Think Big

Thinking big is refusing to model as if the feature in front of you were the only thing happening. In any real data landscape there is a great deal going on at once. Beyond building new functionality and adapting existing data flows to new business requirements, there are always major parallel processes you have to account for: new data sources, changing data sources, company mergers (integrating models and sources), migrations, platform changes driven by things like sovereignty requirements, highly volatile teams, offshore and near-shore teams, shifts in methodology, data mesh, data products. A model that ignores them is correct on paper and wrong in practice. Thinking big is not the opposite of decomposition; it is what tells you what to decompose. You hold the whole landscape in view — all the moving processes — and then break the work into parts that still fit that bigger picture. Miss the scale and you optimize one flow while a migration or a merger quietly invalidates it. Handling all of this at once requires being genuinely planmatig — systematic, structured, intelligent, focused and dedicated. That is a different way of working, and it is the reason Breakthrough Modeling is also, honestly, extreme modeling: modeling under real, concurrent complexity rather than in a tidy demo.