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

  • ID: 3308659
  • Report
  • Region: Global
  • 169 Pages
  • Mind Commerce
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DaaS Market Expected to Continue to Expand Alongside Cloud Services Model Over Next Decade


  • 1010data
  • Clickfox
  • Google
  • IBM
  • Microsoft
  • Rackspace
  • MORE
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 this report 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
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  • 1010data
  • Clickfox
  • Google
  • IBM
  • Microsoft
  • Rackspace
  • MORE
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 Business Intelligence and DaaS Integration The Cloud Enabler DaaS XaaS Drives DaaS
3.3.2 Constraints 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 Platform
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 Data Models and Modelling 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 Transactional vs. Non-transactional Data Reference vs. Analytics Data
8.3.2 MDM and DaaS Data Acquisition and Provisioning Data Warehousing and Business Intelligence Analytics and Virtualization Data Governance
8.4 Data Mining
8.4.1 Data Capture Event Detection Capture Methods
8.4.2 Data Mining Tools


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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4 of 4
- 1010data
- Amazon
- Clickfox
- Datameer
- Google
- Hewlett-Packard
- Infosys
- Microsoft
- Oracle
- Rackspace
- Salesforce
- Splunk
- Teradata
- Tresata
Note: Product cover images may vary from those shown
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Note: Product cover images may vary from those shown