Ask HN: What's the most tedious task in Data Science?

I've done my share of Data Science projects in the past and have my opinions about which parts are interesting and which parts 'just have to be done' to get there. Generally the tedium is concentrated on cleaning/joining/figuring out irritating issues in the data. What specifically is the lowest common denominator of this problem in your opinion?