Data might tend to be clustered around the mean. The "kurtosis" can be used to measure how closely data resembles a "bell-shaped" collection. However, note that data values that are far from the mean will generate relatively larger contributions when the difference in the formula is raised to the fourth power. So, the kurtosis that is relatively large can indicate the presence of "outliers"...that is, a significant number of data values far from the mean. In this case, notice that that would also preclude the clumping of data in the middle (since lots are far from the middle) and so a possibly flatter distribution would be indicated by a larger kurtosis.