Yesterday I raked a lot of leaves.
Raking leaves and analyzing data share a number of similarities.
You don't rake leaves unless there are enough leaves to make it worth your while. You don't analyze data unless you have enough of it to make it worth your while.
If you don't rake often you'll get blisters and you probably won't have the best tools. You may even avoid the job. If you don't analyze your data regularly you may find it hard to begin and painful to accomplish.
Gathering the data is hard work, but it isn't quite half the work. You still have to do something with it. Getting leaves in piles is not the end of the job.
If you have a lawn mower that bags debris you can cut your grass and get the leaves up at the same time. If your information management systems are well designed analysis is built right in.
No one rakes leaves efficiently the first time. There are tricks you learn from trail and error and from watching others. Someone who rakes leaves for a living expends less energy and gets more done. Analysis tools like Google Analytics are designed for websites with millions of hits a day. You can leverage a tool like that even if you have a little church website.
There is a beautiful yard under there (you think). You may find out the grass is dead when you pick up the dead leaves. You always begin data analysis with an assumption. Some people don't want to rake the data and find out the assumption is wrong.
In the end the analogy breaks down since data is useful stuff and dead leaves aren't very useful. In both cases though you can get a sense of accomplishment when you look at the finished product and you bring order to chaos.
I have a lot of data to rake this next few weeks. I need to know what I'm going to do with the data before I get it all piled up. I need to list the questions that need answers, confront my assumptions, determine my goals, and mock up the final product before I start raking it together.


In June I will step back from 3 days per week in the office at 