I liked the extension of your taut-slack constraints to the theory-date setting. I think you are correct that people are still working though that shift.
″ Data is now very cheap, so consume a lot of it and see what happens.” is a bit more problematic to me. There certainly is a lot of truth to the old saying, there is no seeing without looking. In one sense the data is cheap—it is just there and in many ways not an economic good any longer.
However, the act of consuming the data is still costly for most of us. As romeo notes, when we are wondering though the fields on our unknown unknowns it looks very random (I also attributed that idea to you) so how do we get any patterns to emerge.
While part of the pattern recognition stems form some underlying theory, new patterns will be found as one starts organizing the data and then the pattern can start to be understood be thinking about potential relationships that explain the connections.
There was a online tool someone here mentioned a year or so back. Totally forgetting what the name, basically it was a better set of note cards for information bits than then could be linked. You get a nice graph forming up (searchable I believe on edges not merely phase/subject/category/word). If that were a collaborative tool (might be) that might be a slack constraint for bringing up unseen patterns in the data (reducing that cost of consuming). The edges might be color-coded and allow multiple edges between nodes based on some categorization/classification of the relationship, then filtering on color (though might also be interesting to look at possible patterns in the defined edges too).
However, the act of consuming the data is still costly for most of us. As romeo notes, when we are wondering though the fields on our unknown unknowns it looks very random (I also attributed that idea to you) so how do we get any patterns to emerge.
While part of the pattern recognition stems form some underlying theory, new patterns will be found as one starts organizing the data and then the pattern can start to be understood be thinking about potential relationships that explain the connections.
There used to be an exhibit at Epcot on “the pattern of progress” which I think pointed to the same thing you’re pointing to here. There’s a short video from it which I really like; it breaks “progress” down into a five-step pattern:
Seeing—i.e. obtaining data
Mapping—organizing the data and noticing patterns
Understanding—figuring out a gears-level model
Belief—using the model to make plans
Action—actually doing things based on the model
Breaking things into steps is always a bit cheesy, but I do think there’s a valuable point in here: there’s an intermediate step between seeing the data and building a gears-level model. I think that’s what you’re pointing to: there’s a need to organize the data and slice it in various ways so you can notice patterns—i.e. mapping, in the colloquial sense of the word.
Does that sound right?
There was a online tool someone here mentioned a year or so back. Totally forgetting what the name, basically it was a better set of note cards for information bits than then could be linked.
Yes. At some level we need to have some type of theory to start moving the data into different piles which we can compare. But if we’re theory constrained we don’t see how to put any order on the data—it’s not even information at that point; it’s that random noise.
But clearly we do find ways to break out of that circle.
When the constrain is the data then intermediate constraints between data and theory are probably not as obvious, the data is not as overwhelming.
I liked the extension of your taut-slack constraints to the theory-date setting. I think you are correct that people are still working though that shift.
″ Data is now very cheap, so consume a lot of it and see what happens.” is a bit more problematic to me. There certainly is a lot of truth to the old saying, there is no seeing without looking. In one sense the data is cheap—it is just there and in many ways not an economic good any longer.
However, the act of consuming the data is still costly for most of us. As romeo notes, when we are wondering though the fields on our unknown unknowns it looks very random (I also attributed that idea to you) so how do we get any patterns to emerge.
While part of the pattern recognition stems form some underlying theory, new patterns will be found as one starts organizing the data and then the pattern can start to be understood be thinking about potential relationships that explain the connections.
There was a online tool someone here mentioned a year or so back. Totally forgetting what the name, basically it was a better set of note cards for information bits than then could be linked. You get a nice graph forming up (searchable I believe on edges not merely phase/subject/category/word). If that were a collaborative tool (might be) that might be a slack constraint for bringing up unseen patterns in the data (reducing that cost of consuming). The edges might be color-coded and allow multiple edges between nodes based on some categorization/classification of the relationship, then filtering on color (though might also be interesting to look at possible patterns in the defined edges too).
There used to be an exhibit at Epcot on “the pattern of progress” which I think pointed to the same thing you’re pointing to here. There’s a short video from it which I really like; it breaks “progress” down into a five-step pattern:
Seeing—i.e. obtaining data
Mapping—organizing the data and noticing patterns
Understanding—figuring out a gears-level model
Belief—using the model to make plans
Action—actually doing things based on the model
Breaking things into steps is always a bit cheesy, but I do think there’s a valuable point in here: there’s an intermediate step between seeing the data and building a gears-level model. I think that’s what you’re pointing to: there’s a need to organize the data and slice it in various ways so you can notice patterns—i.e. mapping, in the colloquial sense of the word.
Does that sound right?
Possibly Roam?
Yes. At some level we need to have some type of theory to start moving the data into different piles which we can compare. But if we’re theory constrained we don’t see how to put any order on the data—it’s not even information at that point; it’s that random noise.
But clearly we do find ways to break out of that circle.
When the constrain is the data then intermediate constraints between data and theory are probably not as obvious, the data is not as overwhelming.
Yes, Roam was it. Thanks!