I've worked with a lot of biotech companies over the years. The ones that do enterprise architecture well have one thing in common: they didn't try to do everything at once.

The ones that struggle also have something in common: they tried to model everything from day one, ended up with an incomplete repository nobody trusted, and spent years trying to recover.

If you're in biotech and thinking about EA for the first time, this guide is for you.

Why Biotech Is Different

Biotech companies have a specific set of characteristics that make EA harder than in most industries:

Compliance isn't optional

21 CFR Part 11 governs electronic records. EU Annex 11 does the same for Europe. Your EA documentation isn't just useful — in many cases it's a regulatory requirement.

Validation is expensive

Every system that touches regulated processes needs validation. If your EA tool is part of that, you're adding months to implementation and significant cost.

Documentation is already scattered

LIMS, MES, ERP, QMS — the application landscape is complex and often poorly documented. Starting from scratch means convincing people to give you information they don't have time to find.

Speed matters

When a clinical trial has a deadline, EA documentation that takes six months to build isn't useful. You need something that can provide value faster than traditional approaches allow.

Start With Five Applications

The biggest mistake I see biotech teams make: trying to model their entire application landscape on day one.

Don't do that.

Start with five applications that everyone agrees matter. The ones that are central to your regulated processes. The ones that everyone talks about in meetings. The ones that would cause the biggest problems if they went down.

Get those five applications documented accurately. Add metadata — what they do, who owns them, what they connect to, what regulatory relevance they have. Build a diagram that actually reflects reality.

Then show it to the people who work with those systems every day. Ask them what's wrong. Fix it.

That's a working EA practice. It's small, it's accurate, and people trust it because they've seen it validated against their own experience.

The biotech EA sequence that works:

1. Pick five central applications
2. Document them accurately with real metadata
3. Show to the people who know those systems
4. Fix what's wrong
5. Expand to the next five

What to Document (And Why)

For biotech specifically, not all EA metadata is equal. Some information matters more:

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Regulatory classification

Which systems are part of validated processes? Which are GxP-relevant? This affects validation burden and determines what compliance features you need.

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Data flow

Where does regulated data travel? Which systems receive data from which? This is audit evidence.

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Integration points

API connections, file transfers, database links — anything that moves data between systems matters for both operations and compliance.

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System owner and backup owner

When something breaks at 2am, who do you call? This should be in your architecture documentation, not in someone's head.

How to Actually Get Started

Most biotech teams start EA by sending a spreadsheet to department heads and asking them to fill in application information. This doesn't work.

People are busy. They don't remember every application they manage. They guess at connections they should know. The data comes back incomplete and wrong.

Better approach:

Start with what you already have. Your IT team has a CMDB or service catalog. Your validation team has system inventories. Your QA team has lists of GxP-relevant systems. These documents exist — find them and import them.

Fill gaps by talking to people, not sending surveys. The information you need is in people's heads. Schedule conversations with system owners. Ask them what their system connects to and what it does. Write it down. That conversation is worth more than ten surveys.

Build diagrams that people recognize. Show them to the people who work with those systems. If they say "that's not right," figure out why and fix it. The goal is accuracy, not completeness.

The Compliance Question

If your company operates under FDA or EMA oversight, at some point someone will ask about your electronic records compliance. When that happens, you'll need to demonstrate that your EA documentation is trustworthy.

What that means practically:

• Audit trail — every change to your EA data is logged, timestamped, and attributed
• Version history — you can prove what your architecture looked like at any point in time
• Access controls — only authorized people can modify EA data
• Validation — you can demonstrate the tool itself meets regulatory requirements

Not every EA tool is designed for this. If you're in a regulated environment, the question of whether your EA tool meets compliance requirements shouldn't be an afterthought. It should be the first question you ask any vendor.

What Good Looks Like

I've seen EA practices in biotech that work, and they share certain characteristics:

Small but current: They have 30 applications documented, not 300 half-documented ones. They can tell you the owner of each system, what it connects to, and when it was last updated.

Trusted: When a QA manager asks about the application landscape, they get an answer they can rely on. Not "I think it's mostly this" but "here's exactly what we have."

Used: The architects actually open the tool. When they're planning a new integration, they check the existing connections first. When something goes wrong, they look in the EA system before starting from scratch.

The goal isn't a comprehensive model of everything. The goal is a small set of accurate information that people actually use to make decisions. Start there. Expand when you have something people trust.