Global AI-Powered Storage Market is expected to grow from USD 10.71 Billion in 2018 to USD 25.05 Billion by 2025, at a CAGR of 17.56%, during the forecast period.
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The difference between structured and unstructured data creates a varied set of requirements on the storage system underlying, both in terms of size of the data and the number of files in the dataset. Hence, AI-powered storage systems are converged networks, which provide a platform for the storage of a massive amount of data generated from various data sources. AI workload is very different from generic workloads. AI and machine learning workloads require vast amounts of storage to extract translate and load input data, do exploratory data analysis to see what data is relevant, create test data, build and train the models, retrain the models to reflect changing data patterns, inferences, and discoveries and keep them running. Data does not lose value, so data must be retained for as long as there is perceived value, and regulatory requirements like GDPR may require retention of data in a form that explains the decisions made by AI. Data protection and data dispersion will also create copies of data. This generates orders of magnitude more data than the statistical analysis of a big data repository. Scalability not only requires the ability to scale capacities into the tens of petabytes but also scale connectivity to thousands of servers. Scalability includes the ability to minimize the footprint and cost of storage through intelligent use of deduplication, compression, virtualization, tiering, archiving, indexing cataloging, and shredding.
The efficacies of AI infrastructure generally focus on computing hardware like the general-purpose CPUs, GPUs, FPGAs, and tensor processing units. In general, these are accountable for training complex algorithms and making forecasts based on those models. Nonetheless, AI further demands a lot from data storage. It is astute to keep a potent compute engine well-utilized, which needs feeding it with vast amounts of information as fast as possible. If the requirements remain unfulfilled, it could lead to clogging of the works and generating bottlenecks. The demands of AI in enterprise storage infrastructure have become more demanding. It is no longer enough to deploy capacity and connect faster drives or buy an all-flash array and assume that flash resolves all consumer problems. Additionally, optimizing an AI solution for size and cost, while scaling for growth, means taking a renewed look at its data pipeline. It also denotes the AI readiness of an organization. An AI application is not one type of processing, like transaction processing in a DBMS application. There are numerous phases with different processing requirements, from model deployment, data ingestion, data discovery and visualization, data engineering, model development and training, and retention, all with various performance, tiering, and protocol requirements. Intelligent storage systems that employ AI are needed to constantly learn and adapts to its infrastructure environment to manage better and serve data.
The rising adoption of technologies such as artificial intelligence, machine learning, and data analytics has led to an increase in data that need high storage capacity. The enterprises are registering a need for a refreshed AI-based storage architecture. Factors such as the growing implementation of cloud-based services, a rise in the number of hyperscale data centers, and the deployment of data analytics tools across small and medium enterprises have positively impacted the demand for AI-powered storage. However, the concerns regarding data privacy are estimated to impede market growth. As many companies have started utilizing cloud-based services to store their data with the usage of virtual servers, companies aim to enlarge their storage capacities beyond existing infrastructural capabilities to meet the demand for AI-enabled applications and workloads. In the finance sector, decisions about loans are now being made by software that can take into account a variety of finely construed data about a borrower, rather than merely a credit score and a background check. In addition, there are also robot-advisors that create personalized investment portfolios. The BFSI sector is incorporating advanced analytics and, simultaneously, growing compliances and government regulations, thereby raising the demand for high-level data storage capabilities and leveraging the AI-powered storage market.
Segmentation
On the basis of offerings, the market has been segmented into hardware and software. The hardware segment has been further divided into SSD, HDD, CPU, and GPU. The hardware segment dominated the market in 2018; it is expected to register the highest CAGR during the forecast period from 2019 to 2025.
On the basis of storage system, the market has been segmented into direct-attached storage systems, network-attached storage systems, and storage area network. The storage area network segment dominated the market in 2018, whereas the network-attached storage systems segment is expected to register the highest CAGR during the forecast period from 2019 to 2025.
Key Players
The prominent players in the global AI-powered storage industry are Dell Technologies (US), Advanced Micro Devices (US), CISCO (US), HPE Company (US), IBM (US), Toshiba (Japan), Intel Corporation (US), Pure Storage (US), Hitachi (Japan), NVIDIA Corporation (US), Lenovo (China), Samsung Electronics (South Korea), Micron Technology (US), Western Digital (US), and NetApp (US), Datadirect Network (US), Seagate Technology (US), Flextronics International (Singapore), Fujitsu (Japan), and Tintri, Inc. (US).
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