Types of Derived Metrics Explained Using Data Analysis

In the last lecture, you saw some examples of derived metrics. Now, let’s understand the various types of derived metrics. Watch the following lecture to get an idea of the different types of derived metrics.

Broadly, there are three different types of derived metrics:

1.    Type-driven metrics

2.    Business-driven metrics

3.    Data-driven metrics

Type-Driven Metrics

These metrics can be derived by understanding the variable’s typology. You have already learnt one simple way of classifying variables/attributes — categorical (ordered, unordered) and quantitative or numeric. Similarly, there are various other ways of classification, one of which is Steven's typology.

Steven’s typology classifies variables into four types — nominal, ordinal, interval and ratio.

• Nominal variables: Categorical variables, where the categories differ only by their names; there is no order among categories, e.g. colour (red, blue, green), gender (male, female), department (HR, analytics, sales)

  • These are the most basic form of categorical variables.

• Ordinal variables: Categories follow a certain order, but the mathematical difference between categories is not meaningful, e.g., educational level (primary school, high school, college), height (high, medium, low), performance (bad, good, excellent), etc.

  • Ordinal variables are nominal as well.

• Interval variables: Categories follow a certain order, and the mathematical difference between categories is meaningful but division or multiplication is not, e.g., temperature in degrees celsius (the difference between 40 and 30 degrees Celcius is meaningful, but 30 degrees x 40 degrees is not), dates (the difference between two dates is the number of days between them, but 25th May / 5th June is meaningless), etc.

  • Interval variables are both nominal and ordinal.

• Ratio variables: Apart from the mathematical difference, the ratio (division/multiplication) is possible, e.g., sales in dollars ($100 is twice $50), marks of students (50 is half of 100), etc.

  • Ratio variables are nominal, ordinal and interval type.

Understanding the types of variable enables you to derive new metrics of types different from the same column.

For example, age in years is a ratio attribute, but you can convert it into an ordinal type by binning it into categories such as children (< 13 years), teenagers (13-19 years), young adults (20-25 years), etc. This enables you to ask questions, e.g., do teenagers do X better than children, are young adults more likely to do X than the other two types, etc. Here, X is an action you are interested in measuring. 

Let’s look at some more examples of type-driven derived metrics.

In the next lecture, you will learn the second type of derived metrics - business-driven metrics.