There are always at least two ways to interpret the question you need to answer or the decision you need to make.
Sometimes more.
And when someone from the wet lab talks to someone from the data team, they’re probably thinking of different ones.
How you fix the data collection issues depends on the types of problems you’re trying to avoid.
What project you prioritize depends on what tools you have and the perceived chances of success.
How you define the scope of a project depends on which of the problems it’s solving is most important.
These things seem so natural and obvious, it may not feel worth it to call them out.
The problem is, obvious assumptions depend on the mental models used to infer them.
And your lab and data teams probably have very different mental models.
Which means they’re trying to answer different questions.
So it shouldn’t be surprising that they come up with different answers.
Maybe calling out what you’re taking for granted is worth it after all.