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Notes -
It’s always difficult for me to appreciate what others might find interesting in my daily tedium haha.
I can offer some tidbits though. I’m on a small team (<5 ICs plus manager) and our daily standups are scheduled for 15 minutes. They routinely go 1-2 hours. Every day. I blame a mixture of managers trying to catch up on what’s going on and some on the team just…..always having a lot to say.
Over my career I’ve landed on a kind of “uncertainty principle” for data where the more basic and essential to the business a piece of data is, the more unknowable.
Three examples:
I once worked at a company on a pricing competitiveness project, so I asked the following question: “here’s literally a customer id. What are we charging this person?” And I got 4 different answers. Each was labeled “production” and I was told each was correct.
Another role I once had involved my company’s network so here’s a question: “how much traffic is on the network, right now? Where is it going?”
We’re talking like 10-20% error in these measurements. It’s wild to me. I think this definitely plays a role in how short of its promise “data science” has fallen. It’s a marginal gain and for so many companies I’ve worked with there’s been much much lower hanging fruit.
Last example was an ISP trying to improve the efficiency of their technician house calls. Question: in how many of the visits last month did the tech resolve the problem as opposed to merely doing something that was pointless and telling the customer the problem was solved, and then the problem just went away? Who knows!
The catch to this one is that if the stat was knowable—if it was a solved problem—would it be your core business?
Not that it makes the continued failure to implode any less impressive, mind you.
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