I don't know. How can you gauge the effectiveness of your model without good data? A model can be wrong, but it can also be changed/redone/rebuilt. Without data you can neither develop nor verify imho.
It gives you a working MVP and a lower bound on what's possible. Tnen you can iterate either on model or data or both - data can also be further cleaned, changed and expanded. How can you gauge ROI of your data cleaning/collection efforts without it? Maybe an extra day you've put into it made a difference, maybe it didn't and you'd have better spent it on modelling or other tasks.