
In a real-life situation, a colleague came to me, complaining about a slow team member and hinting, to get rid of this employee. As it was my 3rd day on the new assignment, I promised to have a closer look. For the meantime, I didn't see any point in taking too drastic measures, especially since we didn't have an alternative person with similar skills. I sat down and both with what I received as feedback from the team-member and my own previous background, I started to design a report in order to measure what was really going on.
The first step - obviously - was a huge table of data, which needed some sorting. Thanks to the advanced possibility of xls-sheets, this was accomplished within a few mouse-clicks. It depends however, what you are looking for to finally come to the final report-output. Now comes the skill of give the appropriate interpretation of these figures to the reality; to erase any possible wrong assumptions or prejudices. The conclusion was a bit different from the conclusion of both the client and my colleague.
About a year ago, I was having a casual chat with a business analyst. We talked about various things and I cannot exactly recall how we ended up with this, but I started to elicit one of the assumptions about life in the Middle Ages. I like the example, since it nicely illustrates our remote knowledge of it, and underlines how preconceptions create a totally false image therefore. Beginning with the fact, that "mediaeval man had an average age of 35", makes us assume (in fact believe) that people of around 70 were practically non-existent. The numbers are a bit oversimplified, but they act only as an example. But the opposite is true. Despite dangerous or poor conditions, old people did exist. It was rather the high infant mortality rate (fatality at childbirth or hardly reaching the age of 3), which caused the average age to dramatically drop. His surprise to this revelation was in fact my surprise of why he doesn't know these pitfalls of statistics.
When using reporting data, it should be natural, that a manager takes time to know the context of the data. Know what you measure, know what lead to the given output and make sure that one does not use incomparable parameters. Equally, there is also - sometimes - the possibility to deliberately misinterpret the data.
But at the end of the day, correct understanding - i.e. interpretation - is vital for your business (and your own professionalism). Thus, it needs more than just staring at colourful numbers.