That is the question is was facing today. It wasn’t about my success (or lack thereof). It was about the olympics. One member (a fellow Australian) was happy because we had two additional gold members over the United Kingdom. But there was something wrong with that train of thought. It was too American. Don’t get me wrong, as I see it it is great to have more golden medals, but in my old fashioned way of life (and thinking) it is weird that the runners up get to live. I must be going soft in my old age.

You see with Australia grasping a 14-12-9 achievement and the United Kingdom holding onto 12-15-19 at present this list could go into any direction. However, this got me thinking. How do you measure success? Don’t get me wrong the gold number are nice, yet it is not a true list of achievement, is it? I have been pondering this and my mind took me to the old 1,2,3 squared allocation. So Bronze counts as 1, Silver as 4 and gold as 9. Now we get to 183 for Australia and 187 for the United Kingdom. UK won by a nose-hair as jockeys tend to say. So is this actually fair? How can medals be universally set? I don’t think that a boxer will accept equal points to an equestrian, in support, the horse will not go along with that either. Still there is a need to give some level of equality especially as the best of the best of the best in any of these disciplines are competing, yet the simple set to look at the golden medals seems wrong (possibly Canadian Summer McIntosh might agree but she just got 3 golden and one silver medal), at 17 she got (as far as I know) a tied second place with a few others all with three golden medals in the French Olympics.
However I still ponder, is my formula the right one? It seems to be, but it might be my own shortsightedness to think so.
Still, the question remains, how do you measure success, and not just in sports. In the 90’s I was subject KRA’s (Key Result Areas) and I accepted them as I had no knowledge on how to measure success. Even in customer care and Technical Support these numbers (when applied to the field I was in) made perfect sense. At some point you need to consider what to measure and how to measure it. Medals are a finite point of achievement, customer care is a little bit more fluidic. So how to go about it? The Olympic medallist might have kicked this off, but my brain takes into all directions. So with one movie script under my belt (for assessment with Dubai Media) am I more successful in scripting then all my friends (both of them)? They are not in that field, so how to generalise some metrics? You see we can grab Z-scores but as far as I can see that is a near obsolete approach to matters (perhaps what the people call AI use this) and now we get to the next bit and why I used Summer McIntosh as an example. These were her first Olympics, so how could there be a Z-score of her and how would it be reliable (or relatable)? Previous competitions? These were her first olympics and even in global events the pressures are different.
And the field becomes even more complex, you see whatever they call these systems based on LLM’s and Deeper Machine Learning, it is either set by a programmer, or set by data and there the problem becomes a lot larger as both are used. Without proper verification and a number of constraints the equation becomes a GIGO rule (Garbage In Garbage Out).
I wonder how much some players consider success. Most will measure success by their ability to bring home the bonus funds. To some extent I accept that, but when you consider how they went about getting that success becomes a larger issue. In this I take the conceptual setting of Awareness versus Engagement in market research. Awareness could be shown how many impressions (or clicks) something gets, whilst engagement requires interaction with the solution. As I have always stated Engagement wins every time, but the large companies often herald views per thousand (or clicks as a secondary). So who get the price turkey at the end? Large Language Models with (Deeper) Machine Learning what some call a version of AI has issues and the world is waking up to Nvidia (not meant in a bad way). You see there is currently no AI, not yet anyway. What there is (the LLM and DML reference) is awesome and it can do great stuff, but it has issues like the legal sector recently saw. There is a lack of verification and that will be an issue in plenty of fields.
Have a successful day.
