Why do teams report different numbers?
Start by comparing metric grain, source authority, time windows, exclusions, and ownership. A shared dashboard cannot fix conflicting definitions.
TRAVINTO / DATA ARCHITECTURE
Make the data model
work for the business.
Enterprise data architecture connecting sources, business entities, history, quality, and reusable data products for analytics and AI.
THE DECISIONS BEHIND THE ARCHITECTURE
Start with the question.
Make the responsibility clear.
Should we choose a warehouse, lakehouse, or streaming platform?
Begin with workload needs: freshness, concurrency, history, data shape, access, and operating capability. The architecture may use more than one serving pattern.
What makes data ready for AI?
Accessible records are not enough. AI needs permission-aware context, reliable identities, useful metadata, source versions, quality evidence, and clear meaning.
WORK AREAS & TYPICAL DELIVERABLES
Understand the scope.
See what it should produce.
These are engagement directions; selected responsibilities and deliverables are agreed for the problem in hand.
Domain models & business meaning
Scope & typical output
Define conceptual entities, logical relationships, grain, keys, and the conditions under which a record or metric applies.
Typical output: A domain model, glossary, and decision log.
Source contracts & integration
Scope & typical output
Define authoritative sources, refresh requirements, schema behavior, exception handling, and reconciliation across APIs, batch, CDC, events, and documents.
Typical output: Source contracts, interface boundaries, and ingestion decisions.
History & curated data products
Scope & typical output
Plan raw evidence, validated records, curated products, effective dates, late changes, and business-approved transformations.
Typical output: Product contracts, history rules, and quality requirements.
Semantics & consumption
Scope & typical output
Connect BI measures, applications, APIs, retrieval indexes, and model features to stable definitions and permitted use.
Typical output: A serving model, semantic definitions, and consumer acceptance criteria.
Platform & operating decisions
Scope & typical output
Map logical responsibilities to physical services with workload isolation, observability, recovery, lifecycle, and cost ownership.
Typical output: Target architecture, non-functional requirements, and operating responsibilities.
Migration & architectural evolution
Scope & typical output
Track dependencies and source-to-target semantics; validate changes with representative records and business cases.
Typical output: A transition roadmap, mapping rules, and reconciliation evidence.
A CONNECTED REFERENCE VIEW
See the responsibilities.
Follow the dependencies.
BUSINESS USE
Reporting · Applications · AI knowledge · Domain decisions
SERVING & SEMANTICS
Metric grain · Product contracts · APIs · Access scope
CURATED DOMAIN DATA
Entities · Relationships · History · Quality rules
SOURCE & INTEGRATION
Systems of record · Events · Documents · Ingestion contracts
Retail example / stock is not one number
The domain makes
the architecture concrete.
A merchandising report, an online availability answer, and a warehouse view may use different stock states. Model on-hand, reserved, in-transit, and available-to-promise quantities with location, units, and event time. Then define which consumer can use each view.
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WHAT A USEFUL ASSESSMENT CAN LEAVE BEHIND
A shared view.
A practical next decision.
01
Conceptual, logical & physical views
02
Source-to-consumer dependency map
03
Domain product & metric contracts
04
Quality, access & history rules
05
Target-state & transition roadmap
A USEFUL NEXT STEP
Bring one disputed metric or fragmented domain.
Bring a representative workflow, existing definitions or controls, permitted source examples, and the people who own the result.
Discuss your priorities ↗