In general, task paralellism can model anything data parallelism can and given "a sufficiently smart compiler" you can end up with the same result. This is an informal corollary of Needham's Duality (which is itself informal, so make of it what you will).
Our current hardware tends to offer great data parallelism for homogenous task queues and task parallelism for heterogenous task queues. Given that, we task parallelism needs a lot more consideration from a human. It's also the case that our current implementations of data parallelism tend to focus on shared memory computations, so their scope is a lot more limited than the distributed-system-conflated discipline of task-oriented concurrency, where we're currently having an explosion of engineering.
For a "sufficiently smart compiler" you mean autovectorization. That's a hard problem.
I've mentioned before that I don't think SIMD is going away anytime soon. It has so many upsides (cache locality, simple implementation in hardware due to the single-instruction nature of it) that I think designs that don't take advantage of it will always be at a disadvantage for the foreseeable future.
Our current hardware tends to offer great data parallelism for homogenous task queues and task parallelism for heterogenous task queues. Given that, we task parallelism needs a lot more consideration from a human. It's also the case that our current implementations of data parallelism tend to focus on shared memory computations, so their scope is a lot more limited than the distributed-system-conflated discipline of task-oriented concurrency, where we're currently having an explosion of engineering.