That's not normal is it?
Normal, or Gaussian, distributions are enormously useful across diverse situations. Apparently Karl Pearson was the first person to call a normal distribution a normal distribution. The distribution itself had already been around a century or more, finding useful applications in astronomy, physics, gambling and more. By supplying a name that stuck, Pearson helped to extend its usefulness even further by making it easier for people to share insights.
Pearson apparently also regretted the name, observing that calling one distribution normal, no matter how useful, left an implication that other distributions were somehow abnormal.
The two most common of the most frequently overlooked distributions are the Poisson and Exponential. The Poisson distribution (named after its discoverer, not a fish) is often a great description for the number of customers, or errors, or books borrowed in a given period of time. It also has a neat property where the variance and mean are equal, which is the sort of symmetry that helps lift an equation from being merely useful to being beautiful.
The Exponential distribution often applies when you are looking at waiting times or the time between events. While the Poisson helps describe the total number of customers, an Exponential describes the time-lapse between their arrival. It all depends on what the question is.
As well as being intimately related, the Poisson and Exponential have a further property that can give them an edge over a Normal distribution: they are not symmetrical, so they are more likely to accommodate skewed data. Anyone who deals with consumer type data (e.g. number of sales, size of wallet, transaction volumes) see skews more frequently than the rather more tidy world of the bell curve.
It's a big world out there, and it is not surprising that one distribution cannot describe it all. Adding these two to your tool-kit, along with a few data-transformations, still won't solve all your problems, but it will be a big leap forward.
Data transformations? Well, that is a topic for another day ...