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Storing Additional Attributes in the Data Warehouse

Siebel Customer-Centric Enterprise Warehouse comes with several prepackaged attributes. Because you may want to incorporate additional attributes to the data warehouse, this section provides detailed instructions for storing free text and domained attributes.

There are a variety of scenarios that occur when storing a new attribute. Depending on where the data resides, you can take particular steps to incorporate new attributes and make them available in the data warehouse and your front-end schema. Generally speaking, there are three major areas where data resides—Source database, staging table, and Siebel Customer-Centric Enterprise Warehouse. Figure 43 illustrates the three scenarios and the components that are affected when trying to store the new data.

Figure 43. Scenarios for Storing New Attributes

In the sections that follow, you can find procedures for storing new attributes for each of the three scenarios. Each of the steps in the procedure corresponds to larger topics described in later sections of this chapter. The steps provide you with a high-level overview, and the larger topics provide the details.

To store additional attributes from the source system

1. If applicable, load the code and code name into the IA_CODES table.

   This step applies only if you are storing domained attributes. In addition, perform this step only if the code and code name are not already loaded into the IA_CODES table. For information on creating a codes mapping, see Creating a Codes Mapping.

2. Modify the existing extract mapping or create a new extract mapping to extract this new information from the source system.

   For information on modifying the existing Business Component mapplet of an extract mapping, see Process of Creating and Modifying Business Adapters. For information on creating a new extract mapping, see Creating an Extract Mapping.

   TIP:   You can perform calculations in the Business Component mapplet of the extract mapping or in the Source Adapter mapplet of the load mapping. However, it is not recommended that you perform performance-expensive calculations in the extract. This protects you from interfering with your source transaction system. For these types of calculations, it is recommended that you perform them in the Source Adapter mapplet in the load mapping. One of the major reasons why Siebel Customer-Centric Enterprise Warehouse splits the extract process from the load process into two separate mappings is to minimize the amount of time tying up your source transaction system.

3. Modify the existing load mapping to take the data from the staging table and load it into the data warehouse.

   For information on modifying the existing Source Adapter mapplet of a load mapping, see Process of Creating and Modifying Business Adapters. For information on creating a new load mapping, see Creating a Load Mapping.

   TIP:   If you map the data to a staging table's extension column, you must determine the type of extension column to use. For information on the type of extension column to use for attribute data, see Determining the Type of Extension Column to Use for Attributes.

4. Incorporate the new attribute into your front-end schema.

To store additional attributes derived from staging area objects

1. Modify the existing load mapping to take the data from the staging table and load it into the data warehouse.

   For information on modifying the existing Source Adapter mapplet of a load mapping, see Process of Creating and Modifying Business Adapters. For information on creating a new load mapping, see Creating a Load Mapping.

   TIP:   If you map the data to a staging table's extension column, you must determine the type of extension column to use. For explicit information on the type of extension column to use for attribute data, see Determining the Type of Extension Column to Use for Attributes.

2. Incorporate the new attribute into your front-end schema.

NOTE:  This procedure can also be used to store additional metrics. For more about storing metrics, see Storing Additional Metrics in the Data Warehouse.