One of the things that can confuse a lot of people who are new to data analysis is the concept of “data is data.” That is to say, so long as I have the data, the topic that I am reporting on does not matter from the standpoint of reporting. It doesn’t matter if I am reporting on income or velocity, revenue or shuttle launches.
At a previous job I would be called on to demo the <acronym title=”Online Analaytical Processing”>OLAP</acronym> software that our company configured. In these demos I might show a report of customers by revenue vs. income and get asked “Can you this with total orders” or “can you do that by business region instead of income?”
Sure. Data is data. If you have the data that supports that kind of breakdown, then most software can do the analysis. It doesn’t know that this is “income”: it only knows that it is a number associated to a field in a database.
Context is mostly necessary for interpretation. Knowing whether a number is in miles-per-hour, degrees Kelvin, or revenue per customer gives us an idea of how to interpret the data and indicates what kind of reports might be appropriate, but the mechanics in the background of reporting are not going to be all that different.
The main question then becomes “what kind of reports does this data support.” This is a harder question and one I will go more in depth with at a later time.