Massive amounts of unstructured data are used in production and
decision-making.
Knowledge mining from massive amounts of unstructured data can accelerate human genomics
research, improve weather forecasting accuracy, assist diagnosis and treatment, help
accurately locate oil and gas in energy exploration, and accelerate the R&D of autonomous
vehicles.
With the advancement and widespread use of data analytics technologies, mass unstructured
data is taking center stage in supporting enterprise production and decision-making. This in
turn requires data storage to deliver more elastic scalability, higher efficiency, and
better reliability.
On-premises data is distributed on different types of storage devices from different
vendors, resulting in numerous data silos that hinder data utilization. Cross-region data is
distributed in different data centers and edges, resulting in data isolation, which hinders
instant access and unified protection. In addition, more and more data is distributed in
both on-premises data centers and cloud infrastructure, making data mobility and
collaborative production difficult.
Complicated management is the primary challenge caused by exponential data growth. Storage
systems must be able to mask differences between heterogeneous devices, cross-region
devices, and different IT infrastructures to simplify data management and improve data
access efficiency.
Ransomware attacks have become the primary threat to enterprise data.
Recent years have witnessed more frequent ransomware attacks on business systems. According
to a survey, a ransomware attack occurred every 11 seconds in 2021, and the number of
ransomware variants increased by 98% from 2021 to 2022.
In terms of ransomware protection, the network layer can only intercept known viruses in
most cases. As the final carrier of data, storage systems are responsible for detecting
attacks, retaining important data copies, and promptly restoring services to build the last
line of defense for data security.
Multi-cloud deployment is the new normal.
With the maturity of cloud native technologies and markets, more and more enterprises adopt
multi-cloud IT construction to fully leverage the technical advantages of different cloud
platforms. However, this practice has caused cloud data silos, preventing data from being
shared between multi-cloud applications. Moreover, the services of different cloud platforms
vary, hindering ecosystem integration.
Connecting one storage system to multiple clouds enables centralized data sharing, on-demand
invoking, and multi-cloud application deployment, greatly reducing cross-cloud data
migration and resource wastage while improving data movement efficiency.
Application-based innovation brings greater benefits.
As innovative services become production services and are continuously developed and
segmented, there is an increasing gap between computing power and data lifecycles.
Therefore, computing power and storage resources need to be flexibly and independently
planned and maintained.
More and more enterprises use a decoupled compute-storage IT architecture and expect to
build a highly reliable, cost-effective, and efficient solution for segmented scenarios
through collaborative innovation of applications, computing, and storage.