LONDON--(BUSINESS WIRE)--Technavio analysts forecast the global in-memory computing market to grow at a CAGR of more than 33% during the forecast period, according to their latest report.
The research study covers the present scenario and growth prospects of the global in-memory computing market for 2016-2020. In order to calculate the market size, the report considers the revenue generated from installations, updates, and software licenses, along with technical support cost generated from in-memory computing solutions.
In-memory computing technology enables enterprises to achieve productivity through six characteristics: performance, simplification, innovation, single data source, involvement of low risks and costs, and integration.
Technavio hardware and semiconductor analysts highlight the following four factors that are contributing to the growth of the global in-memory computing market:
- Rapid data explosion in networks
- Demand for effective and easy data access
- Exponential growth in amount of data stored and used
- Need to improve market responsiveness
Rapid data explosion in networks
Enterprise data is generated through different sources including web applications, social networks, cloud computing, and mobile computing. With the increased adoption of cloud computing and cloud-based applications, there is a rapid growth in the digital content being transmitted across networks. In addition, consumer demand for always being connected through wireless broadband connectivity is growing significantly. This is leading to a large number of data transactions. “Organizations are focusing on delivering superior customer service in the retail sector, where consumers increasingly use devices such as smartphones. In order to deliver such services to customers in real time, a number of retailers are adopting in-memory computing technology,” says Sunil Kumar Singh, a lead analyst at Technavio for embedded systems research.
Smartphones accounted for 70%-75% of total mobile data traffic in 2015 while tablets accounted for 15%-20%. This data traffic is creating large opportunities for different sectors to adopt in-memory computing technology in order to analyze the vast available data and get productive output. The complex computations to be performed on large data blocks and dynamic storage and retrieval of data from the storage space are leading to a higher demand for in-memory computing. The big data must be easily accessible to end-users and also efficient computing must be performed to derive desired conclusions for better decision making. This, in turn, will drive the demand for in-memory computing solutions.
Demand for effective and easy data access
The demand for effortless data access among organizations is increasing. This, in turn, is fueling the demand for in-memory computing. In-memory computing processes a large amount of data in the main memory of the server within seconds and helps shorten the execution time of the batch process. In-memory computing enables fast computational speeds, ensuring most accurate results with high data access speed.
Traditional disk-based systems also have storage capabilities, but when it comes to faster analysis of data, in-memory computing has a high processing speed. Also, disk-based systems require a large physical space to store large amounts of data. In the current business environment, every organization, regardless of its size, stores a considerably large amount of data. This has further increased the demand for in-memory computing.
Exponential growth in the amount of data stored and used
Enterprise data is generated from different sources, including enterprise applications, web-based searches, social networks, and cloud-based applications. Currently, any US enterprise with more than 1,000 employees has, on an average, more than 200 terabytes of stored data. Organizations use big data analytics and solutions to keep track of the large volumes of big data generated and use it effectively. It is impossible to manually analyze such large volumes of data.
Thus, big data analytics helps enterprises optimize their business decisions and innovate new business models, products, and services offerings. Therefore, big data must be accessible as part of or as an extension to the corporate information framework and should be made available for analysis and decision making which further increases the demand for in-memory computing.
Need to improve market responsiveness
Businesses are exposed to intense competition. This has led to the need for enterprises to increase the speed of processes that support competitive strategies, which in turn, reduces the cycle time. In-memory computing provides real-time information that can enable organizations to avoid process delays and centralize management to improve market response and decision making.
In addition, to achieve competitive advantage, organizations are required to transform the data into actionable intelligence by constant improvement of customer-facing strategies. “Many organizations are using in-memory computing to manage different data types and large volumes of data while reducing IT complexity. Such benefits of in-memory computing are fostering its increased adoption,” says Sunil.
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