I’m managing a project to install signage for a college campus’s botanical collection.
Our contractor, who installed the sign posts in the ground, did a poor job. A lot of them pulled right out of the ground.
Nobody could agree on how many posts were installed: the groundskeeper, contractor, and two core team members, each had their own numbers from “rough counts” and “lists” and “estimates” and “what they’d heard.”
The best decision I’ve made on this project was to do a precise inventory of exactly which sign posts are installed correctly, completed, or done badly/missing. In our meeting with the VP yesterday, this inventory helped cut through the BS and create a consensus around where the project stands. Instead of arguing over whose estimate is correct, I can redirect conversation to “we need to get this inventory done so that we’ll really know.”
Of course, there are many cases where even the hardest numbers we can get are contestable.
But maybe we should be relieved that the project of “getting hard data” (i.e. science) is able to create some consensus some of the time. It is a social coordination mechanism. Strategically, the “hardness” of a number is its ability to convince the people you want to convince, and drive the work in the direction you think it should go.
And the way you make a number “hard” can be social rather than technical. For example, on this project, the fact that I did the first part of the inventory in the company of a horticulturist rather than on my own probably was useful in creating consensus, as was the fact that the VP and grounds manager have my back and are now aware of the inventory. Nobody actually inspected my figures. Just the fact that I was seen as basically trustworthy and was able to articulate the data and how to interpret it was enough.
It should surprise nobody that the level of interest I got in these numbers far exceeds the level of interest in my Fermi-style model of the expected value of taking RadVac. I am plugged into a social network with a particular interest in the signage. It’s real money and real effort, to create a real product.
It doesn’t matter how much knowledge I personally have about EV modeling. If I don’t plug myself into a social network capable of driving consensus about whether or not to take RadVac, my estimate does not matter in the slightest except to me. In this case, individual knowledge is not the bottleneck, but rather social coordination.
Hard numbers
I’m managing a project to install signage for a college campus’s botanical collection.
Our contractor, who installed the sign posts in the ground, did a poor job. A lot of them pulled right out of the ground.
Nobody could agree on how many posts were installed: the groundskeeper, contractor, and two core team members, each had their own numbers from “rough counts” and “lists” and “estimates” and “what they’d heard.”
The best decision I’ve made on this project was to do a precise inventory of exactly which sign posts are installed correctly, completed, or done badly/missing. In our meeting with the VP yesterday, this inventory helped cut through the BS and create a consensus around where the project stands. Instead of arguing over whose estimate is correct, I can redirect conversation to “we need to get this inventory done so that we’ll really know.”
Of course, there are many cases where even the hardest numbers we can get are contestable.
But maybe we should be relieved that the project of “getting hard data” (i.e. science) is able to create some consensus some of the time. It is a social coordination mechanism. Strategically, the “hardness” of a number is its ability to convince the people you want to convince, and drive the work in the direction you think it should go.
And the way you make a number “hard” can be social rather than technical. For example, on this project, the fact that I did the first part of the inventory in the company of a horticulturist rather than on my own probably was useful in creating consensus, as was the fact that the VP and grounds manager have my back and are now aware of the inventory. Nobody actually inspected my figures. Just the fact that I was seen as basically trustworthy and was able to articulate the data and how to interpret it was enough.
It should surprise nobody that the level of interest I got in these numbers far exceeds the level of interest in my Fermi-style model of the expected value of taking RadVac. I am plugged into a social network with a particular interest in the signage. It’s real money and real effort, to create a real product.
It doesn’t matter how much knowledge I personally have about EV modeling. If I don’t plug myself into a social network capable of driving consensus about whether or not to take RadVac, my estimate does not matter in the slightest except to me. In this case, individual knowledge is not the bottleneck, but rather social coordination.