- You can do the Named Entity Tagging based on the categorical data (e.g. columns that are Text/Strings with low-ish relative cardinality would make good candidates to filter out text fields with for example email addresses (which shouldn't be in a DWH in the first place as categoricals))
- FLOATs/decimals/Integers would be good candidates for values that somebody looks for (and the name of the column would be the 'trigger' of the query.
All in all, with a bit of logic, good OLAP design and a lot of up front configuration I got in a weekends time to answer basic questions like 'revenue in the US in 2016' using NLTK back in the day. Today I would probably give spaCy a try as NLP engine.
Though they feel abandoned, and there hasn't been much recent activity around them. Microsoft probably has all speech engineers working on Cortana instead. (Though I'd be surprised if she's not using .NET at some level.)
Microsoft cognitive services
But NLP based question answering is an unsolved problem and the best way to approach it is ensemble approaches.