Global “In Memory Data Grid Market” 2021 Research report produces information with reference to market size, share, trends, growth, cost structure, capacity, revenue, and forecast 2026. This report also contains the general and comprehensive study of the In Memory Data Grid market with all its aspects influencing the growth of the market. This report is a full-scale quantitative survey of the In Memory Data Grid industry and provides data for building strategies to expand the market growth and effectiveness.
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.
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Top Leading Companies of Global In Memory Data Grid Market are: Hazelcast Inc., GridGain Systems Inc., Oracle Corporation, IBM Corporation, Pivotal (VMware Inc.), GigaSpaces Technologies Inc., Software AG, ScaleOut Software, Alachisoft, TIBCO Software Inc. and others.
The competitive market players are identified along with their size, share, and strategies. The company profiles of these players, which includes their recent major developments, product portfolio, revenue, core competencies, and financials. Additionally, the report presents insights into the mergers & acquisitions, company profiles, financial status, product portfolio, and the SWOT analysis.
Industry News And Update:
– 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.
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.
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Important Features that are under offering and key highlights of the report:
– Detailed overview of the In Memory Data Grid market
– Changing market dynamics of the industry
– In-depth market segmentation by Type, Application, etc.
– Historical, current, and projected market size in terms of volume and value
– Recent industry trends and developments
– Competitive landscape of the In Memory Data Grid market
– Strategies of key players and product offerings
– Potential and niche segments/regions exhibiting promising growth
– A neutral perspective towards In Memory Data Grid market performance
– Must-have information for market players to sustain and enhance their market footprint
The research includes historic data from 2015 to 2021 and forecasts until 2026 which makes the report an invaluable resource for industry executives, marketing, sales, and product managers, consultants, analysts, and stakeholders looking for key industry data in readily accessible documents with clearly presented tables and graphs.
Finally, the In Memory Data Grid Market report is the believable source for gaining the market research that will exponentially accelerate your business. The report gives the principle locale, economic situations with the item value, benefit, limit, generation, supply, request and market development rate and figure and so on. In Memory Data Grid industry report additionally Present new task SWOT examination, speculation attainability investigation, and venture return investigation.
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