❗️The Problem

Everyone wants clean data, fast processes, and easy integration.
But nobody maps how data flows, how it's structured, or where it breaks.
So what happens?

You duplicate logic
You integrate the wrong fields
You migrate garbage
You redesign the same thing three times

All because you skipped the architecture.

🧠 What DAMA Says About Data Architecture

Data Architecture is the blueprint.
Not just models — but how data connects, travels, and lives across systems.

It's how you make sure:

your CRM and ERP don’t speak different languages
your master data isn’t redefined in every project
your MDG system actually fits into the bigger picture

DAMA Key Components of Data Architecture

Component	Meaning
Conceptual Models	High-level views: What data domains exist?
Logical Models	Entity relationships, rules, without tech constraints
Physical Models	How data is stored in systems (tables, types, keys)
Data Flows	Where data comes from, where it goes, and how
Integration Points	APIs, messages, staging layers, pipelines

🔧 In SAP MDG Context

SAP MDG needs architecture to function well — but doesn’t provide it out of the box.

You need to design:

What systems send/receive master data?
What’s the golden source?
How do SOA, SLT, or APIs move data?
How are Flex/Reuse models aligned with global architecture?

If you skip this — you build isolated processes that break the moment you scale.

🧩 Signs of Broken Data Architecture

Same field exists in 5 systems with different meanings
No single source of truth — every system claims ownership
You can’t trace where a data error started
MDG replication fails — and no one knows why

That’s not a workflow issue. That’s an architecture failure.

💬 Bottom Line

If you don’t design the data architecture, it will design itself.
And it’ll be ugly.

You don’t need diagrams for decoration — you need them to survive change, scale globally, and build trust.

DAMA - SAP MDG: Concept #3