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市場調查報告書

資料即服務(DaaS)市場及預測

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

出版商 Mind Commerce 商品編碼 317506
出版日期 內容資訊 英文 169 Pages
商品交期: 最快1-2個工作天內
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資料即服務(DaaS)市場及預測 Data as a Service (DaaS) Market and Forecasts 2015 - 2020
出版日期: 2015年07月13日 內容資訊: 英文 169 Pages
簡介

資料即服務(DaaS)定義為供應商提供用戶存取資料庫,或存取供應商所管理的系統上自己的資料的所有服務。DaaS針對雲端基礎基礎設施/服務、企業聯合資訊伺服器、XaaS(Everything as a Service)的消費者服務趨勢等,不久的未來預測將有大幅成長。供應商管理的系統更提供了可持續的服務執行所需的可擴充性及安全性。

本報告涵蓋資料即服務(DaaS)市場,調查分析DaaS生態系統的技術,企業及解決方案,彙整評估市場機會,到2019年為止的市場展望及預測,主要的供應商分析等資料,為您概述為以下內容。

第1章 簡介

  • 摘要整理
  • 涵蓋的題目
  • 主要調查結果

第2章 DaaS技術

  • 雲端
  • 資料庫方法及解決方案
    • 關聯式資料庫管理系統(RDBS)
    • NoSQL
    • Hadoop
    • 高性能運算叢集(HPCC)
    • OpenStack
  • DaaS及XaaS生態系統
  • Open Data Center Alliance
  • 各行業市場規模

第3章 DaaS市場

  • 市場概要
    • 數據結構
    • 專門化
    • 供應商
  • 供應商分析與展望
    • 大規模供應商:BDaaS
    • 中規模供應商
    • 小規模供應商:DaaS和SaaS
    • 市場規模:BDaaS VS RDBMS
  • 市場發展推動因素及阻礙因素
    • 市場發展推動因素
    • 市場發展阻礙因素
  • 市場佔有率與地區的影響
  • 供應商
    • 1010data
    • Amazon
    • Clickfox
    • Datameer
    • Google
    • Hewlett-Packard
    • IBM
    • Infosys
    • Microsoft
    • Oracle
    • Rackspace
    • Salesforce
    • Splunk
    • Teradata
    • Tresata

第4章 DaaS策略

  • 一般策略
    • 階段的資料重視
    • 價值為基礎的價格設定
    • 開放式開發環境
  • 具體的策略
    • 服務生態系統及平台
    • 收集Mashup用的複數資源
    • 開發作證要件的附加價值服務(VAS)
    • 包含競爭公司的所有企業用開放權限
    • 物聯網(IoT)擁有莫大機會
  • 服務供應商的策略
    • 通訊網路業者
    • 資料中心供應商
    • 管理服務供應商
  • 基礎設施供應商的策略
    • 新經營模式的實現
  • 應用開發業者的策略

第5章 DaaS為基礎的應用

  • 商務資訊
  • 開發環境
  • 檢驗與核准
  • 匯報和分析
  • 開發環境

第6章 市場展望和DaaS的未來

  • 最近的安全性相關疑慮
  • 雲端趨勢
    • 混合運算
    • 多雲端
    • 雲端擴張
  • 一般的資料趨勢
  • 企業活用自家公司資料及通訊
    • 網站API
    • SOA及企業API
    • 雲端API
    • 通訊API
  • DaaS資料聯合的出現

第7章 總論

附錄

  • 結構化資料VS非結構化資料
  • 資料架構與機能性
  • 主資料管理(MDM)
  • 資料探勘

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目錄

Data as a Service (DaaS) is defined as any service offered wherein users can access vendor provided databases or host their own databases on vendor managed systems. DaaS is expected to grow significantly in the near future due to a few dominant themes including cloud-based infrastructure/services, enterprise data syndication, and the consumer services trend towards Everything as a Service (XaaS). In addition, vendor managed systems provide necessary scalability and security for sustainable services execution.

The DaaS market is expected to continue to expand alongside the cloud services model over the next decade. This research evaluates the DaaS ecosystem including technologies, companies, and solutions. The report assesses market opportunities and provides a market outlook and forecast from 2015 to 2020.

The report also includes a vendor analysis segmented by three categories (1) The largest companies providing DaaS at an infrastructural level and handling big data, (2) Mid-sized companies that tend to operate in other areas such as business intelligence, CRM, etc.) and (3) Smaller companies that offer DaaS as an integrated service with SaaS for focused analytical perspectives on specific markets. 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.

Target Audience:

  • Telecom companies
  • Data services companies
  • Cloud services companies
  • Data infrastructure providers
  • Network and application integrators
  • Intermediaries and mediation companies

Report Benefits:

  • Forecast for DaaS through 2020
  • Understand the DaaS ecosystem
  • Identify key players and strategies
  • Understand DaaS technologies and tools
  • Recognize the importance of data mediation
  • Understand data management best practices
  • Understand the importance of managed systems
  • Identify the relationship between DaaS and cloud

Table of Contents

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 19
  • 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 4
    • 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 66
    • 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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