7.9. TELECOM BENEFITS OF OFFERING CLOUD SERVICES
    • 7.9.1. VALUE PROPOSITION FOR TELECOM
    • 7.9.2. WEB-BASED APPLICATIONS PROMOTE IT INDEPENDENCE
    • 7.9.3. CLOUD-BASED MANAGED SERVICES PRODUCES REVENUE
    • 7.9.4. INCREASE DATA CENTER EFFICIENCY AND OPERATIONS
    • 7.9.5. DIFFERENTIATING SERVICE PROVIDERS
  • 7.10. CARRIER ADVANTAGES IN CLOUD ECOSYSTEM
    • 7.10.1. SERVICE-ORIENTATION
    • 7.10.2. PERFORMANCE
    • 7.10.3. SECURITY
  • 7.11. CARRIER CHALLENGES
    • 7.11.1. BUSINESS-CLASS SERVICES
    • 7.11.2. STANDARDIZATION
    • 7.11.3. PORTABILITY
  • 7.12. CLOUD BACK UP SERVICES FOR TELECOM
    • 7.12.1. CTERA AND TELECOM ITALIA
    • 7.12.2. HUAWEI AND CHINA TELECOM
    • 7.12.3. HUAWEI PUBLIC CLOUD AND TELKOMSIGMA
    • 7.12.4. HUAWEI PUBLIC CLOUD AND CHINACOMM
    • 7.12.5. HUAWEI DATA CENTER AND VERT BRAZIL
    • 7.12.6. ACRONIS
    • 7.12.7. VODAFONE
    • 7.12.8. OPENSTACK
    • 7.12.9. HP AND NOKIA
    • 7.12.10. ATOS
    • 7.12.11. VOX TELECOM
    • 7.12.12. ZAJIL TELECOM
    • 7.12.13. OSSTELCO
  • 7.13. CLOUD BACKUP SERVICE PROVIDER COMPEITION
  • 7.14. IMPACT OF ICLOUD

8.0. CONCLUSIONS AND RECOMMENDATIONS

  • 8.1. RECOMMENDATIONS
    • 8.1.1. CONTENT DELIVERY NETWORKS (CDN)
    • 8.1.2. MOBILE PERSONAL CLOUD SERVICES
    • 8.1.3. TELECOM OPERATOR

Figures

  • Figure 1: Cloud Computing Concept
  • Figure 2: Cloud Service Models
  • Figure 3: Benefit Chart of Cloud Computing
  • Figure 4: How Grid Computing Works
  • Figure 5: Cloud Computing Architecture
  • Figure 6: Server Virtualization Architecture
  • Figure 7: Mixed IT Environment
  • Figure 8: Cloud Professional B2B Service Provider Matrix
  • Figure 9: Cloud Computing Stack
  • Figure 10: Deployment Ratio of by Categories of SaaS Application
  • Figure 11: Difference between IaaS, PaaS, and SaaS
  • Figure 12: DaaS Ecosystem
  • Figure 13: Data Value Chain in DaaS Ecosystem
  • Figure 14: Data Value Chain with Value-added Enrichment
  • Figure 15: DaaS Elements
  • Figure 16: DaaS Benefits
  • Figure 17: Cloud Services and APIs
  • Figure 18: Cloud ERP vs. On-premise ERP
  • Figure 19: SCM Cloud Structure
  • Figure 20: Financial Services in the Cloud
  • Figure 21: Retail in the Cloud
  • Figure 22: Telecom Cloud Focus
  • Figure 23: Cloud Computing in Government and Defense
  • Figure 24: Office Automation in the Cloud
  • Figure 25: Cloud Burst of Big Data
  • Figure 26: Top Themes in the Cloud
  • Figure 27: Cloud Service Market Revenue 2015-2020
  • Figure 28: Private Cloud Storage Subscription Forecast 2015-2020
  • Figure 29: Datacenter Infrastructure
  • Figure 30: Virtualization of the Mobile Network
  • Figure 31: DevOps (Development Operations)
  • Figure 32: Cloud Radio Access Network (C-RAN)
  • Figure 33: How People User their Mobile Phone
  • Figure 34: Personal Content on Home Computer and Mobile Device
  • Figure 35: IDS1000 AIO 137 Figure 36: IDS1000 cluster

Tables

  • Table 1: Private Cloud B2C Service Provider Matrix
  • Table 2: Cloud Service Market Revenue by Private and Public Cloud 2015-2020
  • Table 3: Total Revenue Share by Private vs. Public Cloud Service 2015-2020
  • Table 4: Cloud Service Market Revenue by Core Segments or Models 2015-2020
  • Table 5: Total Revenue Share by SaaS vs. PaaS vs. IaaS 2015-2020
  • Table 6: Cloud Market Revenue by SaaS Segments 2015-2020
  • Table 7: Cloud SaaS Revenue Share by Segments during 2015-2020
  • Table 8: Cloud PaaS Market Revenue by Segments 2015-2020
  • Table 9: Percent of Cloud PaaS Revenue Share by Segments during 2015-2020
  • Table 10: Cloud PaaS Market Revenue by Sub-Segments 2015-2020
  • Table 11: Cloud PaaS Revenue Share by Sub-Segments during 2015-2020
  • Table 12: Cloud IaaS Market Revenue by Segments 2015-2020
  • Table 13: IaaS Cloud Service Market Revenue by Segments during 2015-2020
  • Table 14: Public Cloud Services Market Revenue by Segments 2015-2020
  • Table 15: Public Cloud Service Revenue by Segments during 2015-2020
  • Table 16: Public Cloud Mgt & Security Services Mkt Rev by Segments 2015-2020
  • Table 17: Public Cloud Mgt & Security Services Rev by Segment 2015-2020
  • Table 18: Public Cloud BPaaS Services Market Revenue by Segments 2015-2020
  • Table 19: Public Cloud BPaaS Services Rev by Segment 2015-2020
  • Table 20: Cloud Service Market Revenue by Region 2015-2020
  • Table 21: Cloud Service Market Revenue Share by Region 2015-2020
  • Table 22: Cloud Application Service Revenue by Industry Vertical 2015-2020
  • Table 23: Cloud Application Service Revenue by Industry Verticals 2015-2020
  • Table 24: Cloud Application Adoption Trend by Deployment Models 2015-2020
  • Table 25: Cloud Application Adoption Trend by Industry Verticals 2015-2020
  • Table 26: Cloud Investment in Industry Applications 2015
  • Table 27: Benefits of Cloud App Service Adoption over In-House IT Services
  • Table 28: Basic Features or Functionality of Mobile Personal Cloud Services

Social Business Services in the Cloud: Market Analysis and Forecast 2015-2020

1.0. EXECUTIVE SUMMARY

2.0. OVERVIEW

  • 2.1. WHAT IS SOCIAL BUSINESS?
  • 2.2. SOCIAL BUSINESS SERVICES AND SAAS SOLUTION
  • 2.3. CLOUD CONVERGENCE WITH SOCIAL TECHNOLOGIES
  • 2.4. CLOUD SOLUTION DEVELOPMENT FOR SOCIAL BUSINESS
  • 2.5. MOBILE, WEARABLE AND ANALYTICS
  • 2.6. SECURITY COMPLIANCE AND NEW STAKEHOLDERS
  • 2.7. SOCIAL CUSTOMER
  • 2.8. ENTERPRISE SOCIAL ADOPTION
  • 2.9. CLOUD SOCIAL COLLABORATION
  • 2.10. SUCCESS PRINCIPLES OF SOCIAL BUSINESS
    • 2.10.1. RECOGNIZE SOCIAL TECHNOLOGY
    • 2.10.2. BUILDING SUCCESS ARCHITECTURE
    • 2.10.3. MULTISTEP APPROACH
    • 2.10.4. TREAT USERS FIRST
    • 2.10.5. STRATEGIC THOUGHT APPROACH
  • 2.11. SOCIAL ENTERPRISE DEPLOYMENT AND CHALLENGES TO ADDRESS
  • 2.12. BUILDING BLOCK TECHNIQUES
  • 2.13. IMPROVING ENTERPRISE SOCIAL CAPABILITIES
  • 2.14. ASSESSING ENTERPRISE SOCIAL CAPABILITIES
  • 2.15. EFFECTIVENESS OF SOCIAL BUSINESS CAPABILITIES
    • 2.15.1. BUSINESS CHALLENGE
    • 2.15.2. SOCIAL CAPABILITIES
    • 2.15.3. BUSINESS OUTCOMES
    • 2.15.4. INDUSTRY NUANCES
    • 2.15.5. TECHNICAL CHALLENGES
  • 2.16. SWOT ANALYSIS
    • 2.16.1. STRENGTHS & OPPORTUNITIES
    • 2.16.2. WEAKNESSES & THREATS
  • 2.17. INDUSTRY VERTICAL

3.0. TECHNOLOGY, APPLICATION AND PROVIDER

  • 3.1. MARKETING AUTOMATION
    • 3.1.1. MARKETO
    • 3.1.2. PARDOT
    • 3.1.3. ELOQUA
    • 3.1.4. CUSTOMER.IO
    • 3.1.5. HUBSPOT
    • 3.1.6. ADROLL
    • 3.1.7. PICA9
    • 3.1.8. CANTERRIS
    • 3.1.9. BREMY
    • 3.1.10. OUTMARKET
    • 3.1.11. BUSSBUILDER PRO
    • 3.1.12. SALESFUSION
    • 3.1.13. GENOO
    • 3.1.14. BIZIBLE
    • 3.1.15. ETRIGUE
    • 3.1.16. ALLOCADIA
    • 3.1.17. SALES ENGINE INTERNATIONAL
    • 3.1.18. ONTRAPORT
    • 3.1.19. LEADSQUARED
    • 3.1.20. MARCOMCENTRAL
    • 3.1.21. AMBASSADOR
    • 3.1.22. INTEGRATE
    • 3.1.23. BRANDMAKER
    • 3.1.24. BUZZPORTAL
    • 3.1.25. ACTIVE CONVERSATION
    • 3.1.26. COMMUNIGATOR
    • 3.1.27. AGILLIC
    • 3.1.28. APRIX SOLUTIONS
    • 3.1.29. DISTRIBION
    • 3.1.30. SALESFORMICS
    • 3.1.31. ELATERAL
    • 3.1.32. CASCADE
    • 3.1.33. GRAVITY FACTOR
    • 3.1.34. BRONTO
    • 3.1.35. GREENROPE
    • 3.1.36. IFBYPHONE
    • 3.1.37. NEXTBEE
    • 3.1.38. ZOHO
    • 3.1.39. ACTIVECAMPAIGN
    • 3.1.40. CONSTANTCONTACT
    • 3.1.41. MAILCHIMP
    • 3.1.42. ASANA
    • 3.1.43. GLIFFY
    • 3.1.44. JUMPLEAD
    • 3.1.45. SIMPLYCAST
    • 3.1.46. DRIP
    • 3.1.47. KENTICO
    • 3.1.48. LEADSIUS
    • 3.1.49. KAHUNA
    • 3.1.50. DEMANDBASE
  • 3.2. SOCIAL MEDIA MANAGEMENT & MONITORING
    • 3.2.1. SUGARCRM
    • 3.2.2. MARKETO
    • 3.2.3. PIPELINEDEALS
    • 3.2.4. SALESFORCE.COM SERVICE CLOUD
    • 3.2.5. SALESFORCE MARKETING CLOUD
    • 3.2.6. ZIPWIRE
    • 3.2.7. SALESOUTLOOK CRM
    • 3.2.8. SMARTTOUCH
    • 3.2.9. CALLIDUSCLOUD MARKETING AUTOMATION
    • 3.2.10. PROPERTYBASE
  • 3.3. ENTERPRISE COLLABORATION & SOCIAL
    • 3.3.1. CO-OP
    • 3.3.2. CYN.IN
    • 3.3.3. CUBETREE
    • 3.3.4. HASHWORK
    • 3.3.5. JAIKU
    • 3.3.6. OBAYOO
    • 3.3.7. PRESENT.LY
    • 3.3.8. QONTEXT
    • 3.3.9. SHARETRONIX
    • 3.3.10. SNIPIA
    • 3.3.11. SOCIALCAST
    • 3.3.12. SOCIALTEXT
    • 3.3.13. SOCIALWOK
    • 3.3.14. STATUS.NET
    • 3.3.15. YAMMER
  • 3.4. WEB EXPERIENCE MANAGEMENT
    • 3.4.1. ADOBE EXPERIENCE MANAGER
    • 3.4.2. ZENDESK
    • 3.4.3. IBM TEALEAF
    • 3.4.4. SATMETRIX
    • 3.4.5. RESPONSETEK
    • 3.4.6. CLICKTALE
    • 3.4.7. KANA
    • 3.4.8. CLARABRIDGE
    • 3.4.9. SAS
    • 3.4.10. GEMIUS
    • 3.4.11. HUBSPOT
    • 3.4.12. MEDALLIA
    • 3.4.13. MAXYMISER
    • 3.4.14. USERZOOM
    • 3.4.15. UX360
    • 3.4.16. USABILITYTOOLS
    • 3.4.17. EKTRON
    • 3.4.18. EZPUBLISH 5
    • 3.4.19. HIPPO CMS 7.8
    • 3.4.20. SDL
    • 3.4.21. OPENTEXT
    • 3.4.22. KENTICO
    • 3.4.23. DRUPAL
    • 3.4.24. COREMEDIA
    • 3.4.25. SITECORE
  • 3.5. INFORMATION GOVERNANCE
    • 3.5.1. ACAVEO
    • 3.5.2. OSTIA
    • 3.5.3. RELTIO
  • 3.6. DIGITAL COMMERCE
    • 3.6.1. ABILITY COMMERCE
    • 3.6.2. BIG COMMERCE
    • 3.6.3. INTUIT ECOMMERCE
    • 3.6.4. SHOPIFY
    • 3.6.5. VENDIO
  • 3.7. CRM & CUSTOMER SUPPORT
    • 3.7.1. SUPPORTCENTER PLUS
    • 3.7.2. ISUPPORT
    • 3.7.3. BLAZEDESK
    • 3.7.4. C-DESK
    • 3.7.5. ACT!
    • 3.7.6. NABD
    • 3.7.7. FUZE SUITE
    • 3.7.8. LIVECHAT
    • 3.7.9. ESERVIZ
    • 3.7.10. HAPPYFOX
    • 3.7.11. MYTIPS
    • 3.7.12. LIVEHELPNOW SUITE
    • 3.7.13. MIGHTYCALL
    • 3.7.14. CHATAROO
    • 3.7.15. CUSTOMANSWERS CRM
    • 3.7.16. ALWAYSUPPORT
    • 3.7.17. CHAT INTERFACE FOR OPERATOR
    • 3.7.18. NICKELLED
    • 3.7.19. OXYGEN SERVICE DESK
    • 3.7.20. ZOHO SURVEY
    • 3.7.21. CUSTOMER SUPPORT SUITE
    • 3.7.22. BOLDCHAT
    • 3.7.23. ACHIEVER CRM
    • 3.7.24. ACTIVATE
    • 3.7.25. AGENDIZE
    • 3.7.26. AGI SELF SERVICE
    • 3.7.27. ALTITUDE UCI SUITE
    • 3.7.28. ANY REQUEST
    • 3.7.29. AURIC PROSPECTOR
    • 3.7.30. BAMBOO CRICKET
    • 3.7.31. BEETRACK
    • 3.7.32. BRAND EMBASSY
    • 3.7.33. CARECALL
    • 3.7.34. CASENGO
    • 3.7.35. CCS CUSTOMER SERVICE
    • 3.7.36. CINCOM SYNCHRONY
    • 3.7.37. CLICKDESK
    • 3.7.38. CLOSE SUPPORT
    • 3.7.39. CLOUD 9 SUPPORT
    • 3.7.40. COGNITIVE VIRTUAL ASSISTANT
    • 3.7.41. COMARCH NGSF
    • 3.7.42. COMM100 HELP DESKVIEW PROFILE
    • 3.7.43. COMMUNICATIONS CENTER
    • 3.7.44. CONSUMER RELATIONSHIP SYSTEM
    • 3.7.45. CONVERSOCIAL
    • 3.7.46. COVEO FOR CUSTOMER SERVICE & CRM
    • 3.7.47. CRM EXPRESS
    • 3.7.48. CRMDESK
    • 3.7.49. CRMUNLEASHED
    • 3.7.50. CUSTOMER CENTRIC FRAMEWORK
    • 3.7.51. CUSTOMER EXPERIENCE MANAGEMENT
    • 3.7.52. CUSTOMER SERVICE HELP DESK
    • 3.7.53. CUSTOMERFIRST
    • 3.7.54. E-TRACK
    • 3.7.55. ECOMMSOURCE
    • 3.7.56. EGAIN SUITE
    • 3.7.57. EPTICA ENTREPRISE SUITE
  • 3.8. ECM & FILE SHARING
    • 3.8.1. ACRONIS ACTIVECHO
    • 3.8.2. CITRIX SHAREFILE
    • 3.8.3. DRUVA INSYNC
    • 3.8.4. EGNYTE BUSINESS FILE SHARING
    • 3.8.5. EMC SYNCPLICITY
    • 3.8.6. HYLAND SOFTWARE
    • 3.8.7. IBM
    • 3.8.8. MICROSOFT
    • 3.8.9. OPENTEXT
    • 3.8.10. PERCEPTIVE SOFTWARE
    • 3.8.11. HUDDLE
    • 3.8.12. OXYGEN CLOUD
  • 3.9. WORKFORCE MANAGEMENT
    • 3.9.1. CHETU
    • 3.9.2. CERIDIAN
    • 3.9.3. ORACLE
    • 3.9.4. SAP/SUCCESSFACTORS
    • 3.9.5. IBM / KENEXA
    • 3.9.6. INFOR
    • 3.9.7. ULTIMATE SOFTWARE
    • 3.9.8. WORKDAY
    • 3.9.9. PEOPLEFLUENT
    • 3.9.10. ADP
    • 3.9.11. SILKROAD
    • 3.9.12. SABA
    • 3.9.13. FINANCIALFORCE HCM
    • 3.9.14. HRSOFT
    • 3.9.15. LUMESSE
    • 3.9.16. KRONOS
    • 3.9.17. SUMTOTAL SYSTEMS
    • 3.9.18. HALOGEN
    • 3.9.19. CORNERSTONE ONDEMAND
    • 3.9.20. BENEFITFOCUS EENROLLMENT
    • 3.9.21. PAYCOM
    • 3.9.22. PAYLOCITY

4.0. MARKET PROJECTIONS 2015-2020

  • 4.1. GLOBAL SOCIAL CLOUD BUSINESS APPLICATION MARKET 2015-2020
  • 4.2. SOCIAL CLOUD BUSINESS APPLICATION MARKET BY INDUSTRY SEGMENT 2015-2020
  • 4.3. SOCIAL CLOUD BUSINESS APPLICATION MARKET BY DEVELOPMENT PLATFORM 2015-2020
  • 4.4. SOCIAL CLOUD BUSINESS APPLICATION MARKET BY BUSINESS MODEL 2015-2020
  • 4.5. SOCIAL CLOUD BUSINESS APPLICATION MARKET BY GEOGRAPHIC SEGMENT 2015-2020
  • 4.6. ENTERPRISE ADOPTION RATE OF SOCIAL BUSINESS SERVICES 2015-2020
  • 4.7. SOCIAL BUSINESS APPLICATION MAJOR VENDOR REVENUE FORECAST 2015-2020

5.0. ECOSYSTEM ANALYSIS

  • 5.1. VENDOR
  • 5.2. CLOUD SERVICE PROVIDERS
  • 5.3. SOCIAL MEDIA NETWORK
  • 5.4. END USER
  • 5.5. SOFTWARE MANUFACTURER
  • 5.6. GOVERNMENT
  • 5.7. TELECOMMUNICATION

6.0. VENDOR ANALYSIS

  • 6.1. IBM
    • 6.1.1. IBM SMARTCLOUD
    • 6.1.2. IBM SMARTCLOUD ORCHESTRATOR
    • 6.1.3. IBM CONNECTIONS AND IBM NOTES AND DOMINO SOCIAL EDITION 9
    • 6.1.4. SWOT ANALYSIS
  • 6.2. SALESFORCE
    • 6.2.1. CHATTER
    • 6.2.2. SALESFORCE SOCIAL CRM
  • 6.3. MICROSOFT
    • 6.3.1. MICROSOFT OFFICE 365 AND CONNECTED EXPERIENCE
    • 6.3.2. PUBLIC CLOUD-WINDOWS AZURE
    • 6.3.3. PRIVATE CLOUD-WINDOWS SERVER AND SYSTEMS CENTER
    • 6.3.4. MICROSOFT DYNAMICS CRM ONLINE
    • 6.3.5. SHAREPOINT 2013
    • 6.3.6. MICROSOFT SKYDRIVE
    • 6.3.7. ORANGE AND MICROSOFT CLOUD PARTNERSHIP CASE
  • 6.4. YAMMER
    • 6.4.1. YAMMER AND OFFICE 365
    • 6.4.2. YAMMER AND SHAREPOINT SERVER
    • 6.4.3. SWOT ANALYSIS
  • 6.5. NEWSGATOR
    • 6.5.1. SUCCESS CASE WITH ACCENTURE
    • 6.5.2. SUCCESS CASE WITH CME FEDERAL CREDIT UNION
    • 6.5.3. SUCCESS CASE WITH AMERICAN FAMILY INSURANCE
  • 6.6. JIVE
    • 6.6.1. JIVE SOCIAL BUSINESS PLATFORM
    • 6.6.2. SWOT ANALYSIS
    • 6.6.3. SUCCESS CASE OF JIVE SOCIAL INTRANET WITH ALCATEL-LUCENT
    • 6.6.4. SUCCESS CASE WITH LIVEPERSON
  • 6.7. TELLIGENT
    • 6.7.1. SUCCESS CASE WITH DELL
  • 6.8. SOCIALTEXT
    • 6.8.1. SWOT ANALYSIS
  • 6.9. MZINGA
    • 6.9.1. MZINGA OMNISOCIAL
  • 6.10. COMMUNISPACE
    • 6.10.1. FULL-SERVICE SOLUTION
    • 6.10.2. BULLYING PREVENTION CAMPAIGN CASE
  • 6.11. LITHIUM
    • 6.11.1. LITHIUM SOCIAL WEB: THE SOCIAL CUSTOMER EXPERIENCE PLATFORM
    • 6.11.2. SOCIAL SOLUTIONS FOR MARKETING, COMMERCE AND SUPPORT
  • 6.12. SUCCESSFACTORS: AN SAP COMPANY
    • 6.12.1. SUCCESSFACTORS BIZX SUITE
    • 6.12.2. SUCCESS CASE WITH SIEMENS
  • 6.13. CITRIX
    • 6.13.1. GOTO CLOUD SERVICES
  • 6.14. ORACLE SOCIAL SERVICES
  • 6.15. SABA POOPLE CLOUD
  • 6.16. CISCO WEBEX SOCIAL
    • 6.16.1. SWOT ANALYSIS
  • 6.17. POKESHOT///SMZ
    • 6.17.1. PUBLIC SECTOR COMMUNITY
    • 6.17.2. POKESHOT///SMARTERPATH
    • 6.17.3. TRANSLATION MANAGER
    • 6.17.4. SOCIAL CONNECTOR FOR CISCO, SAMETIME AND LOTUS NOTES
    • 6.17.5. SECURITY EXTENSION

7.0. CASE STUDY ANALYSIS OF SUCCESS SCENARIOS

  • 7.1. LINKEDIN ONLINE RECRUITMENT BUSINESS
  • 7.2. FACEBOOK AND TWITTER CASE AS ENABLER
  • 7.3. RESTAURANT BUSINESS CASE ON PT BAMBU DESA
  • 7.4. SATEL OY AND FORD CASE
  • 7.5. CLIMATE ASIA PACIFIC CASE
  • 7.6. OXFAM CASE ON ADVOCACY THROUGH SOCIAL MEDIA
  • 7.7. ASSIST CASE ON SOCIAL CAPACITY BUILDING
  • 7.8. FERRING ITALY SPA IMPLEMENTATION CASE

8.0. CONCLUSIONS AND RECOMMENDATIONS

  • 8.1. RECOMMENDATION FOR VENDORS
  • 8.2. RECOMMENDATION FOR ORGANIZATIONS

Figures

  • Figure 1: Reasons of Cloud Computing Adoption to Social Businesses
  • Figure 2: Social Customer Conversion Ecosystem
  • Figure 3: Building Block Techniques for Social Business Capabilities
  • Figure 4: Social Collaboration Component and Business Activities Part 1
  • Figure 5: Social Collaboration Component and Business Activities Part 2
  • Figure 6: Social Collaboration Component and Business Activities Part 3
  • Figure 7: Parameters to Assess Social Business Capabilities
  • Figure 8: Sample List of Business Outcomes
  • Figure 9: Industry vs. Nuance
  • Figure 10: Major Obstacles of Cloud Based Social Business Services
  • Figure 11: Global Social Cloud Bus App Market by Enterprise Spend 2015-2020
  • Figure 12: Enterprise Social Business App at Least One 2015-2020
  • Figure 13: Vendor in Social Business Services in Cloud Ecosystem
  • Figure 14: Engagement Ratios by Internal, External, and Unified Social Network
  • Figure 15: Market Share of Major Social Networking Media
  • Figure 16: IBM's Investment for Business Partner Growth
  • Figure 17: SaleForce Solution Dimensions for Social Business
  • Figure 18: Salesforce Chatter Dynamic Interface
  • Figure 19: Upcoming Enhanced Experience with Yammer Social & Office 365
  • Figure 20: Jive Social Business Platform
  • Figure 21: Full-Service Solution Sketch
  • Figure 22: Interface of Bullying Prevention Campaign Site
  • Figure 23: Lithium Social Customer Experience Platform

Tables

  • Table 1: Global Social Cloud Business App Market by Segment 2015-2020
  • Table 2: Social Cloud Business App Market by Major Functions 2015-2020
  • Table 3: Social Cloud Bus App Market by Desktop, Mobile & Platform 2015-2020
  • Table 4: Social Cloud Business App Market SaaS vs. PaaS Model 2015-2020
  • Table 5: Social Cloud Business App Market Region 2015-2020
  • Table 6: Social Business App Major Vendor Revenue Projection 2015-2020
  • Table 7: Government Cost Reduction due to Social Cloud Business 2015-2018

Big Data Market: Business Case, Market Analysis & Forecasts 2015-2020

1. Introduction

  • 1.1. Executive Summary
  • 1.2. Topics Covered
  • 1.3. Key Findings
  • 1.4. Target Audience
  • 1.5. Companies Mentioned

2. Big Data Technology & Business Case

  • 2.1. Defining Big Data
  • 2.2. Key Characteristics of Big Data
    • 2.2.1. Volume
    • 2.2.2. Variety
    • 2.2.3. Velocity
    • 2.2.4. Variability
    • 2.2.5. Complexity
  • 2.3. Big Data Technology
    • 2.3.1. Hadoop
    • 2.3.2. Other Apache Projects
    • 2.3.3. NoSQL
      • 2.3.3.1. Hbase
      • 2.3.3.2. Cassandra
      • 2.3.3.3. Mongo DB
      • 2.3.3.4. Riak
      • 2.3.3.5. CouchDB
    • 2.3.4. MPP Databases
    • 2.3.5. Others and Emerging Technologies
      • 2.3.5.1. Storm
      • 2.3.5.2. Drill
      • 2.3.5.3. Dremel
      • 2.3.5.4. SAP HANA
      • 2.3.5.5. Gremlin & Giraph
    • 2.3.6. New Paradigms and Techniques
      • 2.3.6.1. Streaming Analytics
      • 2.3.6.2. Cloud Technology
      • 2.3.6.3. Google Search
      • 2.3.6.4. Customize Analytical Tools
      • 2.3.6.5. Internet Keywords
      • 2.3.6.6. Gamification
  • 2.4. Big Data Roadmap
  • 2.5. Market Drivers
    • 2.5.1. Data Volume & Variety
    • 2.5.2. Increasing Adoption of Big Data by Enterprises and Telecom
    • 2.5.3. Maturation of Big Data Software
    • 2.5.4. Continued Investments in Big Data by Web Giants
    • 2.5.5. Business Drivers
  • 2.6. Market Barriers
    • 2.6.1. Privacy and Security: The 'Big' Barrier
    • 2.6.2. Workforce Re-skilling and Organizational Resistance
    • 2.6.3. Lack of Clear Big Data Strategies
    • 2.6.4. Technical Challenges: Scalability & Maintenance
    • 2.6.5. Big Data Development Expertise

3. Key Investment Sectors for Big Data

  • 3.1. Industrial Internet and Machine-to-Machine
    • 3.1.1. Big Data in M2M
    • 3.1.2. Vertical Opportunities
  • 3.2. Retail and Hospitality
    • 3.2.1. Improving Accuracy of Forecasts & Stock Management
    • 3.2.2. Determining Buying Patterns
    • 3.2.3. Hospitality Use Cases
    • 3.2.4. Personalized Marketing
  • 3.3. Media
    • 3.3.1. Social Media
    • 3.3.2. Social Gaming Analytics
    • 3.3.3. Usage of Social Media Analytics by Other Verticals
    • 3.3.4. Internet Keyword Search
  • 3.4. Utilities
    • 3.4.1. Analysis of Operational Data
    • 3.4.2. Application Areas for the Future
  • 3.5. Financial Services
    • 3.5.1. Fraud Analysis, Mitigation & Risk Profiling
    • 3.5.2. Merchant-Funded Reward Programs
    • 3.5.3. Customer Segmentation
    • 3.5.4. Customer Retention & Personalized Product Offering
    • 3.5.5. Insurance Companies
  • 3.6. Healthcare and Pharmaceutical
    • 3.6.1. Drug Development
    • 3.6.2. Medical Data Analytics
    • 3.6.3. Case Study: Identifying Heartbeat Patterns
  • 3.7. Telecommunications
    • 3.7.1. Telco Analytics: Customer/Usage Profiling and Service Optimization
    • 3.7.2. Big Data Analytic Tools
    • 3.7.3. Speech Analytics
    • 3.7.4. New Products and Services
  • 3.8. Government and Homeland Security
    • 3.8.1. Big Data Research
    • 3.8.2. Statistical Analysis
    • 3.8.3. Language Translation
    • 3.8.4. Developing New Applications for the Public
    • 3.8.5. Tracking Crime
    • 3.8.6. Intelligence Gathering
    • 3.8.7. Fraud Detection & Revenue Generation
  • 3.9. Other Sectors
    • 3.9.1. Aviation
    • 3.9.2. Transportation & Logistics: Optimizing Fleet Usage
    • 3.9.3. Sports: Real-Time Processing of Statistics
    • 3.9.4. Education
    • 3.9.5. Manufacturing

4. The Big Data Value Chain

  • 4.1. How Fragmented is the Big Data Value Chain?
  • 4.2. Data Acquisitioning & Provisioning
  • 4.3. Data Warehousing & Business Intelligence
  • 4.4. Analytics & Virtualization
  • 4.5. Actioning and Business Process Management
  • 4.6. Data Governance

5. Big Data Analytics

  • 5.1. What is Big Data Analytics?
  • 5.2. The Importance of Big Data Analytics
  • 5.3. Reactive vs. Proactive Analytics
  • 5.4. Technology and Implementation Approaches
    • 5.4.1. Grid Computing
    • 5.4.2. In-Database processing
    • 5.4.3. In-Memory Analytics
    • 5.4.4. Data Mining
    • 5.4.5. Predictive Analytics
    • 5.4.6. Natural Language Processing
    • 5.4.7. Text Analytics
    • 5.4.8. Visual Analytics
    • 5.4.9. Association rule learning
    • 5.4.10. Classification tree analysis
    • 5.4.11. Machine Learning
      • 5.4.11.1. Neural networks
      • 5.4.11.2. Multilayer Perceptron (MLP)
      • 5.4.11.3. Radial Basis Functions
      • 5.4.11.4. Support vector machines
      • 5.4.11.5. Naíve Bayes
      • 5.4.11.6. k-nearest neighbors
      • 5.4.11.7. Geospatial predictive modelling
    • 5.4.12. Regression Analysis
    • 5.4.13. Social Network Analysis

6. Standardization and Regulatory Initiatives

  • 6.1. Cloud Standards Customer Council-Big Data Working Group
  • 6.2. National Institute of Standards and Technology-Big Data Working Group
  • 6.3. OASIS
  • 6.4. Open Data Foundation
  • 6.5. Open Data Center Alliance
  • 6.6. Cloud Security Alliance-Big Data Working Group
  • 6.7. International Telecommunications Union
  • 6.8. International Organization for Standardization
  • 6.9. International Organization for Standardization)

7. Key Players in the Big Data Market

  • 7.1. Vendor Assessment Matrix
  • 7.2. 1010Data
  • 7.3. Actuate Corporation
  • 7.4. Accenture
  • 7.5. Amazon
  • 7.6. Apache Software Foundation
  • 7.7. APTEAN (Formerly CDC Software)
  • 7.8. Booz Allen Hamilton
  • 7.9. Cap Gemini
  • 7.10. Cisco Systems
  • 7.11. Cloudera
  • 7.12. Computer Science Corporation
  • 7.13. DataDirect Network
  • 7.14. Dell
  • 7.15. Deloitte
  • 7.16. EMC
  • 7.17. Facebook
  • 7.18. Fujitsu
  • 7.19. General Electric
  • 7.20. GoodData Corporation
  • 7.21. Google
  • 7.22. Guavus
  • 7.23. Hitachi Data Systems
  • 7.24. Hortonworks
  • 7.25. HP
  • 7.26. IBM
  • 7.27. Informatica
  • 7.28. Intel
  • 7.29. Jaspersoft
  • 7.30. Juniper Networks
  • 7.31. Marklogic
  • 7.32. Microsoft
  • 7.33. MongoDB (Formerly 10Gen)
  • 7.34. MU Sigma
  • 7.35. Netapp
  • 7.36. NTT Data
  • 7.37. Opera Solutions
  • 7.38. Oracle
  • 7.39. Pentaho
  • 7.40. Platfora
  • 7.41. Qliktech
  • 7.42. Quantum
  • 7.43. Rackspace
  • 7.44. Revolution Analytics
  • 7.45. Salesforce
  • 7.46. SAP
  • 7.47. SAS Institute
  • 7.48. Sisense
  • 7.49. Software AG/Terracotta
  • 7.50. Splunk
  • 7.51. Sqrrl
  • 7.52. Supermicro
  • 7.53. Tableau Software
  • 7.54. Tata Consultancy Services
  • 7.55. Teradata
  • 7.56. Think Big Analytics
  • 7.57. TIBCO
  • 7.58. Tidemark Systems
  • 7.59. VMware (Part of EMC)
  • 7.60. Wipro
  • 7.61. Zettics

8. Market Analysis

  • 8.1. Big Data Revenue 2014-2020
  • 8.2. Big Data Revenue by Functional Area 2014-2020
    • 8.2.1. Supply Chain Management
    • 8.2.2. Business Intelligence
    • 8.2.3. Application Infrastructure & Middleware
    • 8.2.4. Data Integration Tools & Data Quality Tools
    • 8.2.5. Database Management Systems
    • 8.2.6. Big Data Social & Content Analytics
    • 8.2.7. Big Data Storage Management
    • 8.2.8. Big Data Professional Services
  • 8.3. Big Data Revenue by Region 2014-2020
    • 8.3.1. Asia Pacific
    • 8.3.2. Eastern Europe
    • 8.3.3. Latin & Central America
    • 8.3.4. Middle East & Africa
    • 8.3.5. North America
    • 8.3.6. Western Europe

Figures

  • Figure 1: NoSQL vs Legacy DB Performance Comparisons
  • Figure 2: 2014 Gartner Hype Cycle for Emerging Technologies
  • Figure 3: Roadmap Big Data Technologies 2014-2030
  • Figure 4: The Big Data Value Chain
  • Figure 5: Big Data Vendor Ranking Matrix
  • Figure 6: Big Data Revenue 2013-2020
  • Figure 7: Big Data Revenue by Functional Area 2013-2020
  • Figure 8: Big Data Supply Chain Management Revenue 2013-2020
  • Figure 9: Big Data Supply Business Intelligence Revenue 2013-2020
  • Figure 10: Big Data Application Infrastructure & Middleware Revenue 2013-2020
  • Figure 11: Big Data Integration and Quality Tools Revenue 2013-2020
  • Figure 12: Big Data DB Management Systems Revenue 2013-2020
  • Figure 13: Big Data Social & Content Analytics Revenue 2013-2020
  • Figure 14: Big Data Storage Management Revenue 2013-2020
  • Figure 15: Big Data Professional Services Revenue 2013-2020
  • Figure 16: Big Data Revenue by Region 2013-2020
  • Figure 17: Asia Pacific Big Data Revenue 2013-2020
  • Figure 18: Eastern Europe Big Data Revenue 2013-2020
  • Figure 19: Latin & Central America Big Data Revenue 2013-2020
  • Figure 20: Middle East & Africa Big Data Revenue 2013-2020
  • Figure 21: North America Big Data Revenue 2013-2020
  • Figure 22: Western Europe Big Data Revenue 2013-2020 140

Mobile Application Marketplace 2015: Market Analysis and Assessment of Future Evolution and Opportunities

1. Introduction

  • 1.1. Executive Summary
  • 1.2. Target Audience
  • 1.3. Companies Mentioned

2. Mobile Applications Overview

  • 2.1. Definition of a Mobile Applications
  • 2.2. What Separates an App From a Bundled Device Feature?
  • 2.3. Examples of Current Mobile Apps

3. Mobile Platforms (Operating Systems)

  • 3.1. OHA Android (free and open source)
  • 3.2. iOS from Apple
  • 3.3. BlackBerry 10 from RIM
  • 3.4. Windows Mobile from Microsoft
  • 3.5. BlackBerry OS from RIM
  • 3.6. BREW from Qualcomm
  • 3.7. Symbian OS from Nokia and Accenture
  • 3.8. Firefox OS from Mozilla Foundation
  • 3.9. Sailfish OS from Jolla
  • 3.10. TIZEN from the Linuz Foundation
  • 3.11. Ubuntu from Canonical Ltd.

4. Mobile Programming

  • 4.1. Widgets
  • 4.2. Hardware Widgets
  • 4.3. Hardware and Software Evolution
    • 4.3.1. Hardware Evolution and Handset Manufacturers Market Share
    • 4.3.2. The Smartphone Revolution
    • 4.3.3. Development Platforms
    • 4.3.4. HTML5
    • 4.3.5. HTML and Mini Browsers
    • 4.3.6. Adobe, Flash, and SilverLight
    • 4.3.7. JavaScript
    • 4.3.8. AJAX
    • 4.3.9. Future Directions of Mobile Development

5. Application Development Platforms

  • 5.1. J2ME Platform
  • 5.2. Platform Specific
    • 5.2.1. iOS SDK
    • 5.2.2. Blackberry OS Development Tools
    • 5.2.3. Nokia Development Tools
    • 5.2.4. Motorola Development Tools
    • 5.2.5. LG Development Tools
    • 5.2.6. Samsung Development Tools
    • 5.2.7. HTC Development Tools
    • 5.2.8. Sony Ericsson Development Tools
    • 5.2.9. Android Development Tools

6. Key Development Concepts

  • 6.1. Mobile Development Trends
    • 6.1.1. Platforms
    • 6.1.2. Programming Techniques
    • 6.1.3. Mobile Optimization
    • 6.1.4. Software Development Methodology
  • 6.2. Native Programming Techniques
    • 6.2.1. Size Constraints
      • 6.2.1.1. Compact Code
      • 6.2.1.2. Compact File Space
    • 6.2.2. Display Constraints
      • 6.2.2.1. Display Sizes and Standards
      • 6.2.2.2. Multiple Displays
    • 6.2.3. Input and Controls
      • 6.2.3.1. Input device types
      • 6.2.3.2. Keyboard
      • 6.2.3.3. Touch Screen
      • 6.2.3.4. Thumb Sticks, Roller Balls, and Direction Pads.
      • 6.2.3.5. Environmental Controls
      • 6.2.3.6. Motion and Orientation Sensors
      • 6.2.3.7. Light Sensors
      • 6.2.3.8. Proximity Sensor
      • 6.2.3.9. Gyroscope
      • 6.2.3.10. Accelerometer
      • 6.2.3.11. Peripheral Access
      • 6.2.3.12. GPS Onboard and Off
      • 6.2.3.13. Bluetooth
      • 6.2.3.14. Near Field Communication and S Beam
      • 6.2.3.15. Touch ID
      • 6.2.3.16. Stylus Pen
  • 6.3. Network Access
    • 6.3.1. Connection Persistence
    • 6.3.2. Dial on Demand
    • 6.3.3. Always On
    • 6.3.4. Connection Types and Limitations
    • 6.3.5. Cellular Data
    • 6.3.6. WiFi
    • 6.3.7. Bluetooth
    • 6.3.8. Bluetooth Low Energy (BLE)
    • 6.3.9. Processing
    • 6.3.10. Platforms and Speeds

7. Mobile Application Market

  • 7.1. Mobile Advertising
  • 7.2. Market Summary

8. Application Store Case Studies

  • 8.1. Case Study Blackberry (RIM)
  • 8.2. Case Study Apple
  • 8.3. Case Study Android
  • 8.4. Case Study: Amazon App Store
  • 8.5. Case Study Windows App Store

9. Market Size

  • 9.1. Mobile Application Overall Market
  • 9.2. Mobile Sales Potential
  • 9.3. Forecasted Smart Phone Sales
  • 9.4. Growth Indicators
  • 9.5. Market Analysis
  • 9.6. Application Store Market Performance
  • 9.6.1. Apple App Store
  • 9.6.2. Android Marketplace Analysis

10. Mobile Gaming Analytics

  • 10.1.1. Monetizing Micro Transaction in F2P Model: Creating a Need Approach is Key
  • 10.1.2. Game Balancing Method in Micro Transaction Model
  • 10.1.3. Potential Risk and Solution in F2P Virtual Economy
  • 10.1.4. Pricing Decision Factors: ARPU vs. Average game price vs. Average Gamers
  • 10.1.5. Product Life Cycle of Mobile Game: Adoption of Moore's Lifecycle Model
  • 10.1.6. Game Lifecycle KPI framework
  • 10.1.7. Smartphones vs. Portable Game Players

11. Wearable Devices Apps and Future Apps

  • 11.1. Fitness Apps
  • 11.2. Wearable Devices Payment Apps
  • 11.3. Future Wearables Apps
    • 11.3.1. Military Applications
    • 11.3.2. Industry and Enterprise Applications
    • 11.3.3. A Day in the Life of a Celebrity App
    • 11.3.4. In my Glass

12. Carrier and Vendor Adaptations

  • 12.1. Topology and Network Changes
    • 12.1.1. Policy Changes
    • 12.1.2. Open Network Movements
    • 12.1.3. Billing Plan Changes
    • 12.1.4. Infrastructure Hardware Changes
    • 12.1.5. Location Based Services
    • 12.1.6. WiFi Localized Service Hosting
    • 12.1.7. Handset Manufacturer Changes
    • 12.1.8. Integrating New Handset Features
    • 12.1.9. Evolving the Handset
    • 12.1.10. Multiple Platform Mobile Operating Systems

13. App Publishers Analysis

  • 13.1. Gameloft
  • 13.2. GungHo Online
  • 13.3. Electronic Arts
  • 13.4. Zynga
  • 13.5. DeNA
  • 13.6. SEGA
  • 13.7. King

14. Future of Mobile Applications

  • 14.1. Communication Enabled Apps
    • 14.1.1. Direct API Revenue
    • 14.1.2. Data Monetization
    • 14.1.3. Cost Savings
    • 14.1.4. Higher Usage
    • 14.1.5. Churn Reduction
  • 14.2. Embedded Entertainment and Gamified Apps
    • 14.2.1. Gamification
    • 14.2.2. Wearable Gamification
    • 14.2.3. Mobile Social Gamification
    • 14.2.4. Cloud Gamification
  • 14.3. Cross Platform Apps
    • 14.3.1. Smartphones, Tablets, Wearable Tech and More
    • 14.3.2. Mobile/Wireless Apps Everywhere
  • 14.4. The Impact of SMAC
    • 14.4.1. Social, Mobile, Analytics, and Cloud (SMAC)
    • 14.4.2. SMAC Stack
    • 14.4.3. SMAC and Enterprise Mobile Market and Apps

Tables

  • Table 1: Example of the Most Successful Apps
  • Table 2: Apps Revenues in Apple App and Google Play Stores
  • Table 3: Handsets Manufacturer Market Share
  • Table 4: Mobile/Tablet Browser Share
  • Table 5: Mobile Platform Market Share 2012-2020
  • Table 6: Smartphone Market SWOT
  • Table 7: Key Considerable Mobile Gaming Strategies
  • Table 8: Mobile Gaming Business Model Descriptions
  • Table 9: Game Balancing Methods in Virtual Economy
  • Table 10: Potential Risk & Solution in F2P Virtual Economy
  • Table 11: Revenue vs. Costs in Gaming App Business
  • Table 12: Gameloft most Successful Apps
  • Table 13: Gungho Online Entertainment, Inc most Successful Apps
  • Table 14: EA most Successful Apps
  • Table 15: Zynga most Successful Apps
  • Table 16: DeNa Most Successful Apps
  • Table 17: SEGA Most Successful App
  • Table 18: King Applications

Figures

  • Figure 1: iPhone 6 and iPhone 6 Plus
  • Figure 2: iOS 8
  • Figure 3: Windows Phone 8 from Nokia
  • Figure 4: BlackBerry Z10
  • Figure 5: First Mobile Widgets
  • Figure 6: Early Mobile Widgets and Hardware
  • Figure 7: The Rise of the Smartphones Era
  • Figure 8: Classic Web App vs. Ajax Web Application Model
  • Figure 9: Samsung Note Edge
  • Figure 10: Multi-touch Screen
  • Figure 11: Touch ID
  • Figure 12: Blackberry OS 10.1
  • Figure 13: Amazon App Store
  • Figure 14: Apple App Store vs. iTunes Music Sales
  • Figure 15: Mobile Gaming Business Models
  • Figure 16: Monetizing Micro-Transaction in F2P model
  • Figure 17: Adoption of Moore's Lifecycle Model in Mobile Gaming
  • Figure 18: Sequential Steps of Mobile Game Analytic Approach
  • Figure 19: Mobile Game Lifecycle KPI Framework
  • Figure 20: Apple Watch Payments using NFC
  • Figure 21: A Day in a Life of a Celebrity
  • Figure 22: Mobile App Store Framework

Data as a Service (DaaS) Market and Forecasts 2015-2020

1. Introduction

  • 1.1. Executive Summary
  • 1.2. Topics Covered
  • 1.3. Key Findings
  • 1.4. Target Audience

2. DaaS Technologies

  • 2.1. Cloud
  • 2.2. Database Approaches and Solutions
    • 2.2.1. Relational Database Management System (RDBS)
    • 2.2.2. NoSQL
    • 2.2.3. Hadoop
    • 2.2.4. High Performance Computing Cluster (HPCC)
    • 2.2.5. OpenStack
  • 2.3. DaaS and the XaaS Ecosystem
  • 2.4. Open Data Center Alliance
  • 2.5. Market Sizing by Horizontal

3. DaaS Market

  • 3.1. Market Overview
    • 3.1.1. Data-as-a-Service: A movement
    • 3.1.2. Data Structure
    • 3.1.3. Specialization
    • 3.1.4. Vendors
  • 3.2. Vendor Analysis and Prospects
    • 3.2.1. Large Vendors: BDaaS
    • 3.2.2. Mid-sized Vendors
    • 3.2.3. Small Vendors: DaaS and SaaS
    • 3.2.4. Market Size: BDaaS vs. RDBMS
  • 3.3. Market Drivers and Constraints
    • 3.3.1. Drivers
      • 3.3.1.1. Business Intelligence and DaaS Integration
      • 3.3.1.2. The Cloud Enabler DaaS
      • 3.3.1.3. XaaS Drives DaaS
    • 3.3.2. Constraints
      • 3.3.2.1. Issues Relating to Data-as-a-Service Integration
  • 3.4. Barriers and Challenges to DaaS Adoption
    • 3.4.1. Enterprises Reluctance to Change
    • 3.4.2. Responsibility of Data Security Externalized
    • 3.4.3. Security Concerns are Real
    • 3.4.4. Cyber Attacks
    • 3.4.5. Unclear Agreements
    • 3.4.6. Complexity is a Deterrent
    • 3.4.7. Lack of Cloud Interoperability
    • 3.4.8. Service Provider Resistance to Audits
    • 3.4.9. Viability of Third-party Providers
    • 3.4.10. No Move of Systems and Data is without Cost
    • 3.4.11. Lack of Integration Features in the Public Cloud results in Reduced Functionality
  • 3.5. Market Share and Geographic Influence
  • 3.6. Vendors
    • 3.6.1. 1010data
    • 3.6.2. Amazon
    • 3.6.3. Clickfox
    • 3.6.4. Datameer
    • 3.6.5. Google
    • 3.6.6. Hewlett-Packard
    • 3.6.7. IBM
    • 3.6.8. Infosys
    • 3.6.9. Microsoft
    • 3.6.10. Oracle
    • 3.6.11. Rackspace
    • 3.6.12. Salesforce
    • 3.6.13. Splunk
    • 3.6.14. Teradata
    • 3.6.15. Tresata

4. DaaS Strategies

  • 4.1. General Strategies
    • 4.1.1. Tiered Data Focus
    • 4.1.2. Value-based Pricing
    • 4.1.3. Open Development Environment
  • 4.2. Specific Strategies
    • 4.2.1. Service Ecosystem and Platforms
    • 4.2.2. Bringing to Together Multiple Sources for Mash-ups
    • 4.2.3. Developing Value-added Services (VAS) as Proof Points
    • 4.2.4. Open Access to all Entities including Competitors
    • 4.2.5. Prepare for Big Opportunities with the Internet of Things (IoT)
  • 4.3. Service Provider Strategies
    • 4.3.1. Telecom Network Operators
    • 4.3.2. Data Center Providers
    • 4.3.3. Managed Service Providers
  • 4.4. Infrastructure Provider Strategies
    • 4.4.1. Enable New Business Models
  • 4.5. Application Developer Strategies

5. DaaS based Applications

  • 5.1. Business Intelligence
  • 5.2. Development Environments
  • 5.3. Verification and Authorization
  • 5.4. Reporting and Analytics
  • 5.5. DaaS in Healthcare
  • 5.6. DaaS and Wearable technology
  • 5.7. DaaS in the Government Sector
  • 5.8. DaaS for Media and Entertainment
  • 5.9. DaaS for Telecoms
  • 5.10. DaaS for Insurance
  • 5.11. DaaS for Utilities and Energy Sector
  • 5.12. DaaS for Pharmaceuticals
  • 5.13. DaaS for Financial Services

6. Market Outlook and Future of DaaS

  • 6.1. Recent Security Concerns
  • 6.2. Cloud Trends
    • 6.2.1. Hybrid Computing
    • 6.2.2. Multi-Cloud
    • 6.2.3. Cloud Bursting
  • 6.3. General Data Trends
  • 6.4. Enterprise Leverages own Data and Telecom
    • 6.4.1. Web APIs
    • 6.4.2. SOA and Enterprise APIs
    • 6.4.3. Cloud APIs
    • 6.4.4. Telecom APIs
  • 6.5. Data Federation Emerges for DaaS

7. Conclusions

8. Appendix

  • 8.1. Structured vs. Unstructured Data
    • 8.1.1. Structured Database Services in Telecom
    • 8.1.2. Unstructured Database Services in Telecom and Enterprise
    • 8.1.3. Emerging Hybrid (Structured/Unstructured) Database Services
  • 8.2. Data Architecture and Functionality
    • 8.2.1. Data Architecture
      • 8.2.1.1. Data Models and Modelling
      • 8.2.1.2. DaaS Architecture
    • 8.2.2. Data Mart vs. Data Warehouse
    • 8.2.3. Data Gateway
    • 8.2.4. Data Mediation
  • 8.3. Master Data Management (MDM)
    • 8.3.1. Understanding MDM
      • 8.3.1.1. Transactional vs. Non-transactional Data
      • 8.3.1.2. Reference vs. Analytics Data
    • 8.3.2. MDM and DaaS
      • 8.3.2.1. Data Acquisition and Provisioning
      • 8.3.2.2. Data Warehousing and Business Intelligence
      • 8.3.2.3. Analytics and Virtualization
      • 8.3.2.4. Data Governance
  • 8.4. Data Mining
    • 8.4.1. Data Capture
      • 8.4.1.1. Event Detection
      • 8.4.1.2. Capture Methods
    • 8.4.2. Data Mining Tools

Figures

  • Figure 2: Cloud Computing Service Model Stack and Principle Consumers
  • Figure 3: DaaS across Horizontal and Vertical Segments
  • Figure 8: Different Data Types and Functions in DaaS
  • Figure 9: Ecosystem and Platform Model
  • Figure 10: Ecosystem and Platform Model
  • Figure 11: DaaS and IoT Mediation for Smartgrid
  • Figure 12: Internet of Things (IoT) and DaaS
  • Figure 13: Telecom API Value Chain for DaaS
  • Figure 14: DaaS, Verification and Authorization
  • Figure 15: Web APIs
  • Figure 16: Services Oriented Architecture
  • Figure 17: Cloud Services, DaaS, and APIs
  • Figure 18: Telecom APIs
  • Figure 19: Federated Data vs. Non-Federated Models
  • Figure 20: Federated Data at Functional Level
  • Figure 21: Federated Data at City Level
  • Figure 22: Federated Data at Global Level
  • Figure 23: Federation Requires Mediation Data
  • Figure 24: Mediation Data Synchronization
  • Figure 25: Hybrid Data in Next Generation Applications
  • Figure 26: Traditional Data Architecture
  • Figure 27: Data Architecture Modeling
  • Figure 28: DaaS Data Architecture
  • Figure 29: Location Data Mediation
  • Figure 30: Data Mediation in IoT
  • Figure 31: Data Mediation for Smartgrids
  • Figure 32: Enterprise Data Types
  • Figure 33: Data Governance
  • Figure 34: Data Flow
  • Figure 35: Processing Streaming Data
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