Not bs. But there is both a real GIGO problem, and a problem with under specification. It's certainly easy to propose DS analysis that are unlikely to have much return.

Thinking "data science is hot, we should do that" is different than "we have all this data and don't understand what it means". The latter is more likely to lead somewhere interesting.

10% alpha 90% useless data visualizations so management can pretend to be data driven.

No.

But (and this is a Big But), the value of data science comes at the end of the data journey. Businesses need to be capturing data that is relevant and accurate before they can start analyzing it and deriving any value.

My experience with clients is that they get a ton of value out of that first step of thinking about what information they want to collect about their customers, then actually collecting it (or, conversely, surfacing what they already collect in a meaningful way). So while they come in wanting some kind of neural network powered prediction engine or whatever, they are often really impressed by pretty basic dashboards about their customer behavior.