Meaning
Transforming raw data points into a standardized coordinate system accounts for the different variances and covariances of each feature. Using mahalanobis normalization allows for a more accurate comparison of observations that occur on different scales or are highly dependent on one another. It provides a measure of how many standard deviations a point is from the mean of a distribution.
Correlation Adjustment
Factoring in the way variables move together prevents the over-representation of redundant information. When mahalanobis normalization is applied to a set of performance metrics, it treats a cluster of related successes as a single unit of progress rather than multiple independent wins. This adjustment gives a more realistic view of overall momentum.
Outlier Detection
Identifying points that fall far from the center of a group is easier when the space is normalized. A data point that looks normal on a single axis might be revealed as an anomaly once the relationships with other variables are considered. This technique helps a founder spot unusual patterns in cost or revenue that require immediate investigation.
Scale Invariance
Removing the units of measurement ensures that a change in one variable is comparable to a change in another, regardless of their original magnitude. This property makes the analysis resilient against shifts in the way data is recorded. It allows for the integration of disparate data sources into a single coherent model.
