Using Percentiles To Identify Outliers and the Typical Experience

Statistics play a crucial role in various fields, helping identify areas for improvement and untapped opportunities. However, some statistical measurements are more useful than others, with percentiles often being more effective than averages. Averages can hide outliers and mask typical behavior, while percentiles provide a clearer picture of the data distribution. Despite their advantages, calculating percentiles is resource-intensive and requires loading and sorting entire datasets. Stream processing systems like Apache Flink have made it easier to calculate percentiles by allowing measurements to be accumulated using time or counting windows. By using tools like Flink, companies can gain valuable insights into their data and improve customer experiences.