Skip to content
Tiatra, LLCTiatra, LLC
Tiatra, LLC
Information Technology Solutions for Washington, DC Government Agencies
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact
 
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact

AI meets file data storage: How genAI may solve its own data growth crisis

Ben Franklin famously said that there’s only two things certain in life — death and taxes — but were he a CIO, he likely would have added a third certainty: data growth. 

File data is not immune. The general rule of thumb is that file data will double every two to three years, and that kind of exponential growth makes affordably storing, managing, and providing access to file data extremely challenging.

The problem grew even more acute for CIOs in November 2022, when OpenAI released ChatGPT. Suddenly, every board of directors charged their IT department with deploying generative AI (genAI) as quickly as possible. Unfortunately, genAI requires immense amounts of data for training, so making that ever-growing mass of file data accessible became an even more urgent priority.

Intelligent tiering

Tiering has long been a strategy CIOs have employed to gain some control over storage costs. Chris Selland, partner at TechCXO, succinctly explains how tiering works: “Implementing a tiered storage strategy, leveraging cloud object storage for less frequently accessed data while keeping hot data on high-performance systems, allows organizations to scale cost-effectively while maintaining quick access where it’s most needed.”

But, he says, there’s more to tiering than that for a modern enterprise. “Where possible, implement analytics platforms that can work directly with data in cloud data stores, eliminating the need to move large datasets, and implement data cataloging tools to help users quickly discover and access the data they need. In some cases, you may also need to implement edge computing and federated learning to help process data closer to the source, where data is either not practical or possible to centralize.”

Finally, Selland said, “invest in data governance and quality initiatives to ensure data is clean, well-organized, and properly tagged – which makes it much easier to find and utilize relevant data for analytics and AI applications.”

A tiered model provides the enterprise with advantages as IT moves to implement AI, said Tom Allen, founder of the AI Journal. “Hybrid cloud solutions allow less frequently accessed data to be stored cost-effectively while critical data remains on high-performance storage for immediate access. Using a retail or high-volume e-commerce company as an example, they can use aspects or adapt this strategy to accelerate its data processing for AI models. This will likely show improvements in real-time insights without compromising storage costs.”

Enabling automation with AI

Of course, implementing data tiering is much easier said than accomplished. With so much data already on hand – and much, much more of it being created every minute – manually tagging data for tiering is not feasible. Automation is the key, said Peter Nichol, data & analytics leader for North America at Nestlé Health Science.

“Companies use machine learning and automation to dynamically move data between data tiers (hot, cool, archive) based on usage patterns and business priorities,” Nichol said. “This technique optimizes storage costs while keeping high-value, frequently accessed data accessible.”

AI can also be applied to make it easier to access the data users are looking for, said Patrick Jean, chief product & technology officer at ABBYY. But it needs to be the right combination of different types of AI to ensure accuracy. 

“Organizations’ data are growing exponentially, posing a challenge for decision makers that need quick access to the right insights for making smarter business decisions,” Jean explained. “They’re wanting to use AI to gain faster access to the documents that are fueling their business systems without risking hallucinations or sacrificing accuracy, which is of particular concern with generative AI only solutions. In a recent survey, decision makers say they put more trust in AI that is purpose-built for their organization, documents, and industry. This approach using the best combination of generative AI and symbolic AI delivers significant ROI that gets goods to market faster and improves operational efficiencies in accounts payable or transportation and logistics.”

The future of data storage and generative AI

But as AI has become more advanced, so have the possibilities for employing it to manage ad access rapidly growing file data volumes. “One approach companies are exploring,” Nichol said, “is AI-powered caching and pre-fetching. The technology works by caching frequently accessed data. AI models help predict which data will be needed next, and the AI engine pre-fetches that data. This reduces latency for workloads and analytics, improving the user’s perception of speed.”

Gene de Libero, principal at the marketing technology consultancy Digital Mindshare LLC, said that his firm has had great success reducing data retrieval times with AI. “Since leveraging AI to optimize data storage (specifically data compression and de-duping),” de Libero said, “we’ve improved operational efficiency by 25%. Now, things run much smoother. We manage data growth with a unified, scalable storage platform across on-premises and cloud environments, balancing performance and cost.”

And looking ahead, there’s promise for integrating large language models, small language models and retrieval augmented generation (RAG) with different tiers of storage to further reduce file data costs, increase the accuracy of genAI and improve retrieval performance.

“Enterprises are deploying private gen AI capabilities by integrating large language models (LLMs) with their proprietary data, including unstructured data in file systems,” said Isaac Sacolick, president of StarCIO and author of Digital Trailblazer. “Instead of files that end-users access occasionally as needed, data in file systems that are integrated with retrieval augmented generation (RAG) and small language models are now key to the accuracy of genAI responses and critical decision-making. Chief data officers and infrastructure leaders should review the performance and utilization of data across their file systems and seek faster all-flash solutions for frequently used file data, while more economical infrastructure NAS solutions may be a lower-cost option for long-term and less frequently accessed data with long retention requirements.”  

So, as we move deeper into the 21st century, it appears that, as CIOs search for a way to efficiently store, manage and provide rapid access to file data — in part to lay the foundation for genAI — the solution will likely, itself, involve various types of AI, including genAI. 

NetApp has long been a leader in providing intelligent data infrastructure solutions that combine unified data storage, integrated data services and CloudOps solutions. Learn more about how your organization can tackle the problem of exponential data growth for genAI.


Read More from This Article: AI meets file data storage: How genAI may solve its own data growth crisis
Source: News

Category: NewsFebruary 4, 2025
Tags: art

Post navigation

PreviousPrevious post:Goodbye digital transformation, hello AI-first business transformationNextNext post:Three ways VMware customers can buy time and extend value

Related posts

Barb Wixom and MIT CISR on managing data like a product
May 30, 2025
Avery Dennison takes culture-first approach to AI transformation
May 30, 2025
The agentic AI assist Stanford University cancer care staff needed
May 30, 2025
Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
May 30, 2025
“AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
May 30, 2025
“ROI는 어디에?” AI 도입을 재고하게 만드는 실패 사례
May 30, 2025
Recent Posts
  • Barb Wixom and MIT CISR on managing data like a product
  • Avery Dennison takes culture-first approach to AI transformation
  • The agentic AI assist Stanford University cancer care staff needed
  • Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
  • “AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
Recent Comments
    Archives
    • May 2025
    • April 2025
    • March 2025
    • February 2025
    • January 2025
    • December 2024
    • November 2024
    • October 2024
    • September 2024
    • August 2024
    • July 2024
    • June 2024
    • May 2024
    • April 2024
    • March 2024
    • February 2024
    • January 2024
    • December 2023
    • November 2023
    • October 2023
    • September 2023
    • August 2023
    • July 2023
    • June 2023
    • May 2023
    • April 2023
    • March 2023
    • February 2023
    • January 2023
    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    Categories
    • News
    Meta
    • Log in
    • Entries feed
    • Comments feed
    • WordPress.org
    Tiatra LLC.

    Tiatra, LLC, based in the Washington, DC metropolitan area, proudly serves federal government agencies, organizations that work with the government and other commercial businesses and organizations. Tiatra specializes in a broad range of information technology (IT) development and management services incorporating solid engineering, attention to client needs, and meeting or exceeding any security parameters required. Our small yet innovative company is structured with a full complement of the necessary technical experts, working with hands-on management, to provide a high level of service and competitive pricing for your systems and engineering requirements.

    Find us on:

    FacebookTwitterLinkedin

    Submitclear

    Tiatra, LLC
    Copyright 2016. All rights reserved.