Do the following terms mean anything to you?
load balance toggle join index normalize key
If you are a statistician and aspiring data scientist they should. If not this is one area where you should expand your knowledge base. In her article 'Being a data scientist is as much about IT as it is analysis' Carla Gentry explains why.
"With knowledge of the client's IT setup from a data management/quality perspective, you'll be equipped to handle most situations you run into when dealing with data, even if the Architect and Programmer are out sick. Your professional knowledge is going to be a big help in getting the assignment or job complete."
This reminds me of an article I read not long ago about building data science teams:
"Most of the data was available online, but due to its size, the data was in special formats and spread out over many different systems. To make that data useful for my research, I created a system that took over every computer in the department from 1 AM to 8 AM. During that time, it acquired, cleaned, and processed that data. Once done, my final dataset could easily fit in a single computer's RAM. And that's the whole point. The heavy lifting was required before I could start my research. Good data scientists understand, in a deep way, that the heavy lifting of cleanup and preparation isn't something that gets in the way of solving the problem: it the problem."