If you enjoy making sure your peas don't touch the carrots, then sure, strong typing is great in the data space. But when you get woken up because a spreadsheet comes through as 11.0 rather than 11 or you have to type
Double.parseDouble(x) == Double.parseDouble(y)
/* instead of pythons */
x == y
27 times to get a feed file parsed, then I'd say that the tools are lacking basic useability. And especially primitive in light of the handwriting reading, photo tagging, go playing,supermario winning possibilities of ML.
That means you don't have a clean "domain model" (or business logic layer) and a data filtering layer that creates the domain objects. You should apply business logic on the pure objects/entities, and you should make sure that the filtering handles the representational problems (parsing, data integrity, partial data, and so on).
And ideally if something makes it through the data filtering layer into the logic layer and does not make sense there, then that should be handled. And that's where strong types help. It forces you to handle these cases, even if that means logging/alerting/ignoring, but at least you'll have to make a decision when you write the logic, instead of 3AM in the morning.
