Data Enrichment 
Data enrichment enables you to enhance the uploaded external data from retailers with additional information for reporting. The analysis of the reports helps to give you insights into past and current out-of-shelf and out-of-stock situations at retailer stores. Based on the reports you can resolve the current issues and analyze the trends and problem areas that you should focus on. Reducing the number of out-of-shelf and out-of-stock situations results in a higher on-shelf availability which in turn helps to improve sales figures, consumer satisfaction, and brand loyalty.
Data enrichment in SAP Demand Signal Management provides two specific enrichment sequences for the following purposes:
Sales data enrichment
The main focus is on determining out-of-shelf situations for zero sales using aggregated sales data.
Stock data enrichment
The main focus is on aggregated sales and stock snapshot data to determine out-of-stock situations based on the days of supply.
The rules for data enrichment are contained in an open and extensible framework enabling you to implement your own rules. Data enrichment is orchestrated and integrated into the data flow of SAP Demand Signal Management which is based on SAP NetWeaver Business Warehouse using process chains and the process flow control.
In the data enrichment process new data records are created for the DataStore objects in the Data Propagation Layer. The new records are separated from the retailer data using a reserved value for the Type of Sales and for the Stock Type. The following values are available:
0001: Result of Sales Data Enrichment
00: Results of Stock Data Enrichment .
Prerequisites
You have made the settings for Data Enrichment in Customizing under
Cross-Application Components 
Demand Data Foundation Data Enrichment 
.For sales data enrichment, you have uploaded sales data to the DataStore object (DSO) Aggregated Sales Propagation (/DDF/DS11). For stock data enrichment, you have uploaded stock snapshot data to the DSO Stock Snapshot Propagation /DDF/DS12.
To get meaningful data enrichment results, we strongly recommend that before start enriching your data, you first execute an initial upload of sales and stock history data excluding the process steps for data enrichment. This ensures that the data enrichment algorithms run on a solid data foundation that enables proper analysis and meaningful KPIs. A zero sales record, for example, can only result in a significant out-of-shelf incident, if the sales history for the respective product-location combination is taken into consideration. A solid data foundation requires a sales and stock history of 90 days or more.
This applies to all KPIs calculated during data enrichment, for example outlier detection, zero sales generation, average calculation. We therefore recommend that you include the process step for data enrichment at a later phase.
Data Flow
The system calls the data enrichment process from within the corresponding DataSource. The result of the enrichment is written to the Persistent Staging Area and flows back to the corresponding DSO.
Example: Data Flow for Sales Data Enrichment