As infrastructure automation matures, rigid schemas can hold teams back. This post explains why a flexible data model is becoming essential—and what makes it work.

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A flexible data model for infrastructure automation might feel like stepping into the deep end.

You’re starting with a blank slate, faced with defining everything your way. Where do you begin? How do you land those quick wins? What are the best practices?

But in so many environments, flexibility isn’t just helpful—it’s absolutely necessary.

Most legacy infrastructure tools rely on rigid, vendor-defined schemas. They get you started fast and work well for narrow use cases. But they tend to fall apart as complexity grows or your business builds unique logic into how things work in your particular environment.

You need to reflect the reality of your infrastructure, not shoehorn it into someone else’s model.

That’s why flexibility matters. A flexible platform lets you shape data and automation to match your business, not the other way around.

Why Most Tools Use a Fixed Data Model

There are a few reasons why fixed data models, or schemas, have been so common:

• The creator’s perspective: Often the original creators of infrastructure automation tools were approaching things from a particular domain (e.g., network, storage, compute) and that heavily influenced the model they developed, because it was the boundary of the problem statement they were trying to solve.
• Vendor-driven perspective: Most toolsets were produced by hardware vendors, so it was natural and easy for them to build a model that, even if it wasn’t exclusive, heavily favored that vendor’s products.
• Relative immaturity of infrastructure automation: With rare exceptions, infrastructure automation teams have lagged behind dev teams in practices like CI cycles. Product developers catered to users that in many cases needed and benefited from the automation process being pre-defined and pre-packaged.
• Technology limitations: Historically, most products were built on SQL databases, where table structures are costly to build, relate, and change.

Why You Need a Flexible Data Model Today

Infrastructure automation is no longer a niche experiment—it’s a growing, evolving practice for many organizations. As more teams move beyond basic use cases, they’re discovering that rigid data models can’t keep up with the real-world complexity of their environments.

Early on, predefined schemas and opiniondated tools can offer a valuable head start. They offer structure and reduce decision fatigue.

But as environments grow—spanning hybrid setups, diverse hardware, custom workflows, and multiple stakeholders—those rigid models start to become bottlenecks.

Today, you’re not just automating devices. You’re trying to expose infrastructure as a set of reusable, reliable services. That might mean coordinating multiple “sources of truth”, from custom databases and inventory systems to network design tools and security policy managers. Integrating across all that requires more than a one-size-fits-all schema.

The goal isn’t just integration, though. It’s about enabling processes that are versioned, repeatable, and adaptable—like CI/CD workflows for infrastructure. That’s where a flexible data model becomes a practical requirement, not just a nice-to-have. It gives you the freedom to represent what matters in your context, while supporting the rigor needed for sustainable automation.

It’s like choosing to use Salesforce or SAP. These are powerful, flexible, modifiable platforms that accommodate a ton of different types of business data. They require investment and work upfront, but they can run complex, evolving business processes, where simple tools often can’t.

The Cost of Rigid Data Models in Automation

The 2024 Enterprise Network Automation report from Enterprise Management Associates (EMA) highlighted a telling pattern in the industry.

While 90% of surveyed organizations had deployed a network source of truth (NSoT), only 20% considered it successful. More broadly, just 18% felt their network automation efforts had fully succeeded.

Why the gap? In many cases, it comes down to inflexibility. Rigid tools don’t map well to a company’s unique infrastructure, and limited staff resources can make it hard to fill in the gaps manually. What starts as a quick win can turn into a brittle patchwork.

Over time, complexity accumulates. And without a flexible foundation, it becomes harder, and more costly, to sustain automation as your needs evolve.

Infrahub: A Flexible Data Model in Practice

There are excellent solutions out there for automating application development or spinning up cloud automation for dev, test, or production software deploys (cough Git cough).

But there’s been a glaring lack of platforms that give the flexibility and power needed to build a full, sustainable infrastructure automation lifecycle, from data through deployment.

That’s where a data management platform like Infrahub can help.

Infrahub is designed for teams who’ve moved past the basics and now need to manage diverse infrastructure data, coordinate across multiple sources of truth, and support a continuous integration approach to infrastructure design and delivery.

It brings flexibility by design—not requiring a canonical model—so you can build automation frameworks around the data structures and workflows you already use, not what a tool expects.

Infrahub may not be the right starting point for everyone. If you’re just beginning with automation or only need something simple and prescriptive, there are great tools out there to help. But if your needs are growing, and you’re hitting the limits of what rigid tools can offer, it might be worth exploring a platform that’s built to adapt.

You can find Infrahub on GitHub or reach out for a live walkthrough to see how it might fit into your automation journey.