The answer to the ultimate question of life, the universe, and everything. Oh, and some thoughts on what makes a good metric
At Aruspex, we often discuss what is or isn’t a good measure internally, and with our clients. Generally, those discussions cover these 6 broad categories:
1. The metric must be easily explained
Perhaps the most useless metric ever calculated (although it is fictional), is “The Answer To The Ultimate Question of Life, The Universe, and Everything”. The computer Deep Thought in Douglas Adams’ The Hitchhiker’s Guide To The Galaxy takes 7-and-a-half million years to calculate the answer as 42, and goes on to explain:
“I checked it very thoroughly,’ said the computer, ‘and that quite definitely is the answer. I think the problem, to be quite honest with you, is that you’ve never actually known what the question is.”
As in the above example, it’s not useful to calculate a metric that leaves you wondering “yes, but what does that actually mean?” Good metrics can be explained, in plain language, in 1-2 sentences.
2. The metric must be actionable
Having metrics that are of only academic interest can lead to analysis paralysis… the benefits of good metrics are not that they are interesting, but that you can use them to assist in the development or monitoring of strategic actions. If they don’t meet that test, then they are better placed in your annual report than in your workforce plan.
3. The metric must be timely
Knowing what the attributes of your workforce used to be is not insight… it’s hindsight. It’s important that you can gather and report on data quickly enough that you can use it to make informed decisions.
4. The metric must be repeatable
If you’ve remembered rule #2 and the metric is actionable, you’re going to want to ensure that you can recalculate the measure on an ongoing basis. Without this, you won’t be able to track whether the actions you put in place are working.
5. More is Less / Less is More
The more metrics you calculate and present, the more likely it is that the most important ones will drowned out by the ordinariness of the others. Focus on the important few metrics that meet conditions 1-4 above.
6. Good context makes a good metric
Well thought-out infographics that give shape and context to your data can make you sit up and pay attention, elevating boring data to valuable insight. Stacked column charts and tree-maps, done well, can identify patterns in data that you just wouldn’t notice in a table.
I would be most interested to hear others’ thoughts about the categories here, and whether there are any you would add?