Typically, data comes from multiple sources and might be in different formats. Many applications require input data to be in a specific format before the data can be processed. Although application requirements vary, there are common factors for all applications that access, combine, and process data. You can identify these common factors for your data. Here are tasks to help you start:

You can use the CONTENTS, DATASETS, and PRINT procedures to review the structure of your data.

Relationships among multiple sources of input data exist when each of the sources contains common data, either at the physical or logical level. For example, employee data and department data could be related through an employee ID variable that shares common values. Another data set could contain numeric sequence numbers whose partial values logically relate it to a separate data set by observation number.

You must be able to identify the existing relationships in your data. This knowledge is crucial for understanding how to process input data in order to produce desired results. All related data falls into one of these four categories, characterized by how observations relate among the data sets:

Finally, to obtain the desired results, you should understand how each of these methods combines observations and how each treats duplicate, missing, or unmatched values of common variables. Some of the methods require that you preprocess your data sets by sorting or creating indexes. Testing is a good first step.

Create small temporary data sets that contain a sample of rows that test all of your program's logic. If your logic is faulty and you get unexpected output, you can debug your program.

If your program does not run correctly, review your input data for the following errors: