Every system I have ever fixed was broken at the data layer first. Fields nobody defined, duplicates nobody owned, exports nobody trusted. Data architecture is the discipline of deciding what things mean before deciding what software does.
My school was Salesforce at enterprise scale: designing object models that six subsidiaries could share, migrating years of legacy records, building integrations where one wrong mapping corrupts thousands of cases. Certified in data architecture, tested in migrations at 02:00.
The principles are stable across every stack: model the business, not the UI. Name things the way the company speaks. Make ownership explicit — every field needs a person who answers for it. Design for the report you will need in two years, not the demo next week.
Today this thinking runs through everything we build — from DocuVisio, where unstructured documents become structured data, to mortgage platforms where a clean data spine is the difference between fintech and theatre.
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