Let me tell you about the time we built a data warehouse and dashboards to discover our red-yellow-green dot system could be reduced to just red and green.
Seriously though, the best analytics project I ever worked on was designing a data model to understand and improve the efficiency of a state court system.
Let me tell you about the time we built a data warehouse and dashboards to discover our red-yellow-green dot system could be reduced to just red and green.
Seriously though, the best analytics project I ever worked on was designing a data model to understand and improve the efficiency of a state court system.
Questions like 'with case types of X, are certain judges consistently more efficient than others?' and 'how fast on average does each judicial district clear cases?'
Lots more I can't remember, but it was fascinating stuff and no one had done anything like it at the time. The guy in the state court system spearheading it was a pioneer. Designing that data model was a blast.
ah, i love this. this is the sort of stuff that I think is fascinating, both as a data thing and as a “look at this interesting fact about how the world worlds” thing. (It also seems like this is how most “insight” happens - not by doing complicated analysis, but by looking at something that’s never been looked at before.)
In my experience, a lot of insights happen like many scientific discoveries: by accident. Someone notices something possibly interesting (or asks an interesting new question, or looks at a problem from a new perspective), does a little bit of digging, and discovers a little (or big) treasure. As if they noticed some extra gold in a portion of the river, did some digging upstream, and found a vein.
Meanwhile, the complicated analyses manage to unlock another 0.005% of additional revenue -- or was that just a gust of wind? Or a new product release? Or they start digging all over the place (wherever there is a little more gold than average) in the hope of finding another vein. Trying to use science in order to repeat the successes of the past, instead of venturing into unknown territories in order to discover new treasures (since science doesn't know anything about the unknown, it cannot really help us in unknown territory anymore than a good sense of observation can). Or tip-toeing into new territories using A/B tests, as if that's how anyone ever discovered anything. When's the last time an "optimized" thumbnail on Netflix got you to watch something you didn't want to watch? It's like changing the cover of a book I guess... maybe it can increase sales by half a percent. Maybe. And then if we discover some kind of hidden formula, we can just make all of them look the same. But are we still increasing sales by that point? Insights quickly become trends, and by then any competitive edge is lost.
It seems like going from no data to some data is what provides the greatest insights we will ever get. Beyond that, it's mostly just small optimizations/diminishing returns. Because if there was something obvious hidden in the data, just waiting to be discovered, wouldn't we have discovered it when we analyzed the data for the first time? (if we did so thoroughly)
So maybe that's what we should focus on: what do we _not_ have data about? Or what data have we never really analyzed (or even looked at)? Or maybe new ways to look at the data we already have.
Let me tell you about the time we built a data warehouse and dashboards to discover our red-yellow-green dot system could be reduced to just red and green.
Seriously though, the best analytics project I ever worked on was designing a data model to understand and improve the efficiency of a state court system.
Questions like 'with case types of X, are certain judges consistently more efficient than others?' and 'how fast on average does each judicial district clear cases?'
Lots more I can't remember, but it was fascinating stuff and no one had done anything like it at the time. The guy in the state court system spearheading it was a pioneer. Designing that data model was a blast.
ah, i love this. this is the sort of stuff that I think is fascinating, both as a data thing and as a “look at this interesting fact about how the world worlds” thing. (It also seems like this is how most “insight” happens - not by doing complicated analysis, but by looking at something that’s never been looked at before.)
100%
In my experience, a lot of insights happen like many scientific discoveries: by accident. Someone notices something possibly interesting (or asks an interesting new question, or looks at a problem from a new perspective), does a little bit of digging, and discovers a little (or big) treasure. As if they noticed some extra gold in a portion of the river, did some digging upstream, and found a vein.
Meanwhile, the complicated analyses manage to unlock another 0.005% of additional revenue -- or was that just a gust of wind? Or a new product release? Or they start digging all over the place (wherever there is a little more gold than average) in the hope of finding another vein. Trying to use science in order to repeat the successes of the past, instead of venturing into unknown territories in order to discover new treasures (since science doesn't know anything about the unknown, it cannot really help us in unknown territory anymore than a good sense of observation can). Or tip-toeing into new territories using A/B tests, as if that's how anyone ever discovered anything. When's the last time an "optimized" thumbnail on Netflix got you to watch something you didn't want to watch? It's like changing the cover of a book I guess... maybe it can increase sales by half a percent. Maybe. And then if we discover some kind of hidden formula, we can just make all of them look the same. But are we still increasing sales by that point? Insights quickly become trends, and by then any competitive edge is lost.
It seems like going from no data to some data is what provides the greatest insights we will ever get. Beyond that, it's mostly just small optimizations/diminishing returns. Because if there was something obvious hidden in the data, just waiting to be discovered, wouldn't we have discovered it when we analyzed the data for the first time? (if we did so thoroughly)
So maybe that's what we should focus on: what do we _not_ have data about? Or what data have we never really analyzed (or even looked at)? Or maybe new ways to look at the data we already have.