A system that supports OLAP/ad-hoc queries is going to need a ton of IOPs & probably also CPU capacity to do your data transformations. If you want this to also scale beyond the capacity limits of a single node, then you're going to run into distributed joins and network becomes a huge factor.
Now, to support OLTP at the same time, your big, distributed system needs to support ACID, be highly fault-tolerant, etc.
All you end up with is a system that has to be scaled in every dimension. It needs to support the maximum possible workloads you can throw at it, or else a random, expensive reporting query is going to DOS your system and your primary customer-facing system will be unusable at the same time. It is sort of possible, but it's going to cost A LOT of money. You have to have tons and tons of "spare" capacity.
Which brings us to the core of engineering -- anyone can build a system that burns dump trucks full of venture capital dollars to create the one-system-to-rule-them-all. But businesses that want to succeed need to optimize their costs so their storage systems don't break the bank. This is why the current status-quo of specialized systems that do one task well isn't going to change. The current technology paradigm cannot be optimized for every task simultaneously. We have to make tradeoffs.