Once an objective is established and data sources have been identified, you can begin looking at the data in order to understand how each data element behaves individually, as well as how it interacts in combination with other variables. But even before you start looking at the values of variables, it is important to understand the different types of data levels of measurement and the kind of analyses you can perform with them.
Levels of measurement is a classification system for classifying data into 4 different categories which is discussed as follows (ratio, ordinal, interval, and nominal). It is an important aspect of the project or studies metadata.
Levels of measurement is important in the world of predictive analytics since the specific measurements will often dictate which algorithm or techniques can be applied. For example k-means clustering does work if you want to incorporate nominal data, and logistic regression can not use ratio data...