As the volume of data maintained by enterprises continues to expand, increasing storage costs have compelled large-scale adoption of cloud-based solutions.
Cloud-based solutions provide the benefit of low entry fees. However, as costs continue to rise, more firms shift online. According to a Pepperdata study, more than a third of enterprises experience cloud service budget overruns of up to 40% — IT professionals seek alternatives.
As the cloud becomes an enterprise imperative, AI can streamline data lifecycle management by assessing data’s criticality, performance, security, and cost requirements.
Exploring the Advantages of AI-enabled Storage
With data serving as the new fuel for Artificial Intelligence, it follows that legacy data storage systems must be transformed into smart storage solutions. This necessitates the integration of deep learning and GPU processors, which enables them to process real-time insights and capitalize on the following advantages of AI-enabled storage:
Scalability: AI algorithms can process massive amounts of data in real-time, necessitating the use of significant amounts of storage. By provisioning AI-enabled cloud storage, storage can intelligently scale up or down in response to business requirements.
Shared Data vs. Stored Data: Shared data proved to be more valuable than stored data in a data-driven world. Advanced analytics and AI-processed workloads enable AI-enabled storage to scale-out storage platforms that resolve downtime issues through data-driven insights.
Data Insights: AI-powered storage leverages a variety of analytical tools to quickly and reliably extract insights from billions of stored objects and files. This increases the value of data for major business decisions of the organization.
Automation: Using Software-Defined Storage in conjunction with AI and machine learning, the storage ecosystem can now add software layers on top of the hardware and track additional data. This enables managers to concentrate their efforts on strategic initiatives. Additionally, storage facility automation can be powered by AI. Their flexible and adaptable design makes it simple to manage, access, and reroute data-center traffic to regulate data-center temperatures automatically.
Compression and on-premises
AI is becoming more prevalent in file compression. AI-based compression can achieve the same — or almost identical — visual quality for films, audio, and photos while using fewer bits. Moreover, it is easier to upgrade, standardize, and deploy new AI codecs than it is with standard codecs because the models can be trained in a brief period of time and — most crucially — do not require special-purpose hardware.
Perspectives on the future
The use of AI solutions for data management, which are classified as AIops, continues to be fairly low. AIops platforms seek to improve IT by utilizing AI to examine data collected from an organization’s tools and devices. The pandemic accelerated adoption as businesses seek to automate faster to respond to “rapidly changing” situations. Today’s industry is rife with ‘AI washing.’ As a result, verifying vendor claims and installing an ROI-generating system can be tedious. AIops necessitates extensive integration. A robust AIops deployment may become a pipe dream for teams that lack expertise in sustaining large data environments.
Additionally, a cultural shift is required, where organizations become willing to make data-driven decisions. Industry analysts anticipate that more “versatile” AI-powered storage management solutions will become available beyond the currently available offerings. Through the application of AI, these technologies may enable more intelligent automation and remediation operations.
Artificial intelligence techniques can assist in optimizing data placement on the appropriate storage tiers – balancing performance and cost. Additionally, AI can aid in increasing the availability of data infrastructure, allowing organizations to access data more quickly and build a more reliable infrastructure.