Data management practices are under pressure, and the forces reshaping the landscape can no longer be ignored. Three trends underscore why today’s approaches are breaking down—and what CIOs need to consider as they chart the path forward.
Trend #1: AI acceleration
For decades, enterprises built data infrastructure in vertical application stacks. It wasn’t elegant, but it worked. As systems grew more complex, teams just worked harder, adding cost and effort to keep things running.
But now, AI has changed the equation. Those silos that once held everything together are cracking, exposing data sprawl, security gaps and rising complexity.
AI cares about one thing: data. To work, it needs complete, untethered access across environments. That creates two fundamental problems for traditional stacks:
- Silos block access. Models require seamless, parallel input from every data source. Vertical systems create the opposite: isolated pools that slow down training and inference.
- Copies multiply. To fix these access problems, most teams simply create additional copies. Before AI, enterprises managed three to five copies of their data. Today, they juggle nine to eleven across environments. Each copy has the potential to drift, which can add cost and complicate governance.
According to Michael Leworthy, senior director of Platform Marketing at Pure Storage®, “Before, it was like putting bandages on leaky pipes, managing all these isolated systems. But with AI, we can’t keep up anymore. The basement’s flooded. The old model can’t fix it—we have to change it.”
The impact is clear: GPUs sit idle while waiting for fragmented data, compliance teams lose track of which copy is authoritative, and security issues multiply as IT stitches isolated systems together. Meanwhile, traditional storage—fundamentally unchanged in 40 years—can’t keep pace with AI’s demand for unified, instant access.
Trend #2: Shift to as-a-service models
The public cloud changed expectations. It showed enterprises that infrastructure could flex with demand, spin up workloads in minutes, and charge only for what’s used.
But the bigger shift came from the consumer side. People grew accustomed to tapping an app and getting instant results. They expect systems that just work, without the knobs and dials. That mindset bled into IT. Now teams want the same experience inside the enterprise—automation that handles the busywork, platforms that tune themselves, and the kind of simplicity that makes the hard look easy.
Application teams now expect the same agility on-premises that they get in the cloud. Meanwhile, CIOs are under pressure to deliver cloud-like speed and simplicity everywhere, while still keeping sensitive data local for compliance, performance or cost reasons.
That’s why as-a-service models are no longer just a cloud story. They’re becoming the default expectation for enterprise IT, whether the workload runs in the data center, the public cloud, or at the edge.
Trend #3: Escalating complexity and costs of multi-architecture systems
Finally, storage complexity has caught up with enterprises. Data continues to grow exponentially, and the mix of on-premises, cloud and edge systems only multiplies the challenge. Every new architecture adds another layer of tools, skills and integration work. The result: more cost and friction, with less control.
- More infrastructure, more people. For years, the brute-force answer to sprawl was to buy another array, hire another admin, or build another connector. But headcount and budget can’t keep up with data growth or the sprawl across architectures.
- Rising energy and footprint. Each added system or extra copy consumes power, space and cooling. Storage may only represent a small percentage of the data center footprint, but doubling or tripling copies quickly multiplies cost.
- Security and compliance gaps. When data lives across multiple architectures, enforcing a single policy is nearly impossible. Each system plays by it’s own rules. Every copy increases the odds of drift, ransomware exposure, or compliance slip-ups.
The bottom line is that costs go up, while trust in the data erodes.
Making a change
It's clear that the old model isn't working. The question is what comes next, and the clues lie in what’s already been accomplished in the public cloud.
Leworthy explained:
“The public cloud already showed us the model that works. Hyperscalers don’t run vertical systems—they run everything horizontally across compute, storage and networking. They build unified data planes and control planes, automate with policies, and operate massive environments with just a handful of people. That’s the right architecture. The real question for CIOs is how to bring that same model on-premises and to the edge—in a phased, practical way that avoids the disruption of a full rip-and-replace.”
That’s where EDC comes in. EDC transforms storage into intelligent data management. IT gets control of data, not just infrastructure—eliminating silos, automating operations, and enforcing governance everywhere. The result is faster delivery, lower risk and a platform ready for AI’s speed and scale.
Why the Pure Storage approach stands apart
EDC is the strategic choice—but not all approaches are created equal. Many vendors promise a “cloudlike” EDC model but deliver it by bolting systems together or forcing centralization—leaving customers with more complexity, not less.
Pure Storage took a different path, centered around:
- Unified Data Plane. In a forward-looking move, Pure separated hardware from software and built a common operating environment—Purity—that runs natively across block, file and object storage. This allows IT to virtualize all enterprise data into a single cloud of data—on-premises, public cloud, or edge—so every workload interacts with the same, current dataset. This foundation eliminates silos and ensures consistent control everywhere data lives.
- Single Intelligent Control Plane. Pure Storage enables policy-driven automation across the estate by abstracting data into a virtualized layer. IT can provision, protect, govern and optimize data automatically in real time. Applications consume the same set of services consistently—performance, protection, governance and compliance—without juggling fragmented copies.
- Governance built in. With policies embedded at the data plane, compliance and security travel with the data. Access is consistent everywhere, without bolting on third-party tools.
- Always-modern service delivery. Through the Pure Storage Evergreen® model, organizations consume capacity as a continuously modern service. Upgrades happen with zero downtime, migrations are eliminated, and scaling becomes non-disruptive.
Together, these capabilities give CIOs a platform that’s inherently ready for AI—without the risk of disruptive overhauls.
Ready to see how a unified data cloud could fit into your strategy? Explore the EDC Guide to learn how enterprises are modernizing without disruption.
