When analyzing performance data, business analysts must consider the age of the data that is being used for trend analysis. If the data is out of date and deemed not relevant to the current analysis, it should be replaced with more relevant and timely datasets to prevent any skewed or misinterpreted findings. A larger sample size over a sufficiently long time period will provide a much more accurate depiction of solution performance on which to base decisions. This will also assist you in guarding against false signals brought about by incomplete data. 

Let's consider a real-world example:

A sudden increase in the credit card transaction volumes at the same time of day recorded over a period of a couple of months could indicate a trend in abnormal spending behavior; however, when looking closer at this data sample, the business analyst might find that the data is only collected during a period of high spendings, such as the Christmas season. The results of this data-trend analysis cannot be used for general spending assumptions or measurements because the timing of this data sample is skewing the results.