Data Mesh – Fundamental Pillar for AI-readiness
When AI Meets Data Reality: How to Manage and Govern Data to Enable Swisscom for AI-Readiness
How it all started
The beginning of our Data Mesh journey
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with People for People
with
People & AI
for
People & AI
AI-readiness�Measures an Organization’s preparation to adopt, deploy, and scale AI effectively��
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Main Success Factors
AI-readiness Levels
Data needs to be:
findable and described
trusted and contracted
semantically navigable
��In the AI era�Data Mesh is here to stay
How Data Mesh pillars contribute to the AI-readiness
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Organization –Data & AI is everyone’s business
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SPOKES
HUB
Data, Analytics
& AI
Value Spokes
(Business Units)
Master Data
Producers
(Operational Source Systems)
70%
of AI Investments
should go into
People and processes
Source: McKinsey
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Federated Governance – Shared Responsibility
CENTRAL GOVERNANCE
(What & Why)
DECENTRALIZED DATA TEAMS
(How)
→
←
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Swisscom Data Model (SDM) - a common language for data exchange
Central (Data Management and Governance): owns, develops and maintains the conceptual & logical model across domains.
Data Teams: use it as the reference for every data product (logical and physical data models) — and extend it where their business needs more.
Aligned with TM Forum standards
Shared semantics and consistent relationships is what enables AI to reason across domains
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Principles
Components
Data Product - the unit of trust in the mesh
Data
Metadata
Code
Policies
A named owner
One team, one accountable Data Product Owner.
Discoverable
Registered in the data product catalog.
Value-driven
An explicit value proposition: what problem it solves, who consumes it, and why.
Built on the SDM
Reusable and interoperable through the common language.
Shared via Contract
Explicit claims between producer and consumer.
Trustworthy
Security and compliance rules built in from the start.
Context
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Data Products – types, usage, conformity
SOURCE SYSTEMS
Source-aligned
Closest to the operational systems — owned where data is born.
→
Aggregated
Combining entities into broader, reusable views.
→
Consumer-aligned
Data gains business meaning at every step.
SDM CONFORMITY LEVEL FOR DATA PRODUCTS – BASED ON PURPOSE
GOLD core SDM conformance
SILVER non-analytical purposes
BRONZE analytical purposes
CONSUMER
Analytical purposes
Non - analytical purposes
Consumer-aligned
Shaped for a specific consumption purpose.
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Architecture & Technology - two platforms: one for data, one for meaning
ONE DATA PLATFORM (ODP)
Domain- and use-case-agnostic: the same road for every team.
COMPASS
METADATA & CONTEXT MGMT. PLATFORM
Ownership · Schema
findable & described — Level 1
Quality and Compliance rules
trusted & contracted — Level 2
Semantics · Taxonomy · Ontologies
semantically navigable — Level 3
ODPS & ODCS to govern at scale and provide e2e Data Observability & Mesh Health
Changing the airplane engine�while flying
Transforming to Data Mesh
while going from on-premise to a hybrid Data platform
while enabling an AI-first company
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Opportunities and trade-offs in challenging times�
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Opportunities
Enablers:
Trade–offs accepted during Migration
Business continuity beats purity
Lessons learned from our Journey
What worked and what needed improving
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Mirela Navodaru | Data Mesh Live 2026 | C2 General
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Top Management commitment
A concrete AI-first ambition settles every priority fight.
Governance
Maintain discipline and interoperability in the decentralization
Stay flexible and adapt
Reality check, identify opportunity, stay strategic
Operating model First
Early clarity on who does what, and why, fill the gaps in skills & culture
Quicker in making decisions
Try, learn, iterate: a good-enough decision today beats a perfect one next quarter.
Success
Improving
Make technology easy to use
Self-service and automation for the technical enablers
Things to take home
Key Takeaways and Conclusion
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Key Takeaways
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One
Data Mesh is the foundation for AI-readiness
Data Product Ecosystem with clear ownership and governance to produce and exchange data for humans and AI.
Two
There is no such thing as a green field
Transform while operating. Brownfield is not the obstacle to the strategy, find the right balance of governance and flexibility
Three
Opportunities in challenging times
Don’t just find better ways to reach your objectives , but let the shift set new and bolder ones.
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Conclusion
In the Agentic era, People must remain in the Lead of Design and Architecture for the AI-readiness foundation to avoid
Garbage – In,
Danger - Out
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By making Data and AI everyone's business,
We contribute to a safer AI future.
Mirela Navodaru�Enterprise and Solutions Data Architect
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Thank you
for your attention! �