Data Science: Reality Doesn’t Meet Expectations
Execs frequently ignore data science research when making decisions.
Data is often dirty, or insufficient to make decisions about majority of the users. Sometimes the infrastructure is poor - SQL queries take hours.
Data Scientists are usually the only ‘data person’ on the team. Tons of request from teams, and most of the work is repetitive and ‘easy’.
Measuring impact is hard - especially on dollars that were hypothetically saved but never spent.
There are morally questionable applications, e.g. Uber’s Greyball that identified law enforcement in cities, showed the riders, but no one would pick them up.
References
- Data Science: Reality Doesn't Meet Expectations. dfrieds.com . Apr 7, 2020.
Follow Ups
- Data Science: Reality Doesn't Meet Expectations | Hacker News. news.ycombinator.com . Apr 7, 2020.
- Why do 87% of data science projects never make it into production? venturebeat.com . Jul 19, 2019.