1. Information and data is everywhere. Any given data point has low value. In contrast, the ability to contextualize data has enormous value.
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Just because there is a lot of data, doesn’t mean you can pull up a meaningful table using exactly the summary numbers you need.
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And even if you have that table, you need to be able to interpret it subjectively.
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Not every number exists to be added, aggregated, maximized or minimized numerically and you need to know the real-world meaning of every number.
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And even if you do know this real-world meaning, being able use discernment and wisdom to interpret it usefully.
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2. The high bar for metrics: what decision would this metric help me make? And under what circumstances?
3. The GPA effect…
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Did someone with a 3.94 GPA show more promise than a 3.92 GPA in a role that largely involved the ability crunch spreadsheets for 50 hours straight?
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What about relative strengths and preferences? Would someone gregarious and social with a slightly lower GPA really be a worse salesperson at a bank?
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Didn’t this skew even further towards people already coming from privileged and wealthy backgrounds who had benefited from and could continue to afford nearly infinite tutoring? Didn’t this reduce the diversity of new hires?
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Wouldn’t this skew towards people with a perfectionist streak? And reward them for high compliance? Always looking for the “right” answer in an academic context doesn’t translate that well in all business context (such as new product development for example).
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4. When there is still too much data, give the most weight to data around your first order priorities.
“When there is too much potential data… What are the key things that would change my decision? What is the data around my first order priorities? After you have some idea, then you consider the impacts for 2nd and 3rd order priorities.” — Reid Hoffman on Jordan Harbinger’s podcast
5. Accept that metrics on their own are not the full story, and they never will be.
Aiming for self-accountability at work
Why delivery velocity is a broken metric
“For by the ultimate velocity is meant that, with which the body is moved, neither before it arrives at its last place, when the motion ceases nor after but at the very instant when it arrives… the ultimate ratio of evanescent quantities is to be understood, the ratio of quantities not before they vanish, not after, but with which they vanish” –Principia Mathematica by Isaac Newton
I’m not a mathematician, but I’m not afraid of numbers. I’ve drifted through the standard high school and college math requirements, admittedly with curiosity. And I’ve hung around the hedge fund industry, software engineers, and actually trained mathematicians for long enough that math’s kind of rubbed off on me. Yet my skillset is probably best described as a mix of product and, more recently, delivery management with an academic foundation in accounting.
Over time, I’ve noticed that cost accounting and traditional/waterfall project management operate primarily on absolute values assuming they shouldn’t change. Especially with respect to budgets and time. In fact, variance or change is almost a dirty word in that context. Today’s markets, especially in the context of new product development, are different from the heyday of cost accounting in the 1950s. Back then, you could easily assume stable incremental growth in demand in many markets for 15-20 years. And actually be right. Usually you wanted to establish a budget to control unnecessary costs, because everything else was likely to stay the same.
Most choices in a big company don’t have the same implied cost of time. The value of time is assumed to be incalculable, more philosophical than practical. And it is, if you are stuck using “absolute” values.
Yet you can value time and derive implications for velocity if you are using finite difference methods. Thinking in relative terms enables you to think of relative profitability increases (in the moment) rather than always using an annual or quarterly budget yardstick determined much earlier, when you knew less and which is often likely to be out of date.
An invitation
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