A model is a view of how the world works. It's a way of organizing and making sense of the things that you experience. The more models you know, the more nuanced you can make sense of the experiences you encounter. In a sense the different worldviews and theories of the different philosophers can serve as models to make sense of the challenges we encounter in organization putting their data to work.
Studying philosophy provides ways to interrogate reality that we can apply in business as well. It allows us to get to the core of the matter very quickly and to see if the conclusion follows from the assumptions.
Critical minds will wonder what the return on investment of this kind of intellectual playfulness is, and why not just dive in and build stuff. A valid critique and dwelling on analysis and theorizing is never worth it. Indeed, start building and try to generate value for your business.
But don’t wait too long to take a step back to validate your assumptions, to see if they are really taking you where you want to go. Otherwise, you risk investing in something that’s exciting and fun to build but doesn’t contribute to the bottom line.
Another trait of philosophy that is crucial when you work with data, and is closely related to the previous points, is the emphasis on asking the right questions. An answer you dig up from your data performing analytics is only valuable if it is the answer to the right question.
So, putting a bit more time in getting to the right questions is time well spent. Make sure that the effort you put in climbing the ladder is put to good use by putting the ladder against the right wall.
We are aware that some of the examples are rather superficial, but then again, we are not conducting a philsophical study, we are merely trying to inspire and provide another approach to data projects.
In practice we don’t use explicit reference to philosophy in our data strategy work, but it is always present in the background when we conduct our leadership alignment sessions and during the interviews and workshops to design the data strategy roadmap.
Trying to uncover the implicit assumptions, get to the right questions and making sure the story makes sense.