Drowning in Data? Here’s Why You Need to Ditch the Rowboat for an Aircraft Carrier

(Doremi/Shutterstock)

Many IT leaders today aren’t just managing data. They’re drowning in it.

The volume, velocity, and complexity of enterprise data has exploded, and traditional infrastructure simply can’t keep up. Teams are paddling faster, working longer, and patching together outdated systems just to stay afloat. But the flood is only rising.

Picture a rowboat in a storm. Water is pouring in faster than it can be bailed out. The crew is exhausted. The holes are multiplying. And all the while, the sea of data around them keeps swelling. This is the reality for organizations clinging to legacy tools and cloud architectures built for a fraction of today’s scale.

According to recent research, nearly half of IT leaders say their data is “growing too fast to manage.” Another 64% point to unpredictable cloud costs, while 53% cite rising energy demands as a growing concern. The pressure is mounting from all sides: cost, complexity, and climate.

And the waves are only getting bigger. ​​According to the International Energy Agency, electricity demand from data centers worldwide is projected to more than double by 2030, reaching around 945 terawatt-hours, slightly more than the entire electricity consumption of Japan today. At this scale, inefficiency is no longer a nuisance. It’s a liability.

The Rowboat is Sinking

In an effort to stay afloat, many enterprises are trying to patch their systems with incremental upgrades. They add more cloud instances. They layer on external tools. They spin up new teams to manage increasingly fragmented stacks.

But scaling up a fragile system doesn’t make it strong. It just makes the cracks bigger.

Retrofitting legacy systems to handle modern workloads is like bolting a motor to your rowboat and hoping it becomes a battleship. Instead of addressing the foundational mismatch between architecture and demand, organizations are piling on cost, complexity, and energy burn. And with the rise of AI, things are only getting worse.

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AI thrives on analytics, but it also amplifies the underlying problem. A human might write a handful of queries per day. An AI engine might execute thousands per second. If you’re struggling with performance and cost now, AI will stretch your systems beyond the breaking point.

The deeper issue is this: the dominant architecture most enterprises still rely on was designed over a decade ago. It served a world where workloads operated in gigabytes or single-digit terabytes. Today, companies are navigating hundreds of petabytes, yet many are still using infrastructure built for a far smaller scale. It’s no wonder the systems are buckling under the weight.

The result? Critical queries time out. Workloads stall. And teams spend more time troubleshooting than innovating. What was once “good enough” is now actively slowing the business down.

What You Need is an Aircraft Carrier

So, what’s the alternative?

You don’t need more buckets. You need a new kind of boat. One designed for scale. One that turns the storm into an advantage. An aircraft carrier.

In this metaphor, the “aircraft carrier” reflects a shift in tech…and in thinking. It points to a new approach to infrastructure that’s designed from the ground up to handle hyperscale data, support real-time analytics, and accommodate the growing demands of AI workloads.

As organizations reevaluate their data architectures, several priorities are coming into sharper focus:

•     Reducing fragmentation by moving toward more unified environments, where systems work in concert rather than in silos.
•     Improving performance and cost-efficiency not just through hardware, but through smarter architecture and workload optimization.
•     Lowering latency for high-demand workloads like geospatial, AI, and real-time analytics, where speed directly impacts decision-making.
•     Managing the energy consumption bottleneck in ways that align with both financial and sustainability goals.

Ultimately, this shift is about enabling teams to go from playing defense (maintaining systems and containing cost) to playing offense with faster, more actionable insights.

Why Most Teams Get Stuck

Chris Gladwin is the co-founder and CEO of Ocient

Many data leaders know change is needed. But they get stuck at the edge of transformation. They’re constrained by budget, talent, or institutional inertia. Or they’re simply overwhelmed by a growing cloud bill that penalizes experimentation.

These are smart teams, led by experienced technologists. But when infrastructure problems become cultural ones (e.g., when agility gives way to anxiety) momentum fades fast.

Some organizations even resort to rationing innovation. One analytics leader recently shared that her team receives a weekly cloud bill, broken down by query. If a query costs too much, the person responsible is reprimanded, even if that query was necessary for a business-critical insight.

That’s the tradeoff too many teams face. They’re forced to choose between testing innovative new ideas or complying with budgetary restrictions. It’s a structural limitation, not just a budget line. If cloud cost becomes the gating factor for asking important questions, innovation gets smothered in red tape.

That’s not innovation. That’s survival mode. And no one flies by paddling harder.

The Cost of Inaction

The biggest risk isn’t just technical debt. It’s future blindness. As new AI-driven applications demand faster insights, more granularity, and deeper context, companies tied to legacy infrastructure will fall behind.

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Worse, they won’t even know what they’re missing.

In many cases, the datasets needed to power the next generation of products, services, and decisions are already in-house. They just aren’t accessible in time, scale, and at a cost that makes sense.

From Survival to Strategy

The data flood isn’t slowing down. But it’s not the volume of water that determines who survives. It’s the vessel.

Instead of trying to row around a growing data lake, switching to a new architecture designed for both current and future data challenges will enable you to quickly maneuver around your data ocean like having a modern aircraft carrier.

For many enterprises, this shift starts with new thinking. Are your systems designed to adapt to change, or just to keep the lights on? Are your teams empowered to innovate, or are they stuck firefighting yesterday’s infrastructure?

Stop patching. Start planning for lift-off.

About the Author: Chris is the CEO and Co-Founder of Ocient, a company providing the software platform for the largest data analyzing systems in the world.  He also is the Chair and Co-Founder of P33, an organization that is helping transform Chicago into a globally top-tier tech region. And Chris is the Chair and Co-Founder of The Forge which provides amazing recreational and environmental experiences along with ecosystem revitalization.