Not in biotech, but have worked with people who use a tremendous amount of excel, especially in biology. A lot of the people still using Excel either came befor

Not in biotech, but have worked with people who use a tremendous amount of excel, especially in biology. A lot of the people still using Excel either came before bioinformatics libraries got really really good, had to basically teach themselves how to use a computer to deal with bio data, worked in a lab that had protocols in place for data that used Excel, and/or haven't gotten around to teaching themselves R. You have to keep in mind that Excel is extremely good at being a visual representation of data. R, Python (only really adopted in recent years), and Matlab (which I don't see too often in bio) are all excellent for manipulating the same data as Excel, but if you wanted to quickly scroll through your RNA sequencing counts, Excel provides a fast, (almost) out-of-the box way to get a "big picture" view of your data (I haven't met anyone doing bioinformatics in SAS or STATA, but I know people who have used those software in academia for other fields). Even if no one is directly manipulating the data in Excel, if you don't import the data correctly, or forget to _not_ save the data, you'll end up fucking it with Excel's auto-formatting. These subtleties lead to things as mentioned in the article, but aren't things that the ordinary person learns except through mistakes. Nobody is born knowing what tools to use or how to use them, but Excel is one of the first pieces of software that deal with data for a lot of people, and as such one of the first things they turn to when faced with a new problem.