Core Data Architecture Deliverables

Published: December 11th, 2025

Data Architecture isn’t about drawing boxes and lines. It’s about creating the core artefacts that enable trustworthy, well-managed data - the data that modern platforms, scalable systems and GenAI rely on. Without it, none of them deliver what they promise.

Historically, these artefacts have often been overlooked, but they are no longer “nice to have”. They are essential.

In this article, I break down the core Data Architecture deliverables, how they fit together and why getting them right upfront matters more than ever.

Before looking at the deliverables, it helps to understand where Data Architecture sits in the bigger picture of Enterprise Data Management.

Put simply, Enterprise Data Management is how an organisation makes sure its data is reliable, secure and easy to use. It brings together the policies, processes and tools that stop data becoming a collection of disconnected systems and spreadsheets.

While everyone has a role in managing data, two areas are fundamental:

Data Governance – defining the rules, standards and policies
Data Architecture – designing how data is structured, connected and made available

In simple terms: Governance defines what “good data” looks like. Architecture makes it possible.

What are the core data architecture deliverables?

We will focus here on the core Data Architecture deliverables, the ones that appear across almost every solution. A Data Architect contributes to many areas of Enterprise Data Management, but these artefacts form the backbone.

These are the core Data Architecture deliverables you’ll see in almost every organisation. A Data Architect doesn’t always create each one personally - but they do ensure the standards, patterns and tooling exist so teams can produce them consistently.

Enterprise Data Models

An Enterprise Data Model gives an organisation a shared understanding of its most important data. It turns business concepts into clear entities, attributes, keys and relationships - independent of any specific system or technology.

Think of it as the bridge between business language and application design. When it is done right, application data models and system integrations become far easier to build and far more consistent.

Data Flow Diagrams

A Data Flow Diagram shows how data actually moves through an organisation - where it starts, where it travels, where it is stored and where it ends up.

They make it much easier to spot system boundaries, control points and risk areas. In short, they help ensure data stays accurate, secure and well managed as it flows across the business.

Data Lineage

Data Lineage tells the full story of your data - where it came from, how it moved and how it changed along the way.

It builds on two foundations:

Data Models – describing structure
Data Flows – describing movement

Lineage then adds the missing piece: transformation logic - rules, mappings, calculations, aggregations and more - creating a transparent, end-to-end view from source through to any downstream output, such as analytics, data products or reports. This is what enables trust, faster troubleshooting and regulatory confidence.

Forward engineering data architecture artefacts

Historically, many of these artefacts were skipped during delivery, only to be painfully reverse-engineered later on to satisfy regulators or standards like BCBS 239.

Reverse engineering will always have a place - especially in legacy environments or due to new regulations. But forward engineering is where modern organisations need to focus, and this matters even more in the age of GenAI.

It’s worth asking a simple question: Who is better positioned to succeed -

An organisation that uses GenAI to forward-engineer applications using compliant artefacts

An organisation that uses GenAI to reverse-engineer artefacts after implementation?

Even when reverse engineering is unavoidable, understanding how to create these artefacts properly from the outset is what enables sustainable progress.

As forward engineering becomes the norm, we need to ensure there is a joined-up ecosystem of data management tooling that makes the process easier, faster and far more consistent.

The diagram below brings this together, showing the core Data Architecture artefacts, how they depend on one another and the application components they enable.

And as the diagram shows, forward and reverse engineering together, can ensure that design intent matches what was actually implemented.

Conclusion

Data Architecture is about creating the artefacts that make data trustworthy, scalable and compliant.

When organisations understand these deliverables and how they connect, everything becomes easier - system design, integration, governance and even the effective use of Generative AI.

Get the foundations right and everything built on top becomes faster, safer and more valuable.

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