The AI-powered storage market size reached US$ 16.6 billion in 2022. Demand for AI-powered storage solutions stood at US$ 21.0 billion in 2023.
In the forecast period between 2023 and 2033, demand is poised to exhibit a 20.7% CAGR. Revenue is anticipated to cross around US$ 138.0 billion by the end of 2033.
Artificial Intelligence (AI) has transformed several sectors, and storage applications are no exception. AI-powered storage solutions are intended to automate activities, improve performance, and secure data.
AI-powered storage evaluates data trends and makes data management choices in real-time using powerful machine learning algorithms. Organizations might improve the scalability, flexibility, and reliability of their storage infrastructure by employing AI.
AI-powered storage solutions can be categorized into different types, including intelligent data management, predictive analytics, and autonomous storage. Intelligent data management systems use AI algorithms to classify, organize, and optimize data storage based on factors such as access frequency and data value.
Predictive analytics enables organizations to anticipate storage needs and make proactive decisions, minimizing the risk of data loss or downtime. Autonomous storage systems can self-adjust and optimize performance, adapting to changing workloads and ensuring data availability.
AI-powered storage offers several benefits to organizations. It reduces manual intervention, improving operational efficiency and reducing the risk of human errors. It enables organizations to manage large volumes of data more effectively, leading to improved decision-making and fast time to market.
AI-powered storage can enhance data security by detecting anomalies and potential threats, protecting critical data from unauthorized access. The increased popularity of cloud storage and hybrid settings is one of the most significant developments in this sector. As more firms grasp the benefits of scalability, flexibility, and cost-effectiveness provided by the cloud. They are shifting their data storage strategies accordingly.
The combination of AI and cloud storage enables organizations to optimize their storage infrastructure, access data remotely, and effortlessly scale up or down as per their requirements.
The adoption of AI-powered storage solutions is being driven by the need for rapid data processing and analytics. AI algorithms, with their capacity to swiftly analyze huge volumes of data, improve decision-making processes and enable enterprises to gain important insights from stored data in real-time. As a result, they can respond to market trends quickly, anticipate possible hazards, and make data-driven business decisions.
Machine learning is another key driver in the AI-powered storage market. By leveraging machine learning algorithms, organizations can optimize storage performance and reduce costs. These algorithms learn from patterns in data usage, predict future storage needs, and dynamically allocate resources accordingly. This leads to improved storage efficiency, reduced downtime, and ultimately, cost savings.
| Attributes | Key Insights |
|---|---|
| AI-Powered Storage Market Estimated Size (2023E) | US$ 21.0 billion |
| Projected Market Valuation (2033F) | US$ 138.0 billion |
| Value-based CAGR (2023 to 2033) | 20.7% |
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AI-powered storage market experienced growth at 26.7% CAGR during the historical period from 2018 to 2022. In the forecast period, sales are likely to accelerate at 20.7% CAGR. It was created a value of US$ 16.6 billion in the base year.
The market witnessed a growing adoption of AI-powered storage solutions across various sectors due to the need for efficient data management and analysis. AI-powered storage solutions were focused on optimizing performance by automatically adjusting storage resources based on usage patterns and workload demands.
AI-driven storage systems incorporated advanced security measures such as anomaly detection and real-time threat analysis to safeguard sensitive data from cyberattacks. NLP-based AI storage solutions emerged, allowing users to interact with storage systems using natural language queries, simplifying data retrieval and management.
The implementation of AI-powered storage at the edge of network has helped streamline data processing, reducing latency and enabled real-time decision-making. AI-powered storage solutions have focused on optimizing performance by automatically adjusting storage resources based on usage patterns and workload demands.
AI-driven storage systems have incorporated advanced security measures such as anomaly detection and real-time threat analysis to safeguard sensitive data from cyberattacks. Integration of AI with cloud storage platforms has enabled businesses to leverage the scalability and flexibility of cloud computing while benefiting from AI-driven insights.
NLP-based AI storage solutions have emerged, allowing users to interact with storage systems using natural language queries, simplifying data retrieval and management. The implementation of AI-powered storage at the edge helped streamline data processing, reducing latency and enabling real-time decision-making in IoT and edge computing applications.
Cloud Integration:
Cloud integration allows businesses to easily scale their AI-powered storage solutions based on their specific needs. It accommodates data growth and fluctuating demands effectively. These storage solutions offer greater flexibility in terms of data access and management. It enables seamless integration with diverse applications and platforms.
Cloud integration often eliminates the need for upfront infrastructure investments. It makes it more cost-effective for businesses to adopt AI-powered storage solutions. Cloud-based AI storage leverages distributed computing and advanced algorithms. It will lead to improved data processing speeds and overall performance.
Cloud-based storage also enables remote access to AI capabilities. It allows users to interact with and utilize AI-powered features from different locations and devices. By leveraging cloud resources, AI-powered storage solutions can analyze vast amounts of data and generate valuable insights.
Enhanced Data Management:
AI-driven data management can optimize data storage, leading to reduced wastage and increased efficiency in storage utilization. Integrated analytics can provide valuable insights into data patterns and trends. It enables organizations to make better-informed decisions about data storage requirements.
AI algorithms can automatically classify and tier data based on its importance and usage patterns. It ensures that the most critical data is stored on high-performance storage systems. It can also predict potential storage failures and proactively initiate maintenance actions. This will help in minimizing downtime and enhancing overall system reliability.
AI can also help optimize storage costs by identifying cost-effective storage solutions based on data access patterns and business needs. Data management solutions aided by AI can improve security measures by identifying potential threats and ensuring compliance with data protection regulations. These solutions can deliver personalized user experiences by tailoring data access based on individual preferences and behavior.
Principal Consultant
As AI-powered storage systems handle vast amounts of sensitive data, ensuring data privacy and security becomes a significant challenge. Any breaches or unauthorized access could lead to severe consequences.
Adhering to various data protection regulations and sector standards can be complex. This is especially the case when dealing with data from different regions and jurisdictions.
Integrating AI-powered storage systems with existing IT infrastructures can be challenging and time-consuming.
Ensuring seamless compatibility with various data formats and storage technologies can be problematic, leading to potential data loss or corruption.
AI algorithms used in storage systems might inadvertently perpetuate biases present in the data, leading to unfair decision-making or analysis.
The United States AI-powered storage industry is set to hold a total of around US$ 45.3 billion by 2033. It is predicted to expand at 19.4% CAGR during the forecast period 2023 to 2033.
Several crucial reasons contribute to the United States' dominance in the global AI-powered storage market. The United States is house to the world's leading IT companies and inventors.
The United States has invested in in AI that fosters AI research and development. It provides a thriving atmosphere for both start-ups and existing businesses. As a result, a robust AI ecosystem has emerged driving the use of AI-powered storage devices throughout different sectors.
The United States has a robust data center network and a well-developed communications network. It lays a solid platform for the adoption of AI-powered storage systems. The accessibility of massive data centers and lightning-fast internet has aided in the broad adoption of cloud-based artificial intelligence storage services.
China AI-powered industry is poised to exhibit a CAGR of 21.7% during the assessment period. By 2033, China is expected to reach US$ 11.8 billion.
The Chinese government and tech giants have poured substantial financial resources into AI research, development, and infrastructure. It enables them to build cutting-edge storage solutions. China has a thriving IT economy, with businesses such as Alibaba, Tencent, Huawei, and Baidu spearheading AI development and storage solutions.
The Chinese government established regulations that encourage the growth of AI sectors by encouraging innovation and driving market expansion. China's large domestic market has allowed AI-powered storage companies to scale rapidly. It provides a valuable experience and resources for international expansion.
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By storage system, storage area network division is set to monopolize the global market during assessment period. Sales are poised to expand at 20.6% CAGR in between 2023 to 2033.
Storage area networks offer high-performance, scalable, and centralized storage solutions. In the context of the AI-powered storage market, SANs could become dominant due to several reasons. AI applications generate vast amounts of data, and SANs are designed to scale efficiently to meet these storage demands. It makes them suitable for AI workloads during the forecast period.
SANs utilize fiber channel or Ethernet protocols, providing high-speed connectivity that can accommodate the fast data access required by AI applications. AI workloads often involve data-intensive operations such as training and inferencing. SANs can deliver low-latency access to data, ensuring efficient processing of AI tasks.
SANs allow organizations to consolidate their storage resources, making it easy to manage and utilize storage capacity across AI infrastructure. It offers shared access to data among multiple servers, enabling collaborative AI projects. This will enhance the accessibility of data for AI applications.
SANs provide robust data security features, ensuring data integrity and protection, which is crucial when dealing with sensitive AI datasets. SAN vendors may develop specialized features or integrations that cater specifically to AI workloads. This makes them more attractive to organizations adopting AI technologies.
By end-user, the enterprises segment is set to spearhead the global AI-powered storage market. The abovementioned segment is expected to witness a 20.5% CAGR from 2023 to 2033. Enterprises generate vast amounts of data, and with the increasing adoption of AI and machine learning applications. The need for efficient and scalable storage solutions becomes crucial.
AI-powered apps and workloads are increasingly being used in companies for a variety of purposes. It includes data analysis, predictive modeling, and automation. This increases the demand for AI-powered storage systems capable of handling the unique requirements of these workloads.
AI-powered storage solutions offer better performance, intelligent data management, and automated data optimization. This makes them attractive options for enterprises aiming to improve efficiency and reduce operational costs.
Enterprises often require tailored storage solutions to address their unique needs and accommodate growth. AI-powered storage solutions can be more versatile and scalable, allowing for the agility required in business applications. Adoption of AI-powered storage might offer businesses with a competitive advantage by allowing for fast analysis of data, real-time analytics, and increased decision-making capabilities.
Key manufacturers have been focusing on developing AI-driven storage solutions that optimize performance. These solutions are aimed to help in enhancing data management, and enable intelligent data analysis. They are forming partnerships with AI companies to integrate their technology into storage solutions,
Key manufacturers are offering more advanced and efficient AI-powered features. They were leveraging AI to enhance cloud-based storage solutions, providing intelligent data storage, retrieval, and management capabilities.
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| Attribute | Details |
|---|---|
| Estimated Market Size (2023) | US$ 21.0 billion |
| Projected Market Valuation (2033) | US$ 138.0 billion |
| Value-based CAGR (2023 to 2033) | 20.7% |
| Historical Data | 2018 to 2022 |
| Forecast Period | 2023 to 2033 |
| Quantitative Units | Value (US$ million) |
| Segments Covered | Offering, Storage System, Storage Architecture, Storage Medium, End-user, Region |
| Regions Covered | North America; Latin America; East Asia; South Asia Pacific; Western Europe; Eastern Europe; Middle East & Africa |
| Key Countries Covered | United States, Canada, Brazil, Mexico, Germany, Italy, France, United Kingdom, Spain, Russia, GCC Countries, India, China, Japan and Australia |
| Key Companies Profiled | Intel Corporation; NVIDIA Corporation; IBM; Samsung Electronics; Pure Storage |
| Report Coverage | Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends and Pricing Analysis |
The market is valued at US$ 21.0 billion in 2023.
Intel Corporation, NVIDIA Corporation, and IBM are the vital market players.
The market’s CAGR from 2023 to 2033 is estimated to be 20.7%.
Adoption of AI-powered storage solution to fuel the market.
From 2018 to 2022, the market registered a CAGR of 26.7%.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033
4.1. Historical Market Size Value (US$ million) Analysis, 2018 to 2022
4.2. Current and Future Market Size Value (US$ million) Projections, 2023 to 2033
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Offering
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ million) Analysis By Offering, 2018 to 2022
5.3. Current and Future Market Size Value (US$ million) Analysis and Forecast By Offering, 2023 to 2033
5.3.1. Hardware
5.3.2. Software
5.4. Y-o-Y Growth Trend Analysis By Offering, 2018 to 2022
5.5. Absolute $ Opportunity Analysis By Offering, 2023 to 2033
6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Storage System
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ million) Analysis By Storage System, 2018 to 2022
6.3. Current and Future Market Size Value (US$ million) Analysis and Forecast By Storage System, 2023 to 2033
6.3.1. Direct-Attached Storage (DAS)
6.3.2. Network-Attached Storage (NAS)
6.3.3. Storage Area Network (SAN)
6.4. Y-o-Y Growth Trend Analysis By Storage System, 2018 to 2022
6.5. Absolute $ Opportunity Analysis By Storage System, 2023 to 2033
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Storage Architecture
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ million) Analysis By Storage Architecture, 2018 to 2022
7.3. Current and Future Market Size Value (US$ million) Analysis and Forecast By Storage Architecture, 2023 to 2033
7.3.1. File- and Object-Based Storage
7.3.2. Object Storage
7.4. Y-o-Y Growth Trend Analysis By Storage Architecture, 2018 to 2022
7.5. Absolute $ Opportunity Analysis By Storage Architecture, 2023 to 2033
8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Storage Medium
8.1. Introduction / Key Findings
8.2. Historical Market Size Value (US$ million) Analysis By Storage Medium, 2018 to 2022
8.3. Current and Future Market Size Value (US$ million) Analysis and Forecast By Storage Medium, 2023 to 2033
8.3.1. Hard Disk Drive (HDD)
8.3.2. Solid State Drive (SSD)
8.4. Y-o-Y Growth Trend Analysis By Storage Medium, 2018 to 2022
8.5. Absolute $ Opportunity Analysis By Storage Medium, 2023 to 2033
9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By End User
9.1. Introduction / Key Findings
9.2. Historical Market Size Value (US$ million) Analysis By End User, 2018 to 2022
9.3. Current and Future Market Size Value (US$ million) Analysis and Forecast By End User, 2023 to 2033
9.3.1. Enterprises
9.3.2. Government Bodies
9.3.3. Cloud Service Providers
9.3.4. Telecom Companies
9.4. Y-o-Y Growth Trend Analysis By End User, 2018 to 2022
9.5. Absolute $ Opportunity Analysis By End User, 2023 to 2033
10. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
10.1. Introduction
10.2. Historical Market Size Value (US$ million) Analysis By Region, 2018 to 2022
10.3. Current Market Size Value (US$ million) Analysis and Forecast By Region, 2023 to 2033
10.3.1. North America
10.3.2. Latin America
10.3.3. Europe
10.3.4. Asia Pacific
10.3.5. Middle East & Africa
10.4. Market Attractiveness Analysis By Region
11. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
11.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2018 to 2022
11.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2023 to 2033
11.2.1. By Country
11.2.1.1. United States
11.2.1.2. Canada
11.2.2. By Offering
11.2.3. By Storage System
11.2.4. By Storage Architecture
11.2.5. By Storage Medium
11.2.6. By End User
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Offering
11.3.3. By Storage System
11.3.4. By Storage Architecture
11.3.5. By Storage Medium
11.3.6. By End User
11.4. Key Takeaways
12. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
12.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2018 to 2022
12.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2023 to 2033
12.2.1. By Country
12.2.1.1. Brazil
12.2.1.2. Mexico
12.2.1.3. Rest of Latin America
12.2.2. By Offering
12.2.3. By Storage System
12.2.4. By Storage Architecture
12.2.5. By Storage Medium
12.2.6. By End User
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Offering
12.3.3. By Storage System
12.3.4. By Storage Architecture
12.3.5. By Storage Medium
12.3.6. By End User
12.4. Key Takeaways
13. Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
13.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2018 to 2022
13.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2023 to 2033
13.2.1. By Country
13.2.1.1. Germany
13.2.1.2. United Kingdom
13.2.1.3. France
13.2.1.4. Spain
13.2.1.5. Italy
13.2.1.6. Rest of Europe
13.2.2. By Offering
13.2.3. By Storage System
13.2.4. By Storage Architecture
13.2.5. By Storage Medium
13.2.6. By End User
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Offering
13.3.3. By Storage System
13.3.4. By Storage Architecture
13.3.5. By Storage Medium
13.3.6. By End User
13.4. Key Takeaways
14. Asia Pacific Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
14.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2018 to 2022
14.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2023 to 2033
14.2.1. By Country
14.2.1.1. China
14.2.1.2. Japan
14.2.1.3. South Korea
14.2.1.4. India
14.2.1.5. Malaysia
14.2.1.6. Singapore
14.2.1.7. Australia
14.2.1.8. New Zealand
14.2.1.9. Rest of Asia Pacific
14.2.2. By Offering
14.2.3. By Storage System
14.2.4. By Storage Architecture
14.2.5. By Storage Medium
14.2.6. By End User
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Offering
14.3.3. By Storage System
14.3.4. By Storage Architecture
14.3.5. By Storage Medium
14.3.6. By End User
14.4. Key Takeaways
15. Middle East & Africa Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
15.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2018 to 2022
15.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2023 to 2033
15.2.1. By Country
15.2.1.1. GCC Countries
15.2.1.2. South Africa
15.2.1.3. Israel
15.2.1.4. Rest of Middle East & Africa
15.2.2. By Offering
15.2.3. By Storage System
15.2.4. By Storage Architecture
15.2.5. By Storage Medium
15.2.6. By End User
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Offering
15.3.3. By Storage System
15.3.4. By Storage Architecture
15.3.5. By Storage Medium
15.3.6. By End User
15.4. Key Takeaways
16. Key Countries Market Analysis
16.1. United States
16.1.1. Pricing Analysis
16.1.2. Market Share Analysis, 2022
16.1.2.1. By Offering
16.1.2.2. By Storage System
16.1.2.3. By Storage Architecture
16.1.2.4. By Storage Medium
16.1.2.5. By End User
16.2. Canada
16.2.1. Pricing Analysis
16.2.2. Market Share Analysis, 2022
16.2.2.1. By Offering
16.2.2.2. By Storage System
16.2.2.3. By Storage Architecture
16.2.2.4. By Storage Medium
16.2.2.5. By End User
16.3. Brazil
16.3.1. Pricing Analysis
16.3.2. Market Share Analysis, 2022
16.3.2.1. By Offering
16.3.2.2. By Storage System
16.3.2.3. By Storage Architecture
16.3.2.4. By Storage Medium
16.3.2.5. By End User
16.4. Mexico
16.4.1. Pricing Analysis
16.4.2. Market Share Analysis, 2022
16.4.2.1. By Offering
16.4.2.2. By Storage System
16.4.2.3. By Storage Architecture
16.4.2.4. By Storage Medium
16.4.2.5. By End User
16.5. Germany
16.5.1. Pricing Analysis
16.5.2. Market Share Analysis, 2022
16.5.2.1. By Offering
16.5.2.2. By Storage System
16.5.2.3. By Storage Architecture
16.5.2.4. By Storage Medium
16.5.2.5. By End User
16.6. United Kingdom
16.6.1. Pricing Analysis
16.6.2. Market Share Analysis, 2022
16.6.2.1. By Offering
16.6.2.2. By Storage System
16.6.2.3. By Storage Architecture
16.6.2.4. By Storage Medium
16.6.2.5. By End User
16.7. France
16.7.1. Pricing Analysis
16.7.2. Market Share Analysis, 2022
16.7.2.1. By Offering
16.7.2.2. By Storage System
16.7.2.3. By Storage Architecture
16.7.2.4. By Storage Medium
16.7.2.5. By End User
16.8. Spain
16.8.1. Pricing Analysis
16.8.2. Market Share Analysis, 2022
16.8.2.1. By Offering
16.8.2.2. By Storage System
16.8.2.3. By Storage Architecture
16.8.2.4. By Storage Medium
16.8.2.5. By End User
16.9. Italy
16.9.1. Pricing Analysis
16.9.2. Market Share Analysis, 2022
16.9.2.1. By Offering
16.9.2.2. By Storage System
16.9.2.3. By Storage Architecture
16.9.2.4. By Storage Medium
16.9.2.5. By End User
16.10. China
16.10.1. Pricing Analysis
16.10.2. Market Share Analysis, 2022
16.10.2.1. By Offering
16.10.2.2. By Storage System
16.10.2.3. By Storage Architecture
16.10.2.4. By Storage Medium
16.10.2.5. By End User
16.11. Japan
16.11.1. Pricing Analysis
16.11.2. Market Share Analysis, 2022
16.11.2.1. By Offering
16.11.2.2. By Storage System
16.11.2.3. By Storage Architecture
16.11.2.4. By Storage Medium
16.11.2.5. By End User
16.12. South Korea
16.12.1. Pricing Analysis
16.12.2. Market Share Analysis, 2022
16.12.2.1. By Offering
16.12.2.2. By Storage System
16.12.2.3. By Storage Architecture
16.12.2.4. By Storage Medium
16.12.2.5. By End User
16.13. Malaysia
16.13.1. Pricing Analysis
16.13.2. Market Share Analysis, 2022
16.13.2.1. By Offering
16.13.2.2. By Storage System
16.13.2.3. By Storage Architecture
16.13.2.4. By Storage Medium
16.13.2.5. By End User
16.14. Singapore
16.14.1. Pricing Analysis
16.14.2. Market Share Analysis, 2022
16.14.2.1. By Offering
16.14.2.2. By Storage System
16.14.2.3. By Storage Architecture
16.14.2.4. By Storage Medium
16.14.2.5. By End User
16.15. Australia
16.15.1. Pricing Analysis
16.15.2. Market Share Analysis, 2022
16.15.2.1. By Offering
16.15.2.2. By Storage System
16.15.2.3. By Storage Architecture
16.15.2.4. By Storage Medium
16.15.2.5. By End User
16.16. New Zealand
16.16.1. Pricing Analysis
16.16.2. Market Share Analysis, 2022
16.16.2.1. By Offering
16.16.2.2. By Storage System
16.16.2.3. By Storage Architecture
16.16.2.4. By Storage Medium
16.16.2.5. By End User
16.17. GCC Countries
16.17.1. Pricing Analysis
16.17.2. Market Share Analysis, 2022
16.17.2.1. By Offering
16.17.2.2. By Storage System
16.17.2.3. By Storage Architecture
16.17.2.4. By Storage Medium
16.17.2.5. By End User
16.18. South Africa
16.18.1. Pricing Analysis
16.18.2. Market Share Analysis, 2022
16.18.2.1. By Offering
16.18.2.2. By Storage System
16.18.2.3. By Storage Architecture
16.18.2.4. By Storage Medium
16.18.2.5. By End User
16.19. Israel
16.19.1. Pricing Analysis
16.19.2. Market Share Analysis, 2022
16.19.2.1. By Offering
16.19.2.2. By Storage System
16.19.2.3. By Storage Architecture
16.19.2.4. By Storage Medium
16.19.2.5. By End User
17. Market Structure Analysis
17.1. Competition Dashboard
17.2. Competition Benchmarking
17.3. Market Share Analysis of Top Players
17.3.1. By Regional
17.3.2. By Offering
17.3.3. By Storage System
17.3.4. By Storage Architecture
17.3.5. By Storage Medium
17.3.6. By End User
18. Competition Analysis
18.1. Competition Deep Dive
18.1.1. Intel Corporation
18.1.1.1. Overview
18.1.1.2. Product Portfolio
18.1.1.3. Profitability by Market Segments
18.1.1.4. Sales Footprint
18.1.1.4.1. Marketing Strategy
18.1.2. NVIDIA Corporation
18.1.2.1. Overview
18.1.2.2. Product Portfolio
18.1.2.3. Profitability by Market Segments
18.1.2.4. Sales Footprint
18.1.2.4.1. Marketing Strategy
18.1.3. IBM
18.1.3.1. Overview
18.1.3.2. Product Portfolio
18.1.3.3. Profitability by Market Segments
18.1.3.4. Sales Footprint
18.1.3.4.1. Marketing Strategy
18.1.4. Samsung Electronics
18.1.4.1. Overview
18.1.4.2. Product Portfolio
18.1.4.3. Profitability by Market Segments
18.1.4.4. Sales Footprint
18.1.4.4.1. Marketing Strategy
18.1.5. Pure Storage
18.1.5.1. Overview
18.1.5.2. Product Portfolio
18.1.5.3. Profitability by Market Segments
18.1.5.4. Sales Footprint
18.1.5.4.1. Marketing Strategy
18.1.6. NetApp
18.1.6.1. Overview
18.1.6.2. Product Portfolio
18.1.6.3. Profitability by Market Segments
18.1.6.4. Sales Footprint
18.1.6.4.1. Marketing Strategy
18.1.7. Micron Technology
18.1.7.1. Overview
18.1.7.2. Product Portfolio
18.1.7.3. Profitability by Market Segments
18.1.7.4. Sales Footprint
18.1.7.4.1. Marketing Strategy
18.1.8. CISCO
18.1.8.1. Overview
18.1.8.2. Product Portfolio
18.1.8.3. Profitability by Market Segments
18.1.8.4. Sales Footprint
18.1.8.4.1. Marketing Strategy
18.1.9. Toshiba
18.1.9.1. Overview
18.1.9.2. Product Portfolio
18.1.9.3. Profitability by Market Segments
18.1.9.4. Sales Footprint
18.1.9.4.1. Marketing Strategy
18.1.10. Hitachi
18.1.10.1. Overview
18.1.10.2. Product Portfolio
18.1.10.3. Profitability by Market Segments
18.1.10.4. Sales Footprint
18.1.10.4.1. Marketing Strategy
18.1.11. Lenovo
18.1.11.1. Overview
18.1.11.2. Product Portfolio
18.1.11.3. Profitability by Market Segments
18.1.11.4. Sales Footprint
18.1.11.4.1. Marketing Strategy
18.1.12. Dell Technologies
18.1.12.1. Overview
18.1.12.2. Product Portfolio
18.1.12.3. Profitability by Market Segments
18.1.12.4. Sales Footprint
18.1.12.4.1. Marketing Strategy
19. Assumptions & Acronyms Used
20. Research Methodology
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