Explaining Data Types

A variable corresponds to a column in the data set, and rows contain the observations for the variable. For example, in a database containing customer information, the <name> and <address>​ fields are variables.

In SAP Analytics Cloud Smart Predict, a variable can have the following properties:

• Statistical type: continuous, ordinal, or nominal
• Data type: date, number, or string, for example
• Role: target (or signal), date, entity, or excluded

In SAP Analytics Cloud Smart Predict, there are three statistical types of variables:

1. Nominal variables: a discrete and unordered set of values or categories
2. Ordinal variables: a discrete and ordered set of values
3. Continuous variables: a real number that can take any value (with fractions or decimal places)

There are two data types used in SAP Analytics Cloud:

1. Quantitative or numerical data:
   • Data are numbers and can be quantified.
   • Data can be classified as either discrete or continuous.
   • Data can be counted or measured and summarized using mathematical operations such as addition or multiplication.

   Examples include: age (28 years old), height of a person (200 cm), grade score (85%), and salary amount ($35,000)

2. Qualitative or categorical data:
   • Data are either not numbers, or if they are numbers, they cannot be quantified.
   • Data items can be placed into distinct categories based on some attributes or characteristics.
   • Data can only be summarized by frequency count (or mode). No other mathematical operators can be applied.

   Examples include: gender, race, grading system (A, B, C, or 1, 2, 3), and income level (low, medium, high).

To build a predictive model, you must define the following variable roles:

1. Target or signal variable:
   • There can only be one target/signal variable.
   • A target/signal variable is the variable that you are predicting. In other words, the model outcome (sometimes referred to as the dependent variable in other applications).
   • It can be binary (for classification) or continuous (for regression and time series).
   • There can be no missing values.
2. Date variable:
   • This is the variable used for a date dimension.
   • It is mandatory to include a date variable for a time series predictive scenario, but not for classification or regression scenarios.
3. Entity:
   • In a time series forecasting model, it is a nominal variable, or a combination of variables that split up the predictive model data into segments. Each available combination of entities produces a separate forecasting model, yielding distinct predictions for each combination.
   • The forecasting models can then reflect behaviors that are specific to a given segment, and therefore produce more accurate predictions.
   • The entity can be a dimension in the data, for example, region, store, or product family, or a combination of dimensions.
   • It is optional in a time series predictive scenario.
4. Influencer:
   • There can be multiple influencer variables.
   • The influencers are variables that describe the data and serve to explain a target.

For more information on using variables in Smart Predict, you can visit Variables in Smart Predict | SAP Help Portal.