For one, if you collect the data now you can always apply the "science" later--as long as you have the data and can query it, you're always ready. This alone means that collecting data now could be useful even if you have no idea of what to do with it. Storage is relatively cheap, and you--or somebody else--might come up with a clever way to analyze it in the future.
Additionally, I think the author underestimates the potential of machine learning. I am by no means an expert in the field (I'm taking an AI class--that has to count for something! ;) I hope) but even the simple techniques we've covered can get some interesting information without much domain knowledge involved. As ML evolves, I suspect there will be more and more technology that can find interesting trends and relationships in data regardless of what the data is actually modelling. The article does mention ML a bit, but I think it will be much more significant in the near future.
Ultimately, figuring out what questions to ask about data and harnessing human curiosity will give you much more than just hoarding data. I just think it's possible that sufficiently abstract and generic approaches in the near future will make it easier to get similar--or perhaps orthogonal--results using techniques from ML and AI. I firmly believe that hoarding the data without doing anything to it is still much better than not collecting it at all.