Before you can detect a potential signal within the data, you must first filter out the probable noise. To filter out the noise, you must start with past data. In short, the supervisor, with his limited comparisons, suffered the consequences of his ignorance because he was not able to fully understand his current values. Most of what passes for statistical analysis these days is little more than collections of descriptive statistics. Bar graphs comparing unlike measures, pie-charts showing proportions, and rudimentary comparisons such as those in the story above are more a matter of description than any sort of true analysis.
Descriptive measures are concerned with how much or how many.
They provide no insight into why there are so many or why there is so much. Analysis is concerned with why. That is why analysis requires us to place all data in their context and then separate the potential signals from the probable noise. Thus, you should always begin an analysis by looking at a measure in a time-series plot, and you should always include some method for filtering out routine variations. The limits on this chart define how large or how small a single monthly value must be before it represents a definite departure from the historic average.