Diving deep into a domain and interfacing with a subject matter expert goes a long, long, way. We've been building custom data products for enterprise for about seven years, and a lot of the work is listening and grokking large amounts of content about whatever domain we were helping with in general, and the specifics of our clients. Retail, banking, telcos, energy, communication.

Having a background in acoustics, reservoir characterization, telecom networks, opens up clients because you 'get it' or at least you work hard to get it, which improves buy-in of the experts to sit down with you and answer your questions. You did your homework.

If you don't and just storm in talking about something something neural nets, they'll see it as a waste of time, won't bother explaining nuances, will delay sending data you desperately need. You won't have their cooperation even if you have executive support. There's no data in CSV form or an API to hit in most real world projects, so you need their help getting data, and their expertise to understand it.

Another major point is specifying the metrics. The real world metrics, not AUC or F1 scores. You need collaboration to get there, too.

There's so much to be done before there's data to work with, let alone good data. And there's so much after the model building step.

It can drive people to quit. One reason is that when you storm in and consider that people are morons, you get frustrated rapidly.