I think another thing that could contribute to a crash is companies hiring data scientist roles without really being sure what type of problems they need to have them solve. "We have TBs of data, maybe they can turn it into money." However if the 'data scientists' weren't/aren't even involved in what type of data to collect to solve certain problems (or the problems to solve aren't even known), it makes for very ambiguously drawn up goals and expectations.
Also, depending on the political priorities of the organization, data science may not even be really used. Executives/management may look for analysis results to support their ideas, and just throw out the ones that don't align with what 'they already knew to be right.' After all, who wants to be proven wrong?
EDIT: One anecdote -- I worked for a company and showed pretty plainly that the length of customer engagement had fallen since the previous year. My boss basically said "why did you point that out?" because it made them look bad to the owner of the business.
Also, depending on the political priorities of the organization, data science may not even be really used. Executives/management may look for analysis results to support their ideas, and just throw out the ones that don't align with what 'they already knew to be right.' After all, who wants to be proven wrong?
EDIT: One anecdote -- I worked for a company and showed pretty plainly that the length of customer engagement had fallen since the previous year. My boss basically said "why did you point that out?" because it made them look bad to the owner of the business.