Sometimes it can't be cleaned up, or the process of cleaning it up takes too long or is more expensive than the company wants to spend. Sometimes the clean up p

Sometimes it can't be cleaned up, or the process of cleaning it up takes too long or is more expensive than the company wants to spend. Sometimes the clean up process is error prone, or leaves you with too little useable data. Sometimes the data really is too noisy and no amount of clean up is really possible. It's definitely true that data clean up is a problem data science can address, but it's not a magic wand.