In-Memory Computing Market Size, Share, Growth Analysis, By Component(In-memory Data Management, In-memory Application Platform), By Organization Size(Small & Medium Business, Large Enterprise), By Vertical(BSFI, Transportation, Government, Retail), By Application(Risk Management & Fraud Detection, Sentiment Analysis, Geospatial/GIS Processing, Sales & Marketing Optimization), By Region - Industry Forecast 2024-2031

In-Memory Computing Market Drivers

• As the number of transactions increases in enterprises, the amount of data also increases. In verticals such as telecommunication, BFSI, online gaming, and entertainment, data generated per second is huge. Due to the increasing data volumes, organizations are facing huge problems in processing large amounts of information within a specific time frame. Therefore, the use of traditional disk-based data management systems and the manipulation of massive data becomes quite difficult. IMC- based solutions such as IMDG and in-memory computing platform built around big data can handle three Vs because of their speed and scalability. These solutions process big data with the type of latencies that produce actionable insights and machine-driven decision-making. Through In-Memory Computing, organizations store data in the main memory of servers; hence the time, which is wasted in input/output operations, can be utilized for data processing. Thus, In-Memory Computing helps speed up the process of analyzing all the structured and unstructured data. Organizations are creating, collecting, storing, and hoping to capitalize on structured and unstructured data, driving the overall IMC market growth. BFSI organizations can track the effect of market fluctuations on their portfolios (‘strategies’) of long and short equity positions in various markets, and immediately identify strategies requiring rebalancing through In-Memory Computing.

In-Memory Computing Market Restraints

• Lack of skilled workforce and complexity involved in data extraction as in-memory computing is a new standard in technology development. The availability of trained technical staff and consulting professionals is the major challenge of IMC implementation. Many organizations or technology adopters suffer typical challenges with radical technologies in early adoption phases. These challenges include the lack of general standards and absence of proven architectural patterns. With the emergence of successful vendors, increase in the network of implementation partners, and recent open-source momentum, skill availability challenges are expected to drop rapidly; however, for the interim period, In-Memory Computing continues to be a niche technology and skill availability continues to be a challenge. Due to reasons such as the shortage of skilled workforce and complexity of big data solutions, organizations report trouble in extracting value from structured, unstructured, or semi-structured data. The complete life cycle of data analytics in cloud goes through various processes, including data discovery, data modeling, data mining, and visualization. These processes make big data analytics-related projects quite complex. Such complexities faced by business organizations across verticals act as obstacles for IMC solution vendors in the Global In-memory computing Market. According to World Economic Forum’s Future of Jobs 20184, the demand is accelerating for a variety of wholly new specialist roles related to understanding and leveraging the latest emerging technologies. These specialist roles are big data specialists, AI and ML specialists, process automation experts, information security analysts, user experience and human-machine interaction designers, robotics engineers, and blockchain specialists.