Data as a Service (DaaS) Market and Forecasts 2014 - 2019

Data as a Service (DaaS) Market and Forecasts 2014 - 2019

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 through 2019. 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.

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Target Audience:

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

Key Findings:

Forecast for DaaS through 2019
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

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.

1 Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
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 Structure
3.1.2 Specialization
3.1.3 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
3.3.2 Constraints
3.4 Market Share and Geographic Influence
3.5 Vendors
3.5.1 1010data
3.5.2 Amazon
3.5.3 Clickfox
3.5.4 Datameer
3.5.5 Google
3.5.6 Hewlett-Packard
3.5.7 IBM
3.5.8 Infosys
3.5.9 Microsoft
3.5.10 Oracle
3.5.11 Rackspace
3.5.12 Salesforce
3.5.13 Splunk
3.5.14 Teradata
3.5.15 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 Development Environments
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 1: Total DaaS Revenue, Through 2019
Figure 2: Cloud Computing Service Model Stack and Principle Consumers
Figure 3: DaaS across Horizontal and Vertical Segments
Figure 4: Revenue by XaaS Horizontal 2014 - 2019
Figure 5: BDaaS Revenue by Vertical 2014 - 2019
Figure 6: BDaaS Revenue by Vertical 2014 - 2019
Figure 7: DaaS Revenue by Region 2014 - 2019
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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