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レポート概要
| 本調査資料では、世界のインメモリデータグリッド市場について調査し、イントロダクション、調査手法、エグゼクティブサマリー、市場動向、コンポーネント別（ソリューション、サービス）分析、展開種類別（オンプレミス型、クラウド型）分析、産業別（金融、IT・通信、小売、医療、輸送・物流）分析、地域別分析、競争状況、投資分析、市場の将来などの項目を掲載しています。 ・イントロダクション ・調査手法 ・エグゼクティブサマリー ・市場動向 ・世界のインメモリデータグリッド市場規模：コンポーネント別（ソリューション、サービス） ・世界のインメモリデータグリッド市場規模：展開種類別（オンプレミス型、クラウド型） ・世界のインメモリデータグリッド市場規模：産業別（金融、IT・通信、小売、医療、輸送・物流） ・世界のインメモリデータグリッド市場規模：地域別 ・競争状況 ・投資分析 ・市場の将来 |
The In Memory Data Grid Market attained a value of USD 1.82 billion in 2020 and is expected to reach USD 3.72 billion by 2026 and register a CAGR of over 12.81% over the forecast period 2021 – 2026. In Memory Data Grids are suited to handle Big Data’s “big-three V’s”: variability, velocity, and volume.
– With the emergence of technologies such as Big Data, cloud, mobile, and IoT, businesses need their applications to provide higher performance, flexibility, availability, reliability, and scalability than ever before.
– For instance, the amount of data generated is growing at a rapid pace. According to Seagate Technology PLC, the volume of data created worldwide is expected to increase to 47 zettabytes and 163 zettabytes in 2020 and 2025, respectively, from 12 zettabytes in 2015.
– Additionally, the rising usage of the cloud has supported the data generated across various verticals. With the rapid growth of the cloud, enterprises of all sizes and industries are producing more data than ever, even up to terabytes per second. These data provide insights with potential business value.
– However, this massive data growth is creating new obstacles that make it difficult for applications to meet such demands. Scaling the data tier creates both economic and technical challenges for organizations.
– With the implementation of Platform-as-a-Service (PaaS), cloud, and container-based infrastructures, these challenges have become even more complicated. Whether data is hosted in the cloud, or on-premise, in a distributed or centralized architecture, IT infrastructures are more complex than ever before. Organizations are in need of flexible applications that can be used in a variety of hybrid cloud environments.
– To meet the challenges of IT complexity and data growth, In-memory data grids deliver elasticity and flexibility to help organizations achieve the full benefits of PaaS and microservices architectures, while also helping applications run effectively in the cloud. Additionally, the In-memory data grid gives applications a scalable in-memory repository for rapidly changing application data.
– Additionally, with the COVID-19 pandemic outbreak, non-essential businesses have been shut. The need for computing across sectors has risen, thereby requiring seamless scaling of the data, messaging and application tiers, offloads compute, read, and write-intensive workloads from existing core infrastructure. Hence such trends are expected to create scope for the market.
Key Market Trends
Growing Need for Real Time Data Processing in BFSI Driving the Industry Growth
– Growing digitalization are compelling financial companies to build a lean, flexible, and efficient approach to cater to their customers. Financial institutions deal with critical information, which, if not properly processed, can have severe financial and ethical implications.
– Hence, financial organizations worldwide are looking for in-memory data grid solutions, which can process data in real-time and improve their business-critical applications.
– A company such as Grid Gain is one of the prominent providers of In-memory data grid. Leading banks depend on GridGain to help them offer an integrated omnichannel banking experience. By using GridGain, organizations have not only added speed and scale to digital channels. They have opened up previously siloed data for seamless sharing across channels and implemented in-process HTAP using real-time streaming analytics, machine, and deep learning to monitor and enhance the end-to-end banking experience proactively.
– Trading systems with high transaction rates are examples of environments best-suited for the in-memory data grid. It enables financial applications with real-time analytics due to faster data access.
– One of the prominent use cases for the In-memory data grid is running large-scale simulations such as “Monte Carlo simulations,” which help create a clearer picture of what might happen in the future by considering various factors. These kinds of simulations are commonly run in the financial services industry to better understand the risks that the firms face.
– The increasing speed of commerce and the growing sophistication and organization of the people intent on criminal activity creates a complicated problem for financial institutions.
– With a fast data solution such as IMDG, transactions can be analyzed in real-time for suspicious patterns, compared to historical purchase history, to detect fraud in real-time and to decide on the legitimacy of a transaction.
– Banks are witnessing a sharp rise in cases of internal and external fraud perpetrated during the COVID-19 outbreak. The COVID-19 outbreak rescue package has resulted in an increase in fraud, false claims, and other scams. Many of the systems that financial institutions and government agencies have in place cannot adequately verify the identity and claims of applicants.
North America is Expected to Hold Major Share
– The adoption of an in-memory data grid is rising in the region, primarily attributed to the burgeoning demand for faster processing and analytics on big data coupled with the need for simplifying architecture as the number of various data sources increases. Technology enhancements that are optimizing the total cost of ownership are one other factor driving the market growth.
– The growth of new business insights contributes to expanding the market in the United States, as various data sources increase. Various companies are leveraging big data to enhance marketing, customer experience, identify fraud and waste, and gain other results that can directly strengthen business performance.
– According to the US based Coalition Against Insurance Fraud, fraud accounts for 5-10% of claims costs for American and Canadian insurers. Some insurers expect the total as high as 20% of the claims’ costs. Across all insurance lines in the North America region, the estimated cost is between USD 80 billion and USD 90 billion.
– The healthcare industry, which embraces the cloud for their Electronic health record (EHR) data and other enterprise applications, is also becoming a great source for data. For instance, according to GNS Healthcare, a US-based Data Analytics Company, the United States healthcare industry generates an estimated 1.2 billion clinical care documents annually. Hence, growth in data across end-user industries is anticipated to create real-time processing, thereby creating opportunities for the market.
– With digitization producing an enormous amount of data, the leaders and laggards of the coming decades will be defined by measuring and solving data processing latency. The presence of a prominent player, which continues to see rapid adoption among Global 2000 organizations, including many of the world’s leading financial institutions, such as JPMorgan Chase, National Australia Bank, Lloyds Banking Group, UBS, and many more is contributing to the revenue generation in the region.
Competitive Landscape
The In-Memory Data Grid market is moderately fragmented consisting of various vendors such as GridGain, Hazelcast, Software AG, Oracle Corporation, and GigaSpaces Technologies Inc., among others. Vendors in the market are aiming to capitalize on opportunities by focusing on its applications in e-commerce, financial-instrument pricing in banks, and others. Vendors are adopting several organic and inorganic growth strategies, such as partnerships and collaborations, new product launches, and mergers and acquisitions, to strengthen their presence in the market. Some of the recent developments in the market are:
– June 2020 – Oracle Corporation released the core of their Coherence in-memory data grid (IMDG) product as free and open-source software. The open-source Coherence release provides a very familiar set of core IMDG features, including, Parallel querying and aggregation, Fault-tolerant automatic sharding, Caching, querying, aggregation, transactions, in-place processing, Persistence, Eventing, messaging, and streaming.
– October 2019 – IBM Corporation collaborated with Hazelcast to improve IBM Cloud Pak solutions with an enterprise-grade in-memory computing platform add-on that is purpose-built for applications such as improved customer experience, fraud detection, payment processing, and edge processing or IoT. The Hazelcast in-memory computing platform combines event stream processing with an IMDG.
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1 INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Increasing Need for Attaining Unprecedented Levels of Speed at Data Processing
4.2.2 Growth of Big Data
4.3 Market Challenges
4.3.1 Maintaining Data Security
4.4 Industry Attractiveness – Porter’s Five Forces Analysis
4.4.1 Threat of New Entrants
4.4.2 Bargaining Power of Buyers/Consumers
4.4.3 Bargaining Power of Suppliers
4.4.4 Threat of Substitute Products
4.4.5 Intensity of Competitive Rivalry
4.5 Assessment of the Impact of COVID-19 on the Market
5 MARKET SEGMENTATION
5.1 Component
5.1.1 Solution
5.1.2 Services
5.2 Deployment Type
5.2.1 On-premise
5.2.2 Cloud
5.3 End User Industry
5.3.1 BFSI
5.3.2 IT and Telecommunication
5.3.3 Retail
5.3.4 Healthcare
5.3.5 Transportation and Logistics
5.3.6 Other End User Industries
5.4 Geography
5.4.1 North America
5.4.2 Europe
5.4.3 Asia Pacific
5.4.4 Latin America
5.4.5 Middle East and Africa
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 Hazelcast Inc.
6.1.2 GridGain Systems Inc.
6.1.3 Oracle Corporation
6.1.4 IBM Corporation
6.1.5 Pivotal (VMware Inc.)
6.1.6 GigaSpaces Technologies Inc.
6.1.7 Software AG
6.1.8 ScaleOut Software
6.1.9 Alachisoft
6.1.10 TIBCO Software Inc.
7 INVESTMENT ANALYSIS
8 FUTURE OF THE MARKET