“It’s really important to know your data.” I recall saying that to a few people. I don’t recall explaining it very well.
I don’t mean that it’s important that you memorize a bunch of numbers. That’s not knowledge. That’s just information. Your phone number is information. Einstein famously did not know his own phone number (according to the story at least); he didn’t bother to memorize anything he could just look up.
Knowledge is having a sense of the general shape of your data – like knowing to be careful of using gender as a predictor of giving, if most household gifts are by convention credited to the male spouse. It’s about being aware of certain relationships that exist in your data – perhaps widowed men are not very likely to be Planned Giving expectancies, but widowed women are extremely likely.
Knowledge is more conceptual than informational, and is acquired through exploration of a sometimes aimless nature. Aimless, but not unaware.
An example, which I was told about recently: The staff of a large performing-arts organization noticed a curious phenomenon … their donor lounge is more full during weekday performances than it is during weekend performances. A little exploration of the data is in order. As long as ticketing and donor data can be analyzed together, this interesting observation might be turned into real knowledge. Maybe weekday attendance is highly predictive of giving. The causal link, possibly having to do with income or stage of life or both, might remain murky, but a proven correlation alone would be both interesting and useful.
Thinking like an analyst is NOT memorizing numbers, NOT just geeking out at the computer. It’s about being aware, curious, and creative. It’s about being plugged into your institution’s real day-to-day operations and its interactions with the constituency it serves.