I was in the train station in Nuremberg Germany last week, watching the thousands of people bustling through the main corridor, arriving or departing through the one main passageway, and reflecting on the pathway of a single person and, funny enough, how the pathway of that one person was very much like the value of a single social networking KPI. It's funny how the human brain thinks, with one conversation bleeding over into another, sometimes making connections and correlations that you'd never otherwise come across in nature. I wrote a blog post on the limitations of using tools like Klout to measure personal brand awareness through social activity, and the related problem that the human race seems to have with key performance indicators, in general, in that we give them much more weight than we should.
KPIs are a poor indicator of what actually happens on the ground. They may measure a specific activity, but there are often exceptions (many, many exceptions) which are not usually factored into that measurement, giving a flawed view of what is actually happening. Why do managers allow this to happen? Because plugging data into spreadsheets, and then letting an algorithm guide their management decisions is often easier than actually understanding what is happening on the ground, or -- heaven forbid -- actually talking to people to get a dose of reality.
For example, two support analysts may be measured by the number of tickets they close. One of them may close 10 tickets in a day, the other only 3. Too much focus on this metric often leads management to the wrong conclusion: that analyst #1 with the 10 ticket closures is doing more/better work than analyst #2. But by stepping back and looking at a number of measurements -- and not being caught up in a single slice of data at one point in time, but by looking at the patterns that are developed over time -- you get a better sense of what is actually happening. And in this case, further research into the ticket closure rate may show that analyst #2 only takes on the most difficult problems, while analyst #1 cherry picks their tickets.
What has all of this got to do with a train station in Germany? These are just the thoughts flowing through my head as I sat and watched the patterns of people moving across the floor, trying to get a sense of where the majority of people were going. The movement of a single person was less interesting than abstracting my view (looking at things from a broader perspective), and instead watching the ebb and flow of the entire crowd. Much like stepping back and looking not at the single KPI, but the ebb and flow of all of the KPIs over time, I had a better understanding of the movement of the station, understanding the major directional shifts of the crowds based on the arrival and departure times of the trains.
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