What is a "Data Architect"?

If you strip away the job titles, tools, and buzzwords, a Data Architect’s core purpose is simple: to ensure an organisation’s data is understood, structured, and used correctly so the business can rely on it.

Everything else - the models, diagrams, platforms, standards - exists to support that goal.

But throughout my career, I’ve noticed a funny pattern: the moment something involves “data,” people often assume it must be the Data Architect’s responsibility. Sometimes that’s true - but often it isn’t. Data Architecture is a specialist discipline, and understanding it properly means stepping back and looking at the bigger picture of how IT actually delivers information.

The purpose of IT

At its core, IT exists to deliver the right information, in a timely manner, using technology, so a business can operate effectively.

If you believe IT is purely about delivering technology to a business, just remember the "I" before the "T".

Imagine providing a technology product and asking a business user to manage and process all their own data. A few will embrace it, but most don’t have the time or the skill set. They are focused on running the business and rely on timely, accurate information to do so.

That is why IT builds information systems that transform data into meaningful, usable business value.

What is an information system?

Information systems come in many forms, including, Transaction Processing Systems that handle day-to-day operations; Management Information Systems and Decision Support Systems that help leaders make informed decisions, to name just a few.

Whatever the information system, each one is built around five key components:

People – the users, managers and specialists who design, operate and make sense of the system.
Process – the rules and routines that determine how data is collected, stored, and transformed into meaningful information to meet business requirements.
Data – the raw material, contextualised to become information.
Software – the business applications and technology products that tell the hardware how to manage and process data as per the defined processes.
Hardware – the platform that makes it all run.

Does this mean a Data Architect is responsible for delivering the "data" component? - If so, what does that comprise?

To understand this, we need to ground it in the context of the primary Enterprise Architecture (EA) domains.

The primary domains of Enterprise Architecture

Over the years, the term Data Architect has been stretched, and is now often used to describe a wide range of data-related roles.

While these might sound similar, each one requires its own focus and expertise within EA.

So, what are the primary domains of EA?

Business Architecture - the blueprint of the organisation; how it is structured, how it operates, and what it is trying to achieve. It is not about technology; it is about defining the strategic context on which everything else builds.
Information Systems Architecture - this domain connects business needs to the systems that deliver them and consists of two parts:

Application Architecture – how an organisation’s business applications are structured, interact and integrate. The goal is to make sure they support the business effectively while meeting key non-functional needs like performance and scalability.
Data Architecture – defines the organisation’s data assets and how they are managed. Its purpose is to translate business information needs into concrete data requirements, ensuring that data is consistent, understood, accurate and properly used.

Technology Architecture - the infrastructure, technology products and platforms on which things run. This is where decisions about standards, performance and security come into play, ensuring the environment reliably and efficiently supports the organisation’s needs.

By grounding our understanding of Data Architecture within this broader framework, it starts to become clearer where the Data Architect fits into this.

Mapping data deliverables to the architecture layers

If we next map these architecture domains to key data deliverables, the picture becomes clearer still. This is illustrated in the diagram below using TOGAF ADM and ArchiMate layers for context:

As per the diagram, Data Architecture focuses on the data's content, bridging the gap between business and IT. The core meaning of data remains consistent across transactional and analytical systems. It is how we structure it in our application data models that differs, to ensure efficiency of the query patterns required to deliver the application specific use cases of the data.

Data Platform Architecture is often labelled as Data Architecture, but within EA it actually belongs within Technology Architecture. The role of Data Platform Architecture is to define and configure the infrastructure and technology products needed to host and process data, enabling the advanced analytical workloads that rely on it.

So how does this translate into actual data-related architecture roles?

Defining the data-related architecture roles

Let’s look at the key roles:

Data Architect – provides a specialised view to ensure a shared understanding and correct use of data, using enterprise data models, data flow diagrams and data lineage to bridge business requirements with technical delivery.
Data Modeller – specialises in creating data models, with a focus on optimising application data models for efficient storage, performance, and alignment with business needs.
Data Platform Architect – provides a specialised technical perspective, defining the infrastructure and technology products (e.g. data platforms, data fabrics) required to host, process and secure data for advanced analytical workloads.
Data Solutions Architect – brings a broad, cross-layer perspective to design end-to-end data solutions supporting analytics, BI, data science, AI and machine learning.

Conclusion

Data Architecture isn’t about owning all things data - it’s about making sure data is understood, trusted and used correctly across an organisation. A Data Architect doesn’t replace developers, analysts, business teams or other architecture roles; they connect them by creating a shared understanding of what the data means.

As organisations collect more data and adopt new platforms, this shared clarity becomes essential. When Data Architects do their job well, the IT landscape becomes easier to build, integrate and evolve.

And as AI takes centre stage, Data Architects become the guardians of meaning. AI is only as good as the data and definitions behind it - without consistent context, it can’t deliver reliable insight.

Put simply: Data Architects make good information possible - and good information makes good decisions possible.

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