Z-Score Normalization (also known as Standard Score or Z-Value)

Z-Score Normalization, also known as Standard Score or Z-Value, is a statistical method that describes the position of a data point in relation to the mean or average of a group of points. Imagine you’ve taken multiple choice quizzes all semester and you want to know how well you did on each one compared to the class average. By converting your quiz scores into z-scores, you can tell if you scored above or below the class average and by how much.

In business intelligence and data analytics, z-score normalization is a way to compare data points from different datasets or to normalize a single dataset for analysis. This process changes the original data into a common format that allows for better comparison. For example, if a company wants to compare sales performance across different regions with varying population sizes, using z-scores makes this possible because it puts the sales data on the same scale.

Applying z-score normalization helps data analysts and business professionals make sense of data that has different scales or units, transforming it into a standardized form. This not only simplifies the analysis but also helps uncover hidden patterns and relationships that might not be obvious at first glance, leading to more informed and data-driven decision-making for the business.