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Global Converged Data Platform Market to reach USD XX billion by 2027.

Global Converged Data Platform Market Size study, by Type (VMware, KVM, Hyper-V) by Application (Virtual Desktop Infrastructure, Server Virtualization, Data Protection) by end use industries (Healthcare, Government, Education, IT & Telecom, Others) and Regional Forecasts 2021-2027

Product Code: ICTEITS-84100648
Publish Date: 17-10-2021
Page: 200

Global Converged Data Platform Market is valued approximately USD XX billion in 2020 and is anticipated to grow with a healthy growth rate of more than XX % over the forecast period 2021-2027. Convergence in networking refers to use of one network provider for networking services for voice, data, and video in a single network offering, instead of providing a separate network for each of these services. Convergence allows a business to use one network from one provider for all communication and cloud-based services. Growing Cloud Services Industry and rising adoption of remote office infrastructure are the key drivers for the growth of Converged Data Platform market. For instance, according to The Information Technology and Innovation Foundation (ITIF)- in 2020, the Spending on global cloud computing was estimated at USD 223 billion, up from USD 190 billion in the year 2019 and cloud companies listed on U.S. markets had more than USD 1 trillion in capitalization. Also, with the increasing government expenditure on infrastructure development and surging adoption from end use industries, the adoption & demand for Converged Data Platform is likely to increase the market growth during the forecast period. However, high initial investment for deployment of Converged Data Platform impedes the growth of the market over the forecast period of 2021-2027.

The key regions considered for the global Converged Data Platform market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America is the leading/significant region across the world in terms of market share owing to increasing demand from small and medium enterprises in the region. Whereas, Asia-Pacific is also anticipated to exhibit highest growth rate / CAGR over the forecast period 2021-2027. Factors such increasing IT infrastructure and rising penetration of smart devices and internet would create lucrative growth prospects for the Converged Data Platform market across Asia-Pacific region.

Major market player included in this report are:
Vmware Inc
Simplivity Corporation,
Scale Computing.,
Nutanix Inc,
Pivot3,
Nimboxx Inc.,
Cisco Systems, Inc.,
Maxta Inc,
Gridstore, Inc,
IBM,
The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Type:
VMware
KVM
Hyper-V
By Application:
Virtual Desktop Infrastructure
Server Virtualization
Data Protection
By End Use Industries:
Healthcare
Government
Education
IT & Telecom
Others
By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019
Base year – 2020
Forecast period – 2021 to 2027

Target Audience of the Global Converged Data Platform Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. Converged Data Platform Market, by Region, 2019-2027 (USD Billion)
1.2.2. Converged Data Platform Market, by Type, 2019-2027 (USD Billion)
1.2.3. Converged Data Platform Market, by Application, 2019-2027 (USD Billion)
1.2.4. Converged Data Platform Market, by End Use Industries, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Converged Data Platform Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Converged Data Platform Market Dynamics
3.1. Converged Data Platform Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Growing Cloud Services Industry
3.1.1.2. Rising adoption of remote office infrastructure
3.1.2. Market Challenges
3.1.2.1. High initial investment
3.1.3. Market Opportunities
3.1.3.1. Surging adoption from end use industries.
3.1.3.2. Growing government expenditure on infrastructure development.
Chapter 4. Global Converged Data Platform Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2027)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
4.5. Top investment opportunity
4.6. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1.1. Assessment of the overall impact of COVID-19 on the industry
5.1.2. Pre COVID-19 and post COVID-19 market scenario
Chapter 6. Global Converged Data Platform Market, by Type
6.1. Market Snapshot
6.2. Global Converged Data Platform Market by Type, Performance – Potential Analysis
6.3. Global Converged Data Platform Market Estimates & Forecasts by Type 2018-2027 (USD Billion)
6.4. Converged Data Platform Market, Sub Segment Analysis
6.4.1. VMware
6.4.2. KVM
6.4.3. Hyper-V

Chapter 7. Global Converged Data Platform Market, by Application
7.1. Market Snapshot
7.2. Global Converged Data Platform Market by Application, Performance – Potential Analysis
7.3. Global Converged Data Platform Market Estimates & Forecasts by Application 2018-2027 (USD Billion)
7.4. Converged Data Platform Market, Sub Segment Analysis
7.4.1. Virtual Desktop Infrastructure
7.4.2. Server Virtualization
7.4.3. Data Protection

Chapter 8. Global Converged Data Platform Market, by End Use Industries
8.1. Market Snapshot
8.2. Global Converged Data Platform Market by End Use Industries, Performance – Potential Analysis
8.3. Global Converged Data Platform Market Estimates & Forecasts by End Use Industries 2018-2027 (USD Billion)
8.4. Converged Data Platform Market, Sub Segment Analysis
8.4.1. Healthcare
8.4.2. Government
8.4.3. Education
8.4.4. IT & Telecom
8.4.5. Others

Chapter 9. Global Converged Data Platform Market, Regional Analysis
9.1. Converged Data Platform Market, Regional Market Snapshot
9.2. North America Converged Data Platform Market
9.2.1. U.S. Converged Data Platform Market
9.2.1.1. Type breakdown estimates & forecasts, 2018-2027
9.2.1.2. Application breakdown estimates & forecasts, 2018-2027
9.2.1.3. End Use Industries breakdown estimates & forecasts, 2018-2027
9.2.2. Canada Converged Data Platform Market
9.3. Europe Converged Data Platform Market Snapshot
9.3.1. U.K. Converged Data Platform Market
9.3.2. Germany Converged Data Platform Market
9.3.3. France Converged Data Platform Market
9.3.4. Spain Converged Data Platform Market
9.3.5. Italy Converged Data Platform Market
9.3.6. Rest of Europe Converged Data Platform Market
9.4. Asia-Pacific Converged Data Platform Market Snapshot
9.4.1. China Converged Data Platform Market
9.4.2. India Converged Data Platform Market
9.4.3. Japan Converged Data Platform Market
9.4.4. Australia Converged Data Platform Market
9.4.5. South Korea Converged Data Platform Market
9.4.6. Rest of Asia Pacific Converged Data Platform Market
9.5. Latin America Converged Data Platform Market Snapshot
9.5.1. Brazil Converged Data Platform Market
9.5.2. Mexico Converged Data Platform Market
9.6. Rest of The World Converged Data Platform Market

Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. Company Profiles
10.2.1. Vmware Inc
10.2.1.1. Key Information
10.2.1.2. Overview
10.2.1.3. Financial (Subject to Data Availability)
10.2.1.4. Product Summary
10.2.1.5. Recent Developments
10.2.2. Simplivity Corporation,
10.2.3. Scale Computing.,
10.2.4. Nutanix Inc,
10.2.5. Pivot3,
10.2.6. Nimboxx Inc.,
10.2.7. Cisco Systems, Inc.,
10.2.8. Maxta Inc,
10.2.9. Gridstore, Inc,
10.2.10. IBM,
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption

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Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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