After having a Twitter message convo with @dominicad on prioritization and "saying no," I want to share a few thoughts. So, here is a thread - TLDR; Prioritization is (also) about knowing why the highest priority CAN'T be done.
1/ AFTER the YES'S have been decided, there is a need to decide out of those "YES" what order do we start them. I pick the top 3 candidates and get them positioned on this trilemma. The goal is to find reasons for and against starting being successful against its peer options.
2/ So the process I use is described in this image. The goal is to determine why whilst we NEED to do all three, some are easier/prudent than others to start next. Easier/prudent meaning, there are reasons for and against starting sooner rather than later.
3/ At this stage of prioritization, after the EVERYTHING is priority 1, I move the conversation if these are all P1 then how do we arm teams with the right one to focus on first, and if there are reasons we can't start them (risks) how do we fix that.
4/ The gold is in the reasons WE CAN'T YET or AT ALL do these agreed things. And to solve them. I call these patterns of "NO". I couple of common ones -
1. There are lots of diminished Do'Able - a skill or capacity deficit 2. Priority driven by last-minute perishable items
5/ My key point (and as my wife says, finally) - Capture the reasons why an ideal priority and P1 items CAN'T be done. Find and fix these. Prioritization is more than order, it's allowing that ordering to be actioned.
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Forecasting is often made more complex than it needs to be. Given your alternatives are guessing (or hoping), even a simple model will perform better. Here is my thinking process 1/
My first goal is to get a baseline that real (future) data an prove the forecast is reliable. Until then, I hide the most recent periods of data and see how my model would have performed using data from the period before that. 2/
Is the work we are about to do similar in complexity and unknowns as prior work. If it isn't, I IGNORE historical data. Sometimes historical data matches just because the system is more at play than the work itself. 3/