Need data preparation cheatsheet / guidelines / first principles to train team members! Really ran out of ideas and hence such a basic question to the community -
Despite the repeated emphasis on ensuring data accuracy/validity, team members just do not spend enough time on it because they are in a hurry to develop models and get results. One would imagine that once you point out massive errors/misses in their data - the first time, second or third time - they would adapt. But it is really hard to get them to change their behavior. And this problem is not limited to only one or two people - you see this happening across the board.
As an alternative, thinking of sharing some sort of exhaustive checklist that each team member has to run through every time before starting the analysis. But not sure how effective it would be and given the open-ended nature of the EDA if it is even possible to make a good checklist. A couple of hours on internet did not throw up anything very relevant.
Please can you point me to something that you may have come across or you yourself follow. Also, any other ideas on how to tackle this problem will be helpful.

There is a reason that no such cheat sheet exists.  The hardest data problems are regime-specific.  While some issues arise universally, the reason why data is messed up is because of its lifecycle.  Who or what created it?  How was it put together?  How is it stored?  How did it change between the time it was created and the time it was received?  Those are the hard questions that statistical analysis of the data received usually cannot answer.
The most important advice in general is to ask the right questions, along the lines of what I described above.  Know the data subject matter expert, and get a solid understanding of the data lifecycle.  If you can give some specific information on the data you are talking about, I will edit this answer to give some more specific guidance.
