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




Published: July 2015   Pages: 169
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Overview:

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.

Data as a Service (DaaS) Market and Forecasts 2015 - 2020 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 8
1.1 Executive Summary 8
1.2 Topics Covered 10
1.3 Key Findings 11
1.4 Target Audience 12
2 DaaS Technologies 13
2.1 Cloud 13
2.2 Database Approaches and Solutions 14
2.2.1 Relational Database Management System (RDBS) 14
2.2.2 NoSQL 15
2.2.3 Hadoop 16
2.2.4 High Performance Computing Cluster (HPCC) 18
2.2.5 OpenStack 19
2.3 DaaS and the XaaS Ecosystem 19
2.4 Open Data Center Alliance 22
2.5 Market Sizing by Horizontal 23
3 DaaS Market 25
3.1 Market Overview 25
3.1.1 Data-as-a-Service: A movement 27
3.1.2 Data Structure 27
3.1.3 Specialization 28
3.1.4 Vendors 30
3.2 Vendor Analysis and Prospects 31
3.2.1 Large Vendors: BDaaS 31
3.2.2 Mid-sized Vendors 35
3.2.3 Small Vendors: DaaS and SaaS 37
3.2.4 Market Size: BDaaS vs. RDBMS 38
3.3 Market Drivers and Constraints 39
3.3.1 Drivers 39
3.3.1.1 Business Intelligence and DaaS Integration 42
3.3.1.2 The Cloud Enabler DaaS 44
3.3.1.3 XaaS Drives DaaS 44
3.3.2 Constraints 44
3.3.2.1 Issues Relating to Data-as-a-Service Integration 47
3.4 Barriers and Challenges to DaaS Adoption 48
3.4.1 Enterprises Reluctance to Change 48
3.4.2 Responsibility of Data Security Externalized 49
3.4.3 Security Concerns are Real 49
3.4.4 Cyber Attacks 50
3.4.5 Unclear Agreements 51
3.4.6 Complexity is a Deterrent 53
3.4.7 Lack of Cloud Interoperability 54
3.4.8 Service Provider Resistance to Audits 55
3.4.9 Viability of Third-party Providers 56
3.4.10 No Move of Systems and Data is without Cost 57
3.4.11 Lack of Integration Features in the Public Cloud results in Reduced Functionality 58
3.5 Market Share and Geographic Influence 58
3.6 Vendors 61
3.6.1 1010data 62
3.6.2 Amazon 62
3.6.3 Clickfox 65
3.6.4 Datameer 66
3.6.5 Google 66
3.6.6 Hewlett-Packard 68
3.6.7 IBM 69
3.6.8 Infosys 70
3.6.9 Microsoft 71
3.6.10 Oracle 71
3.6.11 Rackspace 72
3.6.12 Salesforce 73
3.6.13 Splunk 74
3.6.14 Teradata 74
3.6.15 Tresata 76
4 DaaS Strategies 77
4.1 General Strategies 77
4.1.1 Tiered Data Focus 77
4.1.2 Value-based Pricing 79
4.1.3 Open Development Environment 80
4.2 Specific Strategies 81
4.2.1 Service Ecosystem and Platforms 81
4.2.2 Bringing to Together Multiple Sources for Mash-ups 82
4.2.3 Developing Value-added Services (VAS) as Proof Points 83
4.2.4 Open Access to all Entities including Competitors 83
4.2.5 Prepare for Big Opportunities with the Internet of Things (IoT) 84
4.3 Service Provider Strategies 88
4.3.1 Telecom Network Operators 88
4.3.2 Data Center Providers 96
4.3.3 Managed Service Providers 97
4.4 Infrastructure Provider Strategies 98
4.4.1 Enable New Business Models 98
4.5 Application Developer Strategies 99
5 DaaS based Applications 100
5.1 Business Intelligence 100
5.2 Development Environments 103
5.3 Verification and Authorization 104
5.4 Reporting and Analytics 105
5.5 DaaS in Healthcare 106
5.6 DaaS and Wearable technology 107
5.7 DaaS in the Government Sector 107
5.8 DaaS for Media and Entertainment 108
5.9 DaaS for Telecoms 109
5.10 DaaS for Insurance 110
5.11 DaaS for Utilities and Energy Sector 110
5.12 DaaS for Pharmaceuticals 111
5.13 DaaS for Financial Services 111
6 Market Outlook and Future of DaaS 113
6.1 Recent Security Concerns 113
6.2 Cloud Trends 116
6.2.1 Hybrid Computing 117
6.2.2 Multi-Cloud 118
6.2.3 Cloud Bursting 119
6.3 General Data Trends 121
6.4 Enterprise Leverages own Data and Telecom 123
6.4.1 Web APIs 123
6.4.2 SOA and Enterprise APIs 125
6.4.3 Cloud APIs 127
6.4.4 Telecom APIs 128
6.5 Data Federation Emerges for DaaS 130
7 Conclusions 138
8 Appendix 141
8.1 Structured vs. Unstructured Data 141
8.1.1 Structured Database Services in Telecom 141
8.1.2 Unstructured Database Services in Telecom and Enterprise 143
8.1.3 Emerging Hybrid (Structured/Unstructured) Database Services 143
8.2 Data Architecture and Functionality 146
8.2.1 Data Architecture 146
8.2.1.1 Data Models and Modelling 147
8.2.1.2 DaaS Architecture 148
8.2.2 Data Mart vs. Data Warehouse 150
8.2.3 Data Gateway 151
8.2.4 Data Mediation 151
8.3 Master Data Management (MDM) 155
8.3.1 Understanding MDM 156
8.3.1.1 Transactional vs. Non-transactional Data 157
8.3.1.2 Reference vs. Analytics Data 157
8.3.2 MDM and DaaS 157
8.3.2.1 Data Acquisition and Provisioning 158
8.3.2.2 Data Warehousing and Business Intelligence 159
8.3.2.3 Analytics and Virtualization 160
8.3.2.4 Data Governance 160
8.4 Data Mining 161
8.4.1 Data Capture 163
8.4.1.1 Event Detection 165
8.4.1.2 Capture Methods 165
8.4.2 Data Mining Tools 168

Figures

Figure 1: Cloud Computing Service Model Stack and Principle Consumers
Figure 2: DaaS across Horizontal and Vertical Segments
Figure 3: Different Data Types and Functions in DaaS
Figure 4: Ecosystem and Platform Model
Figure 5: Ecosystem and Platform Model
Figure 6: DaaS and IoT Mediation for Smartgrid
Figure 7: Internet of Things (IoT) and DaaS
Figure 8: Telecom API Value Chain for DaaS
Figure 9: DaaS, Verification and Authorization
Figure 10: Web APIs
Figure 11: Services Oriented Architecture
Figure 12: Cloud Services, DaaS, and APIs
Figure 13: Telecom APIs
Figure 14: Federated Data vs. Non-Federated Models
Figure 15: Federated Data at Functional Level
Figure 16: Federated Data at City Level
Figure 17: Federated Data at Global Level
Figure 18: Federation Requires Mediation Data
Figure 19: Mediation Data Synchronization
Figure 20: Hybrid Data in Next Generation Applications
Figure 21: Traditional Data Architecture
Figure 22: Data Architecture Modeling
Figure 23: DaaS Data Architecture
Figure 24: Location Data Mediation
Figure 25: Data Mediation in IoT
Figure 26: Data Mediation for Smartgrids
Figure 27: Enterprise Data Types
Figure 28: Data Governance
Figure 29: Data Flow
Figure 30: Processing Streaming Data


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