Beyond Silos: Why Data Management Begins with Organizational Design

Conway’s Law states that systems mirror the communication patterns of the teams that build them.

In practice, when teams wall themselves off, their data and technology follow suit. This dynamic shows up in enterprise environments, where the way teams are structured directly shapes the systems they create.

To address data silos, leaders should start by understanding how they form. Teams often choose tools to meet specialized needs, organizational hierarchies, and incentives that often reinforce independence over collaboration. Structural factors like geography, regulations, and culture can add layers of complexity. And while some silos are necessary, unchecked ones multiply and harden into barriers.

Why data silos are becoming existential threats in the age of AI

AI promises speed and scale, but if data and responsibilities are fragmented, AI will only accelerate dysfunction. For instance, a single analyst might manage dozens of AI agents, each executing workflows based on siloed inputs. Instead of bridging gaps, these agents multiply them.

In some cases, specialized tooling that perfectly match a team’s workflow is worth the tradeoff, because the efficiency gains outweigh the friction with adjacent groups. But once you start adding AI-driven automation on top, those inefficiencies compound across departments.

Consider the common scenario where DevOps teams run applications in Kubernetes, network teams handle ingress and egress traffic, and security operations monitor for misuse. If an application suddenly sees a surge in traffic, the network team may provision more endpoints and DevOps may spin up additional pods to meet demand. At the same time, the security team may detect that the surge is actually a coordinated attack. With no integration between autoscaling tools and security alerts, the system responds by scaling the attack surface while security scrambles to contain it. The resolution comes from a good old fashioned phone call across teams to shut off the autoscaler.

Legacy processes that rely on human coordination sometimes avoid these pitfalls, while highly automated environments can magnify them. Unless organizations design structures and communication channels that cut across teams and silos, AI will only accelerate misalignment instead of eliminating it.

The Upside of Alignment

When organizational structures, incentives, and data strategies move together, the difference is immediate and measurable. Incidents are spotted faster because blind spots shrink, audits become a predictable process instead of a scramble, and leadership can focus on long-term risk instead of short-term firefighting.

Alignment is not only about tools and data. It changes the way employees experience their work. When incentives push teams toward shared outcomes, people stop working at cross-purposes and start seeing themselves as contributors to a larger mission. A security analyst who used to be rewarded solely on ticket volume might instead be measured on how effectively threats are neutralized across the enterprise. That shift creates space for more strategic work, reduces burnout, and makes the role feel less like constant triage and more like meaningful impact.

AI works best when it has scale, variety, and integration, and the walls we build with team design, incentive structures, and fragmented responsibilities are the same ones holding back machine learning models, predictive insights, and human-machine collaboration. Leaders who are willing to rethink organizational DNA, not just their technology stack, will unlock the foundations needed for AI to thrive.

Modernizing team structures

Restructuring teams for the age of AI means moving beyond traditional hierarchies. For example, instead of placing all data science talent inside a central function, leading organizations are embedding small squads of data scientists within product, security, or operations teams while keeping them connected through a common guild or center of excellence. This approach allows local teams to innovate with embedded expertise while maintaining shared standards for data governance and AI ethics.

Rotation programs also modernize structures by breaking the grip of deep specialization. When security analysts spend a quarter embedded with engineering, or marketers rotate briefly into data analytics, they not only build empathy across functions but also identify systemic friction points where data handoffs tend to fail. Over time, these rotations help shift the culture from 'my team’s data' to 'our organizational data.

The result is not just flexibility for product launches or cross-functional initiatives, but a workforce that treats data as a shared corporate asset, reducing silos before they form.

Technical solutions rooted in organizational harmony

The tools and processes your teams adopt are not neutral. They mirror the way your organization collaborates or fails to. To reduce silos, CISOs should resist making technology decisions in isolation and instead build collaboration directly into the decision-making process.

One effective approach is to create a cross-functional working group with stakeholders from presales, customer success, support, product, engineering, and beyond. This ensures that every department has a voice, communication happens early, and decisions reflect the needs of the whole business. Importantly, this shouldn’t wait until an organization reaches a certain size - even small orgs should practice this from the start so they don’t create problems down the line.

History shows why this matters. Decades ago, when most organizations ran on mainframes and desktops, IT operations often reported to a single director or CIO. As infrastructures shifted to client-server models, teams specialized to handle scaling demands, spinning out into network operations, security operations, and IT operations groups reporting to different leaders. The move to service-based architectures and microservices multiplied endpoints by orders of magnitude, giving rise to DevOps, DevSecOps, ITOps, SOCs, NOCs, observability teams, and more. Some tasks have been automated away, but humans remain at the center with tens of DevOps engineers, dozens of SOC analysts, distributed NOC staff all coordinating across fragmented responsibilities.

AI is now poised to drive another structural shift: a push back toward recentralizing IT infrastructures and the processes that surround them. As more of the day-to-day work is handled by AI agents, the risk of silos scaling faster than alignment increases. Without cross-functional structures in place, organizations will find themselves automating dysfunction rather than solving it. But with the right design, like architecture review boards and federated governance models that enforce shared guardrails, technology can reinforce collaboration instead of reflecting division.

When these practices are in place, systems no longer work at cross-purposes. Instead, the stack feels coherent, innovation accelerates, and organizations regain the kind of alignment that once came naturally in smaller, centralized team, tool, and process structures, only now at a scale built for AI.The future of AI will not be written by algorithms alone, it will be shaped by the organizations bold enough to reimagine themselves around connection.

Subscribe to the Perspectives by Splunk newsletter and get actionable executive insights delivered straight to your inbox to stay ahead of trends shaping security, IT, engineering, and AI.