An integrated data source is more valuable than the sum of its parts

The benefit of combining two or more data sources into a single comprehensive source to create analyses, charts, and presentations is immediately evident in the improved quality of information and insights realized.

Working together, manufacturers and retailers can determine how sales demand measures up to actual sales or how well marketing programs perform in a specific consumer demographic. The fact is quality analysis and collaborative planning cannot be accomplished without looking at data in an integrated manner.

Example: Retailer POS and Manufacturer Shipment Data

Consider sell-through analysis performed on a recent promotional event. Such analysis can be accomplished by combining retailer POS data with manufacturer shipment data as illustrated below:

In this example, the manufacturer of the "Avalon" brand and the retailer "Supermart" looked at 5 recent promotional events for the Avalon 32 OZ product. By comparing POS and shipment data, it's clear that the promotion for the week 1/18/03 was the most productive, and promotion run during 12/14/02 performed poorly. From here, other analyses such as trade promotional payment administration and retail inventory control can be done.

Data Integration Tips
The term data integration is used quite a bit, but what does "Data Integration" really mean? It's useful to look at some of the characteristics of an integrated data source. A truly integrated data source should:

• Map names from two or more different data sources to a user-friendly name for all dimensions including categories, segments, geographies, products, and measures
  
  
• Roll up values of items that have different names in different data sources
  
  
• Combine any type of data including POS, shipment, planogram, price surveys, ad diaries AND syndicated data
  

Data Integration is vital to realizing the full potential of your data. Additional benefits include more efficient use of network resources / bandwidth and reduction in frequency of dirty or incompatible data.

The data, products and accounts depicted in this example are fictitious. Any resemblance to actual data, products or accounts is purely coincidental.