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Most supply chain leaders think they have a data problem. What they actually have is an architecture problem. Supply chains already generate enormous amounts of data, and capturing it is hugely important. But more data does not automatically create more value. The key idea is that data architecture is what determines the return you can extract from it. There are many ways to think about this architecture. For example: • Event-driven architecture instead of relying entirely on batch processes. • Proper data contracts between producers and consumers. • A unified semantic layer so all terms mean the same thing to everyone. • Governance and data access controls that make data usable. • Data products and APIs so teams consume reliable capabilities. These choices all have economic consequences. You see, good architecture reduces data movement, duplication, and pipeline maintenance. It also lowers the marginal cost of building new data products. The value of data therefore needs to be explicitly connected to business outcomes, and with supply-chain data, the economics get more interesting because there's opportunity even outside the company. When supply-chain partners share standardized data, it produces operational benefits such as reduced buffers, less reconciliation, and lower decision latency. It could also mitigate the distortions driving the bullwhip effect. And so, designing data systems is really an operating-model endeavour. The goal isn't to collect more data. It is to build an architecture where the right data reaches the right system, person, or partner at the right moment, and influences what happens next. That's when data infrastructure stops being a cost centre and becomes an operational flywheel.