I think the "data of decisions" point is probably the most under-appreciated. As my stats professor in grad school put it, "The first step is to count something."
Very true. And I think that's the point that is probably best addressed by data strategy as it isn't being addressed elsewhere. I edited the post to make clear I'd address it in future posts.
I've been thinking about this in the context of causal inference - poor, directional data is too often relied upon to imply causality where none exists. Curious to hear other examples as well.
I think the "data of decisions" point is probably the most under-appreciated. As my stats professor in grad school put it, "The first step is to count something."
Very true. And I think that's the point that is probably best addressed by data strategy as it isn't being addressed elsewhere. I edited the post to make clear I'd address it in future posts.
I've been thinking about this in the context of causal inference - poor, directional data is too often relied upon to imply causality where none exists. Curious to hear other examples as well.