Industry experts share how fragmented, legacy data environments hinder transformation and why an intelligent data infrastructure has become foundational.
Cloud adoption and AI innovation depend on clean, connected, and trusted data. But many enterprises are still running on outdated data infrastructure that can’t keep pace with modern workloads nor modern threats. The result is fragmentation, inconsistent controls, and blind spots that weaken cloud performance and compromise AI outcomes.
To explore how legacy systems create risk—and what leaders can do about it—we asked members of the Foundry Influencer Network for their perspectives. Their insights reveal an urgent need to rethink data foundations as enterprises accelerate into a cloud- and AI-driven future.
Outdated data infrastructure amplifies sprawl, fragmentation, and operational slowdown
For many organizations, the symptoms of legacy data environments are already visible: increased complexity, slower delivery, and an inability to scale. Will Kelly (LinkedIn: Will Kelly), a writer focused on AI and the cloud, sees the limitations clearly.
“Outdated data infrastructure locks critical data in silos and legacy warehouses that were never designed for the real-time, distributed nature of today’s cloud and AI projects,” he says. “Teams end up stitching together brittle ETL jobs and manual exports, which amplifies data sprawl and makes it hard to know which version of the truth to trust. Those same gaps pose security risks: Inconsistent access controls, limited data lineage, and weak monitoring make it easier for misconfigurations and exfiltration to go unnoticed. The result is higher operational complexity, slower AI and analytics delivery, and more shadow IT as the business tries to route around IT bottlenecks.”
As enterprises push further into AI initiatives, this fragmentation becomes more than an inconvenience; it becomes a structural obstacle that blocks progress. This complexity is not just technical; it is operational and human. Peter van Barneveld (LinkedIn: Peter van Barneveld), Group Innovation Manager at Dustin, shares a practical but often overlooked challenge.
“It is very time-consuming to relocate, reorganize, and clean up data, especially when it is spread all over the place and there are many outdated versions of the same documents,” he explains. “You cannot just have IT do a clean sweep because they rely on subject matter experts in all the different departments where the valuable data lives. Most people do not have a lot of time besides their regular job, and when they also contribute to other projects, it is like a full bucket of water.”
Legacy data systems undermine AI performance and drive technical debt
The pressure to operationalize AI has exposed the consequences of outdated data environments. Even the most advanced models falter when the underlying data is incomplete, inconsistent, or poorly governed. Mircea Trofimciuc (LinkedIn: Mircea Trofimciuc), VP of Engineering, Agentic AI at RealPage, notes how incomplete, inconsistent data directly weakens AI outputs.
“Outdated data infrastructure is the silent killer in AI-driven enterprises,” Trofimciuc says. “These create security threats, vulnerable endpoints multiply, and could lead to breaches like those in recent supply chain attacks, with response times ballooning from minutes to days. Operationally, it creates chaos: AI models fed incomplete data yield waste not useful outputs, stalling cloud migrations and adding 25% to costs via rework.”
Others emphasize that this fragmentation doesn’t just degrade AI performance—it traps IT teams in reactive mode. Gene de Libero (LinkedIn: Gene de Libero), Principal Consultant at Digital Mindshare LLC, explains how scattered data environments force teams to focus on maintenance instead of innovation.
“Outdated infrastructure scatters data across disconnected silos, forcing IT teams to manually patch security gaps and fix broken pipelines,” de Libero says. “Without a clean, trusted foundation, AI initiatives stall in pilot and IT stays stuck firefighting instead of building capability.”
For leaders wondering where to begin, modernizing data governance and observability is a logical starting point.
Kumar Srivastava (LinkedIn: Kumar Srivastava), Chief Technology Officer at Turing Labs, offers a path forward. “CIOs and IT leaders need to start with logging all access/reads to determine the source/app/context/value of each data and use the identified hotspots to prioritize investments in infrastructure for collection, distribution, storage and tooling to facilitate secure usage.”
Together, these perspectives show how outdated data foundations weaken AI long before models reach production—impacting trust, security, and cost.
Aged data infrastructure weakens cyber resilience and expands risk
Security leaders are increasingly vocal that fragile, fragmented data environments are not just inefficient; they are dangerous. When organizations cannot see or control where their data lives, attacks become easier, and breaches become harder to detect. Scott Schober (LinkedIn: Scott Schober), President/CEO at Berkeley Varitronics Systems, underscores the consequences.
“For CIO’s and business owners, failing to modernize means accepting manual errors, leaving easy entry points for cyber threats, and ensuring any AI initiatives will fail due to messy, incomplete data,” Schober says. “Ultimately, old infrastructure turns your most valuable asset—your data—into a significant business liability and an operational nightmare.”
Vivek Singh (LinkedIn: Vivek Singh), Senior Vice President of IT and Strategic Planning at PALNAR, echoes how legacy systems create blind spots across security and operations. “Businesses are stuck in slow, disjointed, and unsafe systems due to outdated data infrastructure. It breaks AI initiatives, slows decision-making, exacerbates data sprawl, and gives attackers blind spots. Legacy data stacks hinder organizations from acting on precise, real-time insights and increase operational complexity rather than fostering innovation.”
At scale, this sprawl becomes impossible to manage manually. Robert Siciliano (LinkedIn: Robert Siciliano), CEO at Protect Now LLC, emphasizes how messy, sprawling data undermines both AI and security.
“The easiest way to say this is ‘garbage in, garbage out.’ Data sprawl is the corporate equivalent of a “junk drawer” that has expanded to fill the entire house. In the past, your data lived in one place (like a filing cabinet). Today, employees save files on their laptops, share them via Slack, upload them to unauthorized cloud apps (Shadow IT), and generate massive logs from smart devices,” Siciliano said. “The result: You have thousands of duplicate, outdated, or inconsistent versions of the same information scattered across places IT cannot see, manage, or secure. Data becomes a burden to manage.”
With these challenges mounting, enterprises are increasingly recognizing that the answer is not more point tools but a fundamentally more intelligent data foundation. Sridhar Iyengar (LinkedIn: Sridhar Iyengar), Managing Director of Zoho Europe, highlights the challenge.
“In today’s cloud-first environment, CIOs are increasingly realizing that outdated data infrastructure is a silent hindrance on digital transformation,” Iyengar says. “Legacy systems are struggling to adjust to the scale, speed, or diversity of modern data ecosystems. This fragmentation amplifies vulnerabilities. In short, outdated infrastructure undermines the very agility modern enterprises depend on.”
A path forward: Modernization as a strategic requirement
Across all expert perspectives, one message is unmistakable: Outdated data infrastructure is no longer just an inconvenience. It is a systemic barrier to cloud transformation , AI innovation , and cyber resilience.
By consolidating fragmented environments, enforcing governance, improving data quality, and adopting an intelligent data infrastructure , CIOs and IT leaders can transform data from a liability into a strategic accelerator—powering innovation, strengthening security, and enabling the next era of AI-driven growth.
Learn how NetApp can help CIOs regain control over data sprawl, strengthen security, and reduce operational complexity with an intelligent data infrastructure built for hybrid and multicloud environments.