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Global In-Memory Computing Market to reach USD 36.31 billion by 2027.

Global In-Memory Computing Market Size study, by Component (Solution, services), by Deployment (cloud, on-premises), by Application (Risk management and fraud detection, Sentiment analysis, Geospatial/ GIS processing, Sales and marketing optimization, Predictive analysis, Supply chain management, Others), and Regional Forecasts 2021-2027

Product Code: ICTEITS-81194814
Publish Date: 17-07-2021
Page: 200

Global In-memory computing Market is valued approximately at USD 11.4 billion in 2020 and is anticipated to grow with a healthy growth rate of more than 18% over the forecast period 2021-2027. In- memory computing is a data storage which stores data in RAM rather than in databases hosted on disks. It can cache huge amount of data and enable extremely fast response times, as well as store session data, that can help achieve optimum performance. The global in-memory computing market is being driven by exponential growth of big data to improve machine-driven decision-making and decrease in the overall cost of RAM and TCO as well as increase in production with real-time and low-latency transaction processing. Furthermore, growth in the need for parallel processing and columnar databases and emergence of AI and ML-based technologies to gain real-time actionable customer insights, will provide new opportunities for the global in-memory computing industry. For instance, according to the data of Statista, total global value of data created, copied, captured, and consumed is forecasted to increase rapidly year on year, in year 2020 the value had reached 64.2 zettabytes and over the next five years to 2025, global data creation is projected to grow by more than 180 zettabytes. In year 2020, the value of data created and replicated stood at a new high. The big data market size is also increasing year by year, in 2019 the market value was 49 billion US dollars which increased to 56 billion US dollars and it is expected to reach at 103 billion US dollars by year 2027. As a result, growth of big data market as well as high rate of data consumption, will serve as a catalyst for the in-memory computing industry in the future. However, lack of standards and increase in volatility of data may impede market growth over the forecast period of 2021-2027.

Asia Pacific, North America, Europe, Latin America, and Rest of the World are the key regions considered for the regional analysis of global In-memory computing market. The rising demand for analytics and advanced analytics platforms from the small and medium businesses and by the government agencies in the region makes North America is the leading region across the world in terms of market share. Whereas Asia pacific is also anticipated to exhibit the highest growth rate over the forecast period 2021-2027, due to adoption of In-memory computing technology by different verticals, such as manufacturing and retail which is expected to contribute to the growth of the market in the region.
Major market player included in this report are:

SAS institute
Software AG
Red hat
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 Component:
By Deployment:
By Application:
Risk management and fraud detection
Sentiment analysis
Geospatial/ GIS processing
Sales and marketing optimization
Predictive analysis
Supply chain management

By Region:
North America

Asia Pacific
South Korea
Latin America
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 In-Memory Computing 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

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. In-Memory Computing Market , by Region, 2019-2027 (USD Billion)
1.2.2. In-Memory Computing Market , by Component, 2019-2027 (USD Billion)
1.2.3. In-Memory Computing Market , by Deployment , 2019-2027 (USD Billion)
1.2.4. In-Memory Computing Market , by Application, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global In-Memory Computing 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 In-Memory Computing Market Dynamics
3.1. In-Memory Computing Market Impact Analysis (2019-2027)
3.1.1. Market Drivers Exponential growth of big data to improve machine-driven decision-making Decrease in the overall cost of RAM and TCO Increase in production with real-time and low-latency transaction processing
3.1.2. Market Restraint Lack of standards Increase in volatility of data
3.1.3. Market Opportunities Growth in the need for parallel processing and columnar databases Emergence of AI and ML-based technologies to gain real-time actionable customer insights
Chapter 4. Global In-Memory Computing 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
Chapter 5. Global In-Memory Computing Market , by Component
5.1. Market Snapshot
5.2. Global In-Memory Computing Market by Component, Performance – Potential Analysis
5.3. Global In-Memory Computing Market Estimates & Forecasts by Component 2018-2027 (USD Billion)
5.4. In-Memory Computing Market , Sub Segment Analysis
5.4.1. Solutions
5.4.2. services
Chapter 6. Global In-Memory Computing Market , by Deployment
a. Market Snapshot
6.1. Global In-Memory Computing Market by Deployment, Performance – Potential Analysis
6.2. Global In-Memory Computing Market Estimates & Forecasts by Deployment 2018-2027 (USD Billion)
6.3. In-Memory Computing Market , Sub Segment Analysis
6.3.1. Cloud
6.3.2. On-premises
Chapter 7. Global In-Memory Computing Market , by Application
b. Market Snapshot
7.1. Global In-Memory Computing Market by Application, Performance – Potential Analysis
7.2. Global In-Memory Computing Market Estimates & Forecasts by Application 2018-2027 (USD Billion)
7.3. In-Memory Computing Market , Sub Segment Analysis
7.3.1. Risk management and fraud detection
7.3.2. Sentiment analysis
7.3.3. Geospatial/ GIS processing
7.3.4. Sales and marketing optimization
7.3.5. Predictive analysis
7.3.6. Supply chain management
7.3.7. Others
Chapter 8. Global In-Memory Computing Market , Regional Analysis
8.1. In-Memory Computing Market , Regional Market Snapshot
8.2. North America In-Memory Computing Market
8.2.1. U.S. In-Memory Computing Market Component breakdown estimates & forecasts, 2018-2027 Deployment breakdown estimates & forecasts, 2018-2027 Application breakdown estimates & forecasts, 2018-2027
8.2.2. Canada In-Memory Computing Market
8.3. Europe In-Memory Computing Market Snapshot
8.3.1. U.K. In-Memory Computing Market
8.3.2. Germany In-Memory Computing Market
8.3.3. France In-Memory Computing Market
8.3.4. Spain In-Memory Computing Market
8.3.5. Italy In-Memory Computing Market
8.3.6. Rest of Europe In-Memory Computing Market
8.4. Asia-Pacific In-Memory Computing Market Snapshot
8.4.1. China In-Memory Computing Market
8.4.2. India In-Memory Computing Market
8.4.3. Japan In-Memory Computing Market
8.4.4. Australia In-Memory Computing Market
8.4.5. South Korea In-Memory Computing Market
8.4.6. Rest of Asia Pacific In-Memory Computing Market
8.5. Latin America In-Memory Computing Market Snapshot
8.5.1. Brazil In-Memory Computing Market
8.5.2. Mexico In-Memory Computing Market
8.6. Rest of The World In-Memory Computing Market
Chapter 9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
9.2.1. IBM Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
9.2.2. SAP
9.2.3. Oracle
9.2.4. SAS institute
9.2.5. Microsoft
9.2.6. Tibco
9.2.7. Altibase
9.2.8. GigaSpaces
9.2.9. Software AG
9.2.10. Red hat

Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
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:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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