The global in-memory computing market is projected to advance at a CAGR of 17.5% throughout the forecast period. The industry is expected to reach US$ 77.7 Bn by 2032, from US$ 15.5 Bn in 2022.
The exponential expansion of big data to better machine-driven decision-making, a reduction in overall RAM and TCO costs, and an increase in production with real-time and low-latency transactional processing are driving the in-memory computing market share forward.
One of the critical issues in the in-memory computing market is data security concerns and the inability to construct BI applications. Furthermore, one of the major restraints in the industry is the lack of standards, increasing data volatility, a competent workforce, and the complexity of data extraction in the analytics market.
The growing demand for quicker processing and analytics of big data and the increased use of internet services are projected to positively impact the in-memory computing market adoption trends.
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In addition, the market is growing due to a rising acceptance of in-memory computing to obtain a competitive advantage, improve performance, and boost the analytics market presence.
However, data instability and concerns about data security are stifling the in-memory computing market’s expansion. Furthermore, in-memory computing technology‘s capacity to bring speed, scalability, flexibility, and accessibility at a low cost present good potential for market expansion over the projection period.
Organizations are now integrating techniques linked to advanced analytics, a part of BI, such as Analytics of Things (AoT), predictive analytics, and machine learning ML, into their in-memory computing systems.
These procedures are challenging to master and necessitate extensive analytical understanding. Instead of evaluating big data, organizations spend most of their effort acquiring and correcting data generated from numerous sources. It is unnecessary for all personnel who work with data to be data scientists.
To assist establish a data-driven and decision-making culture, business expertise is also essential, along with suitable training. As a result, one of the most significant difficulties faced by most end-user firms in the in-memory computing market is a capability gap in analytics, which is required to construct complicated BI solutions.
The lack of qualified technical workers and consulting expertise is the most significant obstacle to in-memory computing implementation. In the early stages of adopting radical technologies, many businesses or technology adopters face common obstacles. The lack of general standards and the absence of proven architectural patterns are two of these issues.
With the development of successful vendors, a growing network of implementation partners, and current open-source momentum, talent availability issues are projected to fade quickly; yet, in-memory computing and the analytics market remain a niche technology area, and skill availability remains a challenge for the time being.
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Key Takeaways
- The US in-memory computing market share is projected to reach US$ 24.8 Bn by 2032.
- The requirement for enterprises to sharply focus on the expansion of their risk intelligence skills to combat risk exposures is related to the growth of the risk management and fraud detection segment.
- Due to the existence of growing countries such as India and China, Asia-Pacific in-memory computing technology is predicted to increase significantly during the projection period. These countries are notable for their business process outsourcing (BPO) and knowledge process outsourcing (KPO) services (KPO).
- In-memory technology has been on the market for several years, and the desire for smart, interactive experiences necessitates high-performance back-end systems and programmes that send data faster.
- The demand for in-memory computing in China is expected to advance at a CAGR of 16.7% by 2032.
Competitive Landscape
Major businesses such as SAP SE, Microsoft, Oracle, Altibase, and others dominate the in-memory computing market. To meet market demands, these companies used a variety of techniques, including new product creation, collaborations and partnerships, and business expansions.
Acquisitions and integrations have been a major strategy for big in-memory computing industry players to increase their market share, security portfolio, and client base.
Key Players :
- IBM
- SAP
- ORACLE
- SAS INSTITUTE
- MICROSOFT
- TIBCO
- ALTIBASE
- GIGASPACES
- SOFTWARE AG
- INTEL
- SALESFORCE
- WORKDAY
- FUJITSU
- QLIK
Recent Developments
Microsoft will deploy the next edition of its Azure Stack HCI service in July 2020. Microsoft is releasing a preview of Azure Stack HCI v2, which will allow users to run the service on their own servers and provide built-in Azure Arc integration, among other new capabilities. The new Azure Stack HCI iteration is a new host OS that is different from Windows Server and is developed particularly to operate HCI and is integrated with Azure by design.
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Key Segments:
By Components
- Solution
- In-Memory Database (IMED)
- In-Memory Data Grid (IMDG)
- Service
- Professional Services
- Managed Services
By Application
- Risk Management & Fraud Detection
- Sentiment Analysis
- Geospatial/GIS Processing
- Sales & Marketing Optimization
- Predictive analysis
- Supply Chain Management
- Image Processing
- Route Optimization
By Deployment
- On-premise
- Cloud
By Vertical
- BFSI
- IT and Telecom
- Manufacturing
- Healthcare & Lifestyle
- Education
- Retail & E-Commerce
- Transportation & Logistics
- Government & Defense
- Energy & Utilities
- Media & Entertainment
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