i think this is a very important point - and basically refutes the hypothesis we are in a data scientist bubble.
Right now the term 'data scientist' is far too generic for me and covers so many bases. I graduated university with a strong stats background however it has been difficult for me to transition into solid data science roles due to my lack of programming (which im thankfully improving now) where as i think there is a big risk of some bad data science being done by people who can start messing with data without a solid grounding on what it means for data to be skewed, checking for kurtosis etc etc. For me personally i always knew what i wanted to do with the data, but getting it and cleaning it up was another battle completely. It might be that small discrete data science teams can work together just like any scientific lab would with a technician, professor etc in a bit more of an academic fashion but running under lean methodology
The problems will come from (i anticipate) data scientists who are employed by big corporates because 'we need a data scientist' and HR basically dont understand what they are hiring for or why.
Its more of less on the data science community to self police this - which will likely be the case.
