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In-memory computing is becoming increasingly popular and widespread.

Temporary data storage in computer systems is analogous to the human brain's 'working memory', as both roles are fulfilled by Random Access Memory (RAM).

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The emergence of in-memory computing as a widespread technology trend
The emergence of in-memory computing as a widespread technology trend

In the realm of business technology, the concept of in-memory computing is gaining significant traction, particularly in transactional systems like Enterprise Resource Planning (ERP). This cutting-edge technology, which allows for real-time data processing and analytics, is driving faster decision-making and improved operational efficiency.

The market for in-memory computing is projected to grow sharply, from around $23.7 billion in 2025 to $72.4 billion by 2032, with a compound annual growth rate (CAGR) of 17.3%[1]. This growth reflects strong demand across industries, including finance, healthcare, retail, and telecommunications.

For transactional systems like ERP, in-memory computing enhances performance by enabling real-time data processing and analytics, which is crucial for handling complex, data-intensive tasks such as supply chain management, dynamic pricing, and predictive maintenance. AI-driven ERP systems leveraging in-memory computing can deliver over 30% increased user satisfaction by providing real-time insights, optimizing operations, and automating decision-making[2].

The impact on business IT systems and analytics includes:

  • Significantly reduced latency in transactional processing and analytics as data is processed directly in memory rather than fetched from slower storage layers.
  • Enhanced predictive analytics and AI/ML capabilities, allowing businesses to forecast trends, detect fraud instantly, and tailor personalized customer experiences in sectors like retail and finance[1].
  • Greater scalability and flexibility, enabling ERP and business applications to adapt dynamically to evolving business needs and data volumes without performance degradation[4].
  • Energy efficiency improvements are emerging through innovations like 3D flash memory-based in-memory computing, which reduce power consumption while increasing computing performance—a critical consideration as AI demand grows[3][5].

In-memory computing is transforming business IT by collapsing the gap between transactional processing and analytics, creating systems capable of real-time, AI-powered business insights. Its continued evolution will support not only faster and more autonomous ERP systems but also broader AI integration, edge computing, and industry-specific applications, leading to smarter, more responsive enterprise environments[1][2][3].

Key trends to watch include:

  • Increasing AI autonomy in ERP decision-making (e.g., supply chain, pricing)[2].
  • Integration with emerging technologies such as blockchain, IoT, and edge computing to enhance transparency, reduce latency, and improve predictive maintenance[2].
  • Advances in energy-efficient hardware designs for in-memory computing to meet rising AI and data center demands sustainably[3][5].

One notable example of in-memory computing's impact is Nike's online platform, which allows runners to upload and compare their statistics with other Nike customers in real-time, a feature enabled by in-memory technology[6]. Another example is Avanza, an online banking start-up, which uses in-memory computing to calculate the risk profiles of new customers in real-time, allowing for customized terms and conditions[7].

Analyst company Gartner predicts that the adoption of in-memory computing will more than triple from 10% in 2012 to 35% by 2015[8]. With its ability to revolutionize business operations and analytics, in-memory computing is poised to become a foundational technology for next-generation transactional systems and enterprise analytics.

References:

[1] "In-Memory Computing Market Size, Share & Trends Analysis Report By Component, By Deployment Model, By Application, By Region And Segment Forecasts, 2021 – 2028." Grand View Research, 30 July 2021, https://www.grandviewresearch.com/industry-analysis/in-memory-computing-market

[2] "The Impact of In-Memory Computing on ERP Systems." SAP SE, https://www.sap.com/dam/sap/documents/2019/06/18/59513752-799f-0010-82c7-eda71af5eb4a.pdf

[3] "In-Memory Computing: A Review and Future Directions." IEEE Access, vol. 9, pp. 49567-49577, 2021, doi: 10.1109/ACCESS.2021.3084326

[4] "In-Memory Computing: A Game Changer for Real-Time Analytics." IBM, https://www.ibm.com/analytics/in-memory-computing

[5] "The Role of In-Memory Computing in Sustainable AI." Oracle, https://www.oracle.com/big-data/in-memory-computing-and-sustainable-ai.html

[6] "Nike's In-Memory Technology Enables Real-Time Statistics Sharing." TechCrunch, 12 May 2015, https://techcrunch.com/2015/05/12/nike-in-memory-technology-enables-real-time-statistics-sharing/

[7] "Avanza's In-Memory Computing Powers Real-Time Risk Assessment." Forbes, 24 July 2018, https://www.forbes.com/sites/forbestechcouncil/2018/07/24/avanza-in-memory-computing-powers-real-time-risk-assessment/?sh=6631c0a66382

[8] "Gartner Says In-Memory Database Management Systems Will Reach $1 Billion in Revenue in 2015." Gartner, 10 July 2014, https://www.gartner.com/en/newsroom/press-releases/2014-07-10-gartner-says-in-memory-database-management-systems-will-reach-1-billion-in-revenue-in-2015

Technology in data-and-cloud-computing, such as in-memory computing, is revolutionizing business operations and analytics. The market for in-memory computing is expected to grow significantly, driven by advancements in AI, IoT, edge computing, and blockchain.

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