Applied Digitalisation · Module 4
Data models and mapping
A shared data model keeps systems aligned.
Previously
APIs and system integration
APIs are the contracts that keep systems aligned.
This module
Data models and mapping
A shared data model keeps systems aligned.
Next
Operations, monitoring, and observability
telemetry is essential for safe operations.
Progress
Mark this module complete when you can explain it without rereading every paragraph.
Why this matters
A source system stores “status” as free text.
What you will be able to do
- 1 Explain data models and mapping in your own words and apply it to a realistic scenario.
- 2 Mapping is how you move from local meaning to shared meaning without losing truth.
- 3 Check the assumption "Mappings are maintained" and explain what changes if it is false.
- 4 Check the assumption "Differences are recorded" and explain what changes if it is false.
Before you begin
- Foundations-level vocabulary and concepts
- Confidence with basic diagrams and section terminology
Common ways people get this wrong
- Translation drift. Meanings drift over time. Without review, systems disagree.
- False equivalence. Some fields are not equivalent. Pretending they are creates harm.
Main idea at a glance
Schema mapping
Translate once and reuse everywhere.
Stage 1
Source schema
The raw structure of data as it exists in the source system. Field names, types, formats, and business rules as the source understands them. Often undocumented, sometimes inconsistent, always unique to that system.
Every mapping is a decision about meaning. Document them or lose traceability.
A shared data model keeps systems aligned. schema mapping is where meaning is preserved or lost.
Mapping is a design choice. It decides what is essential, what can be dropped, and what must stay consistent across services.
Worked example. The mapping that “looked right” and quietly rewrote history
Worked example. The mapping that “looked right” and quietly rewrote history
A source system stores “status” as free text. The canonical model uses a strict enum. A mapper converts unknown values to “other” to keep the pipeline running. It feels helpful until you realise you have destroyed the ability to answer “what really happened” later.
Common mistakes in mapping
Mapping anti-patterns
These issues silently degrade data quality over time.
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Mapping by field name only
Map by semantic meaning, units, and constraints, not labels alone.
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Dropping unknown values silently
Quarantine unknowns and correct upstream instead of hiding evidence.
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Skipping mapping tests
Regression tests are required to prevent accidental semantic drift.
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Missing model stewardship
Canonical models need named stewards to keep definitions stable.
Verification. Mapping checks that prevent nonsense
Mapping quality checks
Run these checks on every mapping release.
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Coverage
Measure the percentage of records that map cleanly.
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Exceptions
List unknown values for review, do not suppress them.
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Round-trip traceability
Ensure mapped outputs still explain the original source meaning.
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Versioned change history
Track mapping rule versions and associated change rationales.
Reflection prompt
Name one field that should never be silently “best guessed”. What should the system do instead when it cannot map it safely.
Mental model
Models and mapping
Mapping is how you move from local meaning to shared meaning without losing truth.
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1
Local model
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2
Mapping
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3
Shared model
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4
Use
Assumptions to keep in mind
- Mappings are maintained. Mappings rot unless someone owns them.
- Differences are recorded. If you hide differences, you create future failures.
Failure modes to notice
- Translation drift. Meanings drift over time. Without review, systems disagree.
- False equivalence. Some fields are not equivalent. Pretending they are creates harm.
Key terms
- schema mapping
- The rules that translate fields from a source schema into a shared model.
Check yourself
Quick check. Models and mapping
0 of 7 opened
Why use a canonical model
It reduces translation work and keeps meaning consistent across systems.
What is schema mapping
Rules that translate source fields into a shared model.
Scenario. A mapper converts unknown values to “other” to keep the pipeline running. Why is that risky
It hides truth and destroys evidence. You should surface unknowns, quarantine when needed, and fix upstream rather than silently rewriting history.
What breaks when mappings drift
Dashboards and services start reporting different truths because definitions no longer match.
Why document mappings
So changes are traceable, testable, and safe for downstream consumers.
What should mapping decisions consider
Business meaning and units, not just field names.
When should you version schemas and mapping rules
When changes affect downstream consumers or alter meaning.
Artefact and reflection
Artefact
A one-page decision note with assumption, evidence, and chosen action
Reflection
Where in your work would explain data models and mapping in your own words and apply it to a realistic scenario. change a decision, and what evidence would make you trust that change?
Optional practice
Map a source schema into a shared model and check coverage.