Joseph Carrabis wrote something very relevant to our interests. Especially when it comes to planning Web Analytics projects.
It’s worth the read. Go check it out. I’ll wait.
What’s easily missed on the first scan is the passage:
“The purpose of these rules is to tend towards 0 the likelihood that a mistake will be made.”
And the two rules, which are the meaty bits are:
“Rule #1 – Eliminate Variables”
“Rule #2 – Remove Ambiguities”
Rule 1 is important. I categorize knowledge into three broad buckets:
- What I know that I know.
- What I know that I don’t know.
- What I don’t know that I don’t know.
It’s the third category that’s the scariest of all. When I move information from What I know I don’t know into the What I know that I know bucket, I frequently uncover pieces of things that I didn’t know I didn’t know. The effort is important, and it does help minimize your chances of making mistakes. This makes the Discovery portion of any analytics project really important.
Which twists me into “Remove Ambiguities” line.
So it goes with analytics planning. You can spend a disproportionate amount of time planning contingency plans, or, you can set a goal and plan on a course of action that you have the greatest chance of controlling. Mastery of own destiny sort of thing.
There’s a lot of inside pool to Joseph’s blog posting – specifically the passages around gender – and there are reasons for that. In the coming months, I suspect that we’re going to have more discussions centered around accuracy, ambiguity, and standard deviation.