10 AI ML In Data Storage Trends To Look Out For In 2024

Data Is Getting The Fuel that Powers Your AI Journey

Watching Netflix, searching Google, booking an Uber, shutting off the lights with Alexa, unlocking your phone, and even finding the appropriate shade of cosmetics – these are just a few instances of how we engage with artificial intelligence and machine learning (AI/ML) every single day.

60% of all business data is kept in the cloud. The cloud storage is estimated to exceed 100 zettabytes. 54.62% of consumers utilize 3 distinct cloud storage providers. Google Drive has nearly 1 billion users, whereas Dropbox has over 700 million claimed users.

AI and ML boil down to recognizing patterns. Real-time pattern recognition offers vast potential to enhance operational efficiency, company results, and individual lives. IDC projects that the worldwide AI industry, comprising software, hardware, and services, will approach the $900 billion mark in 2026, with a compound annual growth rate (CAGR) of 18.6 percent in the 2022-2026 timeframe.

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Artificial intelligence (AI) allows machines to mimic human thought processes. Companies across industries are racing to harness this quality to advance their products and services and remain competitive. Any failing business may be saved via the strategic use of AI and the exploitation of the insights it provides.

The data is the engine that drives AI. The danger is that it will become stuck or preserved in a state that makes it difficult or expensive to use, maintain, or expand.

Artificial intelligence is limited by the quality of the data it is fed. Companies need to know the full scope of their data production, the value of that data, how to eliminate unwanted data, and how long that data will persist. Organizations must also have the means to govern, catalog, optimize, and audit their data for compliance purposes. Each of these is a major roadblock.

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By 2028, the global market for AI-powered storage is projected to rise to $66.5 billion, expanding at a compound annual growth rate (CAGR) of 24.5% between 2018 and 2028.

This is when having access to an AI-powered storage solution comes in handy. AI-enabled storage optimizes data and conducts other clever automated functions without requiring human input, providing continuous real-time updates from a wide range of business data sources.

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Companies Profiled

• IBM Corporation
• Microsoft Corporation
• Amazon Web Services, Inc. (Amazon.com, Inc.)
• Google LLC (Alphabet Inc.)
• Cisco Systems, Inc.
• Hewlett-Packard Enterprise Company
• Intel Corporation
• Dell Technologies, Inc.
• Lenovo Group Limited
• NetApp, Inc.

Expert Insights

Exclusively by Reggie Jerath, CEO of Hydro.

We anticipate unprecedented strides in natural language processing in 2024, allowing for more innate interactions with technology.  Real-time decision-making will be enabled by the convergence of edge computing and AI, revolutionizing entire industries.  Explainable AI (XAI) will become more popular, improving AI system transparency and confidence.

By pushing the limits of computational efficiency, quantum machine learning will open up new avenues. AI’s ethical implications will become more prominent, necessitating strict rules and guidelines.

In short, 2024 will be a turning point in the evolution of AI, paving the way for a day when smart technologies are a seamless part of our everyday lives.

How does AI-based Storage Management work?  

 

Artificial intelligence (AI) and machine learning (ML) applications require data to function properly. Training simulations and models with massive quantities of data helps in decision-making and enhances the accuracy and utility of any insights.

To function, AI systems need not just massive volumes of data, but also access to that data via fast, reliable, and scalable storage choices. As a result, data storage especially intended for machine learning and AI applications was invented.

For such storage to be an efficient part of AI workloads, it must include a few essential components:

• Quick connection to the outermost region of the cloud:

Artificial intelligence relies on a complex cloud architecture. Everything from files to programs to data-gathering mechanisms may be hosted remotely on the cloud. AI storage will allow for instantaneous access to data throughout the whole cloud, regardless of where the data now resides or is headed.

• Automation:

A variety of critical operations, including the dynamic management of file and resource rights, identity and access control configurations, routing and load balancing strategies, and processing directives, will need to be carried out by storage allocated for AI workloads. AI storage will automate these processes to boost the system’s efficiency. It would make sense for AI to be the driving force behind this automation.

• Artificial intelligence (AI) storage requires scalability and performance

Period. Storage for AI workloads must be flexible enough to meet shifting needs without slowing them down. Although adequate storage might alleviate scaling or performance issues, in these systems, storage often becomes the limiting factor.

• Complexity

Due to the complexity and scope of AI applications, mismanagement of resources can lead to spiraling costs. AI storage should provide some degree of cost-effectiveness, either through bulk storage management or adequate scalability, to prevent acquiring and maintaining resources that aren’t necessary.

AI storage relies on several factors, not the least of which are effective storage management, cost management, and scalability.

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10 AI ML in Data Storage Trends to look out for in 2024

 AI and ML are increasingly integrated into data storage and management solutions to optimize data processing, enhance efficiency, and improve data security. While it’s challenging to predict specific trends for 2024, here are some areas in AI and ML within data storage to watch for: 

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1. Intelligent Data Tiering: AI and ML will be used to automatically classify and tier data based on its value and access patterns. This ensures that frequently used data is stored in high-performance storage tiers, while less frequently accessed data is moved to cost-effective, lower-tier storage. 

2. Predictive Storage Analytics: AI-driven analytics will offer predictive insights into storage capacity, performance, and potential issues, enabling proactive maintenance and optimization. In an enterprise data center, predictive storage analytics uses machine learning to analyze historical performance data, forecast future storage needs, and automate alerts for potential issues. This enables proactive resource allocation, optimizing storage efficiency, reducing downtime, and achieving cost savings through informed capacity planning. 

3. Data Compression and deduplication: ML algorithms will be used to improve data compression and deduplication techniques, reducing storage costs and optimizing data transfer speeds. Compressing data further eliminates unnecessary information inside each data block, whereas deduplication eliminates superfluous data blocks. When combined, these methods drastically cut down on data storage space needs. Want to know about the two main ways that data may be compressed? Both lossy and lossless compression techniques exist. By erasing some of the original material permanently, lossy compressed files. By deleting extraneous information, lossless compression makes files smaller. IS GETTING

4. Smart Data Archiving: AI systems will assist in identifying which data should be archived, when it should be archived, and where it should be stored, improving long-term data management and retrieval. The acronym “AIOps” (artificial intelligence for IT operations) is what Gartner is calling this. From a low of less than 10% in 2020, Gartner projects that by 2025, 40% of all infrastructure product installations, including storage and hyper-converged systems, will be AIOps-enabled. By analyzing capacity and performance in advance, forecasting difficulties that can interrupt data services, and offering practical suggestions for fixing Level 1 concerns, the new tools improve storage utilization efficiency.

5. Data Security and Privacy: AI and ML will enhance data security by identifying and mitigating security threats and ensuring compliance with data privacy regulations. Anomaly detection will play a significant role in identifying unauthorized access or data breaches. The dramatic growth in product capabilities and pent-up demand for more stringent data security measures will cause 30% of organizations to have used bDSP by 2025, up from 10% in 2021.

6. Data Governance: AI will assist in creating and enforcing data governance policies, ensuring that data is appropriately classified, tagged, and handled throughout its lifecycle. Potentially supported areas for expansion include capacity-based pricing, more granular control of the many types of flash storage available, and increased support for hybrid settings. You can already factor in data egress and influx costs using tools like Virtana and vSAN.

7. Storage Resource Allocation: ML algorithms will continuously monitor and adjust storage resources, ensuring that data storage is optimally allocated based on evolving usage patterns. Information on the storage infrastructure’s availability, capacity, and performance, as well as management of devices, problem identification, configuration planning, and change management, is provided by storage resource management (SRM) software in near real-time and historical form. With the data provided by SRM software, applications, business units, or users may monitor storage use, availability, and performance. This information can then be utilized for IT consumption tracking and chargeback in both homogeneous and heterogeneous contexts.

8. Data Recovery and Backup: AI and ML will improve data recovery processes by identifying critical data and enabling faster, more efficient backups and restoration. Data recovery in storage is evolving with trends like cloud-based solutions for scalability, AI-powered automation for faster and more accurate recovery, and emphasis on ransomware protection. Immutable storage prevents data loss, while hybrid and multi-cloud recovery ensures flexibility. Faster recovery times, endpoint protection, and compliance integration further enhance the efficiency and reliability of data recovery solutions in today’s dynamic storage environments.

9. Storage Optimization for Edge Computing: With the growth of edge computing, AI and ML will play a role in optimizing data storage at edge locations, ensuring efficient use of limited resources. In edge computing, storage optimization is critical for efficient data processing. By employing predictive analytics, edge devices can anticipate local storage demands, ensuring timely and relevant data access. This minimizes latency, optimizes resource usage, and enhances overall system performance. Automated algorithms adjust storage dynamically, allowing edge environments to adapt to varying workloads, improving responsiveness, and enabling more streamlined and effective edge computing operations.

Cognitive search and content management transform data storage by employing AI-driven insights. Using natural language processing and machine learning, these systems enhance search accuracy, extracting meaningful information from unstructured data. They automate content categorization, metadata tagging, and intelligent indexing, enabling streamlined access to relevant information. This improves data discovery, organization, and retrieval, empowering organizations to harness the full potential of their stored data.

Organizations need to monitor these trends and adopt AI and ML solutions that align with their data storage needs. As data continues to grow in volume and complexity, leveraging these technologies is essential to maintain efficient and secure data management practices.

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If you employ storage analytics, how can AI change that?

• Automating the process of spotting and fixing possible hardware problems and compliance concerns can help you save time and boost performance.
• Accurately forecast future needs by analyzing data-generation rates in the present and the past, therefore decreasing operational costs and freeing up IT resources for more creative, strategic endeavors.
• Data migration to different on-premises or cloud-based storage tiers may be automated from day one with the help of predictive storage analytics.

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Future of Data Storage with AI

The ability of human engineers to manage, monitor, and maintain such massive data storage may be jeopardized in the future as AI increases the size of storage systems. Artificial intelligence-enabled data storage solves this scalability and efficiency problem. While today’s storage systems and apps can collect massive amounts of data and turn it into useful insights, progress is hampered without the intelligence and automation of AI-powered storage solutions.

Technology advancements in artificial intelligence and machine learning promise to have far-reaching effects on businesses. Edge artificial intelligence, decision intelligence, and deep learning are just a few examples of the technologies that the Gartner hype cycle predicts will see widespread use over the next two to five years. The ability of businesses to fully realize the promise of AI/ML applications will be greatly influenced by the storage infrastructure they decide to use as they begin their separate journeys to deploy this formidable new method.

Organizations can improve their decision-making and can automate routine daily tasks and choices. Workers now have more time to devote to “human” pursuits like creativity, teamwork, and interpersonal interaction. To provide unique experiences for customers and employees, and to allow the development of cutting-edge new applications that aren’t readily available today, a data-driven culture is essential. 

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