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AI-Ready Data Mesh
Enterprise Data Architecture for Scalable AI
AI-Ready Data Mesh is a public, enterprise-grade body of work focused on data architecture, governance, platform engineering, and operating models required to enable scalable, trustworthy AI.
This repository documents a practical, experience-led approach to Data Mesh in large, complex, regulated organisations.
This is not an AI tooling guide.
It is not a machine learning tutorial.
It is an enterprise data architecture playbook.
Key Topics
- Data Mesh architecture
- AI-ready data platforms
- Enterprise data governance
- Federated governance models
- Domain-oriented data ownership
- Data products and contracts
- Platform engineering for data and AI
- Organisational change for data and AI
- Data Mesh adoption and maturity
- Enterprise AI enablement
Problem Statement
Many enterprise AI initiatives fail to scale due to:
- fragmented data ownership
- unclear accountability
- weak data quality
- inconsistent semantics
- manual, non-scalable governance
AI-Ready Data Mesh positions Data Mesh as the architectural response to these structural problems.
AI does not fail because of models.
It fails because of data architecture.
Who This Repository Is For
This content is intended for:
- Data Architects
- Enterprise Architects
- Heads of Data & Analytics
- Platform Engineering Leaders
- AI & ML Leaders
- CTOs, CIOs, and CDOs
If you are responsible for enterprise data strategy or AI enablement, this repository is designed for you.
Repository Structure
docs/– core content (modules, templates, reading paths)docs/modules/– architecture, governance, platform, organisation, and adoption modulesdocs/templates/– practical templates and frameworksdocs/reading-paths.md– role-based guided reading
Start with Reading Paths to navigate by role and responsibility.
How to Use This Content
This repository can be used as:
- a reference for enterprise data and AI initiatives
- supporting material for architecture design and decision-making
- thought leadership on Data Mesh and AI enablement
- training or internal enablement material
The content is modular and can be consumed selectively.
Technology
The site is written in Markdown and is compatible with static site generators such as:
- MkDocs (recommended)
- GitHub Pages
- Docusaurus
The content is intentionally platform- and vendor-neutral.
Author
Richard Fisher
Chief Data Architect
Architecting the data foundations behind scalable AI.
Philosophy
This work is guided by a small set of principles:
- Architecture is about decisions and trade-offs
- Ownership matters more than tooling
- Governance must scale through automation
- Platforms should reduce friction
- AI requires trust, context, and accountability
- Sustainable change is organisational as well as technical
Status
This is a living repository.
Content will evolve as new lessons are learned and enterprise AI practices mature.
License
Unless otherwise stated, this content is shared for learning and reference purposes.
Commercial reuse should be discussed with the author.
If you are serious about AI, you must be serious about data architecture.