The In Memory Data Grid Market is expected to register a CAGR of over 11% over the forecast period 2022 – 2031. 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.
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– 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 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.
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North America is Expected to Hold Major Share
– North America is expected to hold a major share due to the growing adoption of data grid solutions across industries such as BFSI, healthcare, and retail. The region has a strong foothold of key vendors, which contributes to the growth of the market.
– The growth of new business insights contributes to the expansion of the market in the United States, as various data sources increase. Many companies are leveraging big data to improve customer experience, enhance marketing, identify fraud and waste, and achieve other results that directly strengthen business performance.
– According to the U.S based Coalition Against Insurance Fraud, fraud accounts for 5-10% of claims costs for American and Canadian insurers, with some insurers expecting the total as high as 20% of the claims costs. Across all lines of insurance in the North America region, the estimated cost is between USD 80 billion and USD 90 billion.
– The healthcare industry, which is embracing the cloud for their Electronic health record(HER) data and other enterprise applications, is also becoming a great source for data. For instance, The United States healthcare industry produces an estimated 1.2 billion clinical care documents annually, according to GNS Healthcare, a US-based Data Analytics Company. Hence, growth in data across end-user industries is expected to create the need for real-time processing, thereby creating opportunities for the market.
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.
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