Usage of Data Preparation procedures

Your usage of Data Preparation procedures depends on your particular needs. A typical route, after loading your data, is:

  • Metadata preparation. Review the variables in your data file and determine their valid values, labels, and measurement levels. Identify combinations of variable values that are impossible but commonly miscoded. Define validation rules based on this information. This can be a time-consuming task, but it is well worth the effort if you need to validate data files with similar attributes on a regular basis.
  • Data validation. Run basic checks and checks against defined validation rules to identify invalid cases, variables, and data values. When invalid data are found, investigate and correct the cause. This may require another step through metadata preparation.
  • Model preparation. Use automated data preparation to obtain transformations of the original fields that will improve model building. Identify potential statistical outliers that can cause problems for many predictive models. Some outliers are the result of invalid variable values that have not been identified. This may require another step through metadata preparation.

Once your data file is "clean," you are ready to build models from other add-on modules.