Half a century ago, one mathematician thought out-of-the-box, to solve this problem and came up with the box plot. This is also where other metrics come into play, like the median, 95 percentiles that can give us a better understanding of the data.
Now we may be happy with that metric, but what happens if every now and then it takes 6000ms to load? The 300ms average number hides that alarmingly bad experience for sizable customer base. What if sizable number of customers are experiencing a slow load time even though the average is within the limits of our expectation? Imagine that we had a dataset that showed on average it took 300ms to load the app.
While the average is often a useful metric, by itself is a lossy compression algorithm.
Showing averages over time or across some series of data often allows us to answer questions like: How long did the app take to load in the mobile device? To answer this question, most commonly, we would find all data points for the day and then compute the average. But when you have diverse data points and sources, telling the story with just one aggregation to represent the whole range of numbers might often not tell the fully story. By Amir Netz, Technical Fellow and Mey Meenakshisundaram, Product Manager
