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  4. Infrastructure Predictions for 2024

Infrastructure Predictions for 2024

Enterprises see an AI adoption inflection point, ransomware threats collide with penalties, hybrid cloud architecture goes mainstream, infrastructure upgrades are key.

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Tom Smith user avatar
Tom Smith
DZone Core CORE ·
Dec. 11, 23 · Opinion
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As we enter 2024, the technology landscape continues to evolve at a rapid pace. While economic uncertainties persist, investments in digital transformation and emerging technologies like AI and cloud continue to accelerate.  

I connected with experts from Nasuni to get their take on some of the key tech predictions for the year ahead. Here are some of the pivotal trends and challenges they foresee.

AI Adoption Reaches Inflection Point Amid Data Disorder

According to Jim Liddle, Chief Innovation Officer at Nasuni, 2024 will be a "make-or-break year" for enterprises implementing AI. While interest boomed in 2023 on the promise of competitive advantage, systemic data issues threaten progress.

"Before they can integrate AI effectively, organizations will first have to address how they collect, store, and manage their unstructured data, particularly at the edge," Liddle explains. Getting AI right requires modernizing data infrastructure to support capabilities like visibility, governance, classification, and availability across hybrid environments.  

As pressure mounts from corporate boards, data management, and visibility will rapidly emerge as urgent priorities. Companies that transform their data platforms will power ahead with AI, while others find themselves stalled.

The Ransomware and Compliance Collision Course

Liddle also warns that the ransomware threat will continue evolving and spreading in 2024, expanding well beyond its US origins. Healthcare, government, critical infrastructure, and other under-protected industries with sensitive data will attract sophisticated global attacks. 

At the same time, expanding regulations like GDPR and CCPA carry strict data privacy penalties and mandate rapid breach reporting. "By delaying their investment in protection and compliance solutions until forced to, many large organizations will soon face the possibility of steep penalties, ransom demands, and business disruption simultaneously," Liddle says.

The Hybrid Cloud Revolution Accelerates

According to Russ Kennedy, Chief Product Officer at Nasuni, we'll see rapid adoption of hybrid cloud architectures in 2024 as leaders embrace the combination of edge and cloud capabilities. On-premises infrastructure captures and consolidates real-time data, while the cloud provides abundant storage, backups, disaster recovery, and advanced analytics.

Kennedy explains further: "To remain competitive in a data-driven world, enterprises need high-performance processing at the edge where data is generated, in combination with the scale, capacity, and advanced tools available in the cloud."

Upgrading Infrastructure To Support the New World of Work  

The pandemic-fueled shift to hybrid work left many IT teams scrambling to provide secure remote collaboration at scale. While initial measures kept businesses operating, workarounds caused shadow IT sprawl and eroded data oversight.

"Heading into 2024, establishing governance and upgrading data infrastructure remain urgent priorities to support the new world of work," Kennedy says. The organizations that implement solutions to streamline remote work processes and data sharing will thrive; those that don't will struggle to attract top talent.

In summary, 2024 promises economic opportunities coupled with intensifying threats. Navigating these turbulent times requires organizations to become data-centric, cloud-enabled, and AI-powered in order to out-innovate and out-secure the competition. 

What potential pivots and pitfalls do you see on the road ahead?

AI Machine learning

Opinions expressed by DZone contributors are their own.

Related

  • The Only AI Test That Still Humbles Every Machine on Earth
  • Architecting AI-Native Cloud Platforms: Signals to Insights to Actions
  • Beyond Accuracy: Measuring Divergence Between Actual and Predicted Distributions in Machine Learning
  • AI-Based Multi-Cloud Cost and Resource Optimization

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