• 出版社/出版日：Mordor Intelligence / 2021年1月
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The In-Memory Analytics Market was valued at USD 1.51 billion in 2020 and is expected to reach USD 5.80 billion by 2026, at a CAGR of 25.1% over the forecast period 2021 – 2026. New persistent memory technologies will help reduce the costs and complexity of adopting BMI-enabled architectures (in-memory computing), which is coming in trend nowadays. Persistent memory represents a new layer of memory between the DRAM and NAND flash memory, which can provide economical mass memory for high-performance workloads. This option has the potential to improve application performance, availability, boot times, load methods, and security practices while keeping costs under control.
– Digital transformation of end-users leading to the adoption of real-time analytics is driving the market as technology trends such as hyper-connectivity, cloud computing, and big data are going hand-in-hand with social and business trends. It will enable enterprises to start implementing hybrid transactional/analytical processing (HTAP) strategies, which have the potential to revolutionize data processing by providing real-time insights into big data sets while simultaneously driving down the costs.
– Growing data volume demanding analytical methods is driving the market. 90% of the world’s data has been created in the last two years alone. According to a recent study, 79% of enterprise executives agree on the point that companies that do not embrace big data will lose their competitive position and could face extinction.
– Lack of awareness about the product and higher penetration of conventional analytics tools is restraining the market growth. In-memory may not immediately produce the results; one should desire simply by swapping out technologies and architecture. It requires skills and expertise to manage what’s going on, which is profoundly lacking.
Key Market Trends
Manufacturing Sector to Drive the Market Growth
– Manufacturing sector is expected to witness significant growth for the in-memory analytics market. Many manufacturing organizations are looking for ways to improve their quality of manufacturing while reducing support costs by improving defect tracking and improving forecasting abilities to optimize supply chains, thus leading to overall operational efficiency improvements. Usage of in-memory-analytics is increasing for the faster supply chain management.
– Data warehouse should have a good query and reporting performance. One of the premises of an in-memory database like SAP HANA is that one necessarily does not need to copy the transactional data to a separate data warehouse. One can create analytical or calculation views over the operational, transactional tables to provide a dimensional view through which the data can be reported and analyzed.
– Capturing real-time change data through in-memory big data analytics from enterprise databases and integrating with machine and sensor data for a comprehensive view of operations is increasing the productivity in the manufacturing sector. It analyzes data-in-motion to respond to time-sensitive operational events such as changes in traffic or equipment conditions.
– Manufacturing remains an essential sector of the modern British economy, and the UK is one of the most attractive countries in the world for direct foreign industrial investment. Advanced supply chain analytics is increasing their productivity, especially in airplanes and automobile sector. Hadoop-based sandbox environment and use in-memory processing to quickly explore unknown data relationships and creating analytical models with refining them continuously is exploring the market.
Asia-Pacific to Witness Significant Growth
– The in-memory analytics market in Asia-Pacific region is driven by the growing digitization of end-users coupled with the rising adoption of cost-effective cloud-based analytical software by the SMBs (Small and Medium Business) especially in China and India.
– Countries such as China, India, and Japan act as the hub for enterprises such as BPOs and KPOs, and hence are known as manufacturing factories of the world. The very basic foundation of such organizations is the huge quantities of data that need to be stored, analyzed, and then used for decision-making. This drive the demand for the in-analytics market.
– Wipro is cited as a leader in business intelligence services in Asia. They work with customers to develop end-to-end analytics and information strategy and has made investments in emerging technology areas like visualization, in-memory analytics, etc., which will significantly increase the market growth in this region.
– ThoughtSpot announced the expansion of its engineering operations in India. The company has invested USD 10 million in R&D to accelerate cloud development, specifically for in-memory cloud development. Apart from this, the Indian government is using big data for various purposes, such as to get an estimate of trade in the country, urbanization analysis, and unreserved railway passengers analysis. To maintain its edge and sustain its growth, China’s economy may also shift to a higher value and more advanced industries, with big data as one of the instrument to facilitate this shift.
The in-memory analytics market is fragmented as several key players along with the new entrants form a competitive landscape thereby accounting for a substantial market share. Also, strategic partnerships, acquisitions and new launch of product/technology are increasing a high rivalry in the market. Key players are SAP SE, IBM Corporation, SAS Institute, Inc., etc.
– April 2019 – SAP SE announced enhancements to the SAP HANA database in the cloud and on-premise that provide everyone instant access to critical data and extreme performance to democratize in-memory computing. Updates include enhanced cloud support, persistent memory support with Intel, intelligent recommendations for efficient database management, new machine learning (ML) capabilities, hyper-converged infrastructure (HCI) certification, improved cost-effective data tiering and data security enhancements.
– March 2019 – SAP HANA has been to create real-time, in-memory analytics for a large trucking operation with fleets in the United States., Canada, and, most recently the UK. Using data compression, real-time information is stored in RAM, and fleet managers can access this data within minutes.
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1.1 Study Deliverables
1.2 Study Assumptions
1.3 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Introduction to Market Drivers and Restraints
4.3 Market Drivers
4.3.1 Digital Transformation of End-users Leading to Adoption of Real-Time Analytics
4.3.2 Growing Data Volume Demanding Swift Analytical Methods
4.3.3 Advancements in Computational Technology
4.4 Market Restraints
4.4.1 Lack of Awareness in End-users
4.5 Industry Value Chain Analysis
4.6 Industry Attractiveness – Porter’s Five Force Analysis
4.6.1 Threat of New Entrants
4.6.2 Bargaining Power of Buyers/Consumers
4.6.3 Bargaining Power of Suppliers
4.6.4 Threat of Substitute Products
4.6.5 Intensity of Competitive Rivalry
5 MARKET SEGMENTATION
5.1 By Deployment
5.2 By End-user Industry
5.2.3 IT and Telecommunications
5.2.5 Government and Public Sector
5.2.6 Other End-user Industries
5.3.1 North America
5.3.4 Latin America
5.3.5 Middle East & Africa
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 SAP SE
6.1.2 IBM Corporation
6.1.3 Oracle Corporation
6.1.5 Amazon Web Services, Inc.
6.1.6 Information Builders, Inc.
6.1.7 Kognitio Ltd
6.1.8 Microstrategy Incorporated
6.1.9 SAS Institute, Inc.
6.1.10 Software AG
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS