• agent_flounder@lemmy.one
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    1 year ago

    So how does a company manage anything if they can’t use measurement targets?

    Like software engineering. How do you improve productivity or code quality if setting a target value for a measurement doesn’t work?

    • Moobythegoldensock@lemm.ee
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      1 year ago

      You don’t make your measures your targets.

      Example:

      “Our customers hate us. We will make our employees get a 10 on their surveys for each customer or we’ll punish them” makes the measure a target.

      “Our customers hate us, so we’re going to change our shitty policies to be more consumer friendly and see how our customers respond” keeps the measure as a measure.

      • mild_deviation@programming.dev
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        1 year ago

        So the difference is who decides what changes to make when interacting with the subject of the measure: workers vs management. Making the measure a target is basically a shitty management technique that abdicates responsibility.

    • agent_flounder@lemmy.one
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      1 year ago

      Ok I’m going to answer my own question because I’m too curious to wait lol

      Goodhart’s Law states that “when a measure becomes a target, it ceases to be a good measure.” In other words, when we use a measure to reward performance, we provide an incentive to manipulate the measure in order to receive the reward. This can sometimes result in actions that actually reduce the effectiveness of the measured system while paradoxically improving the measurement of system performance. … The manipulation of measures resulting from Goodhart’s Law is pervasive because direct measures of effectiveness (MOEs), which are more difficult to manipulate, are also more difficult to measure, and sometimes simply impossible to define and quantify. As a result, analysts must often settle for measures of performance (MOPs) that correlate to the desired effect of the MOE. … These negative effects can sometimes be avoided. When they cannot, they can be identified, mitigated, and even reversed.

      • Use MOEs instead of MOPs whenever practicable and possible
      • Use the scientific method to generate new measurement data, rather than harvesting existing and possibly compromised data
      • Help customers establish authoritative and difficult-to-manipulate definitions for measures
      • Identify and avoid the use of manipulated data and data prone to manipulation
      • Use measurement data not generated by the organization being measured
      • Collect data secretly or after a measurable activity has already occurred
      • Measure all relevant system characteristics rather than just a representative few
      • Randomize the measures used over time
      • Wargame or red team potential measures

      This report recommends that the organizations that employ analysts should do the following:

      • Return to the roots of operational research to focus more on direct measurements in the field
      • Answer the questions that should be answered, rather than the questions that can be answered simply because the required data are already available
      • Train analysts on MOEs, MOPs, and Goodhart’s Law and how they are interrelated
      • Make recognition of Goodhart’s Law part of the internal peer review process and part of all delivered analytical products
      • Identify and share mitigation best practices

      [Source]

      • BJHanssen@lemmy.world
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        1 year ago

        It’s pretty well established academically that basically the only way KPIs can actually work toward their intended purpose is if they are changed often and determined by the people doing the work that is ultimately measured. Ongoing measurements should only ever be used as indicators - hence the term *key performance indicators_ - and should never be used as targets. What that means in practice is that you should generally ignore all the individual metrics, and look across all of them instead to see if you can spot trends and anomalies, then investigate these qualitatively with the workers who ultimately produce those data to figure out what is happening and if any intervention is necessary.

        The problem is that the higher up you get in the hierarchy, the less of that kind of work there is to do and you end up chasing the people below you for nice numbers to plot into your presentations to make it look like there’s a point to your job’s existence.