Master Data Model: Creating a Single Language for Retailers

Centralized and uniform master data is the foundation for creating an integrated, flexible and adaptable supply chain


A famous African parable tells of three blind men, from different tribes and speaking different languages, who were each asked to describe an elephant. However, each man had only experienced a different, singular part of the animal. One of the men, after touching the elephant's leg, concluded that the elephant is like a large tree without any branches. The second man, after feeling the trunk of the elephant declared, "No, it's nothing like a tree it is like a mighty snake." Finally, the third man, who had experienced only the tusk of the elephant, observed that the elephant is nothing like a tree or a snake it is long and smooth and sharp. "Clearly," said the man, "an elephant is like a spear."

Of course each one of the men accurately described their understanding of what an elephant is based on their experience, but finally none of them was able to accurately describe an elephant.

Piecing Together the Supply Chain Vision

Many retail organizations today operate in a similar manner. Each business unit has a view of their part of the organization, but it is often difficult to form a common or a complete understanding of the business because there is no way to link the disparate views. Commonly, the capability to form a coherent understanding is lost because the master data (the foundational data that is common to every department in the organization like sites, vendors and items) is incomplete, inaccurate or not aligned between systems.

One retail manager described it this way: "It's like you're in a room with peep holes all the way around and you need a panoramic view to understand what's happening in the business. So you begin looking through the peep holes, one at a time, to see what's happening out there, and finally you are able to piece together what you think is most of the picture. However, you realize that it's not today's picture but that it's yesterday's picture, and then you start the process all over again."

It has often been said that good decision making requires good information delivered to the right people at the right time. Taking this thought one level deeper, it can also be said that the bedrock of good information is well organized and coherent master data (also called referential data). These days there is very little room for error in a highly competitive retail industry with paper-thin margins and increasingly higher costs of doing business. Companies looking for every small advantage must look at the foundation of their decision making (master data) and ensure that they are not entering the competition with an avoidable and all too correctable handicap.

Creating Understanding through Common Language

One can imagine that some other person, interviewing each man in the African parable, could begin to cobble together a pretty accurate picture of what really is an elephant. But what might it be like if the men used different words to describe the same thing? A leg might be called an ear, a tusk might be called a tail and an eye might be called a trunk. The picture that would emerge in such a case would be strange indeed. Surprisingly, plenty of retailers do something very similar to this when trying to form a global picture of their supply chain.

To illustrate this confusion of language, let's look at a situation that will be familiar to many. Logic would dictate that a can of corn in Kansas is the same as a can of corn in Florida. At many retailers, however, the merchandise system in Kansas does not communicate with the merchandise system in Florida. Identical items are added to each system using the next available warehouse item number, and these numbers are usually not the same. For a retailer to determine how much corn is being moved in the enterprise can be a time-consuming endeavor. By using the case UPC they can begin to piece together a picture of movements into and out of the distribution center (DC). However, to see how that relates to unit sales from the point of sale requires a lot more detective work. With this model there is no good way to directly translate consumer demand to what is being ordered into the warehouse. For these organizations forecast optimization is difficult at best and impossible at worst.

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