In my company, the biggest issue is finding the right data sets in company's vast data landscape, figuring out the exact definition/meaning of each column etc. Then, it's dealing with the data quality issues. Then, it's getting access to it and setting up an ingestion job for the data to be copied to some common storage (e.g. Hadoop). At the very end, it's the actual data science. I suspect a lot of the PhDs we hire start drinking before they reach the data science stage :)