# Data architecture · Glossary
The overall design of how an organisation’s data is collected, stored, integrated, governed and made available, including the platforms, data models, flows and standards involved.
[Glossary](/glossary) · Data platforms and engineering

# Data architecture

     The overall design of how an organisation’s data is collected, stored, integrated, governed and made available, including the platforms, data models, flows and standards involved. It sets the structure within which individual pipelines and products are built.

## Why it matters

     Architecture decisions are expensive to reverse. Choices about where data lives, how systems integrate and which platform is authoritative for each domain shape cost, security, agility and the feasibility of AI for years.

     A useful architecture is driven by the decisions and workflows the business needs to support, not by technology trends. It should be documented clearly enough that new teams can extend it consistently.

## In practice

     For example, a UK specialist manufacturer with separate ERP, MES and quality systems might define an architecture in which operational data lands in a central lakehouse, is modelled into shared domains such as orders and batches, and is exposed to reporting and AI through governed views.

## Where Rodan fits

     Rodan designs data architecture as part of [Platform and Cloud Engineering](https://rodan.io/platform-cloud-engineering) and [Analytics and Intelligence](https://rodan.io/what-we-build/analytics-intelligence) engagements.

## Related terms

- [Data engineering](/glossary/data-engineering)

- [Data lakehouse](/glossary/data-lakehouse)

- [Data mesh](/glossary/data-mesh)

- [Data fabric](/glossary/data-fabric)

- [Semantic layer](/glossary#semantic-layer)

- [Data modelling](/glossary/data-modelling)

- [Legacy modernisation](/glossary/legacy-modernisation)
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