Collecting metrics is important. But we all know that many metrics are chosen for collection because they are inexpensive and obvious, not because they are actually useful.
(Quick pre-emptive strike #1: I’m using metrics very broadly here. Yes, sometimes I really mean measurements, etc. For better or for worse, this is the way metrics is used in the real world. Oh well.)
(Quick pre-emptive strike #2: Sure, if you’re Google or Amazon, you probably collect crazy amounts of data that allow highly informative and statistically valid metrics through sophisticated tools. I’m not talking about you.)
I try to avoid going the route of just supplying whatever numbers I can dig up and hope that it meets the person’s need. Instead, I ask the requester to tell me what it is they are trying to figure out and how they think they will interpret the data they receive. If pageviews have gone up 10% from last year, what does that tell us? How will we act differently if pageviews have only gone up 3%?
This has helped me avoid iterative metric fishing expeditions. People often ask for statistics hoping that, when the data comes back, it will tell an obvious story that they like. Usually it doesn’t tell any obvious story or tells a story they don’t like, so they start fishing. “Now can you also give me the same numbers for our competitors?” “Now can you divide these visitors into various demographics?”
When I first started doing this, I was afraid that people would get frustrated with my push-back on their requests. For the most part, that didn’t happen.
Instead, people started asking better questions as they thought through and explained how the data would be interpreted. And I felt better about spending resources getting people the information they need because I understood its value.
Just like IT leaders need to “consistently articulate the business value of IT”, it is healthy for data requesters to articulate the value of their data requests.