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Oracle’s Data as a Service (DaaS) Offering

Oracle’s Data as a Service (DaaS) Offering

Through many acquisitions in the ICT and digital media space, Oracle is positioning itself to be a preferred provider of the core technologies for cloud computing, communications, content, commerce and applications. The company is also leveraging its data management experience and expanding its Cloud Computing service with its Data as a Service (DaaS) offering.

DaaS is defined as any service offered wherein users can access vendor provided databases to utilize data in a Cloud-based service environment. 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).

This research evaluates the DaaS market opportunity with a focus on anticipated industry leader, Oracle. The report analyzes Oracle’s strategy, opportunities, and outlook. 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 service providers
Data integration companies
Big Data and Analytics companies
Wireless infrastructure companies
Telecom managed service companies
Cloud infrastructure and XaaS providers
Intermediaries and mediation companies
Enterprise, medium, and small businesses

Report Benefits:

BDaaS Forecast to 2019
Understand Data as a Service
Identify DaaS issues and challenges
Recognize the market potential for DaaS
Understand how DaaS is leverages Oracle DB
Identify Oracle’s potential strategies and solutions

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 Key Findings
1.3 Target Audience
2 Company Overview
2.1 Strategy
2.1.1 General Company Strategies
2.1.2 DaaS Specific Strategies
3 Data as a Service
3.1 Overview
3.2 Marketplace
3.2.1 Overview
3.2.2 DaaS Adoption Factors
3.3 Forecasts
4 Oracle’s DaaS Products and Services
4.1 Overview
4.2 Current Services
4.2.1 Oracle DaaS for Marketing
4.2.2 Oracle DaaS for Sales
4.2.3 Oracle DaaS for Social
5 SWOT Analysis
6 Conclusions and Recommendations
6.1 Externalizing Data to Third Parties vs. Internal Data for Oracle Solutions
6.2 Future Oracle DaaS Solution Areas
6.2.1 Business Intelligence as a Service
6.2.2 Verification and Authorization as a Service
6.2.3 Reporting and Analytics
6.1 Oracle Specific Strategies and Offerings
6.1.1 Create further Barriers for Database Competitors
6.1.2 Create Standardized Ecosystem for DaaS
6.1.3 Create Industry Accepted Business Models
7 Appendix: Existing and Future Opportunities
7.1 Big Data Services
7.2 Analytics Services
7.1 Blending Data Types: Structured and Big Data
Figure 1: Oracle in Cloud, Coms, Apps, Content and Commerce
Figure 2: DaaS Model Architecture
Figure 3: BDaaS Forecast through 2019
Figure 4: DaaS Verification and Authorization
Figure 5: Oracle Big Data Offerings
Figure 6: Data Integration and Blending Architecture
Figure 7: Federated vs. Non-Federated Models

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