In the world of Master Data Management (MDM), it is quite common to see a single view of customer, product, supplier, etc but one of our customers is building a Single View of Cow.
The customer is in the animal management business and they are responsible for managing the life cycle (literally!) of a cow from birth to beef stroganoff. Tracking starts with parentage, siblings and relatives, cows in proximity on the various ranches. Tracking continues to the abattoir and then follows the various beef components as they travel through the supply chain all the way to the end consumers. The main business driver is food safety regulation. If a disease shows up at some point in the supply chain, you need to be able to quickly track upstream where the cow came from and downstream all the way to supermarket shelves.
The Single View of Cow use case provides some interesting best practices for the broader MDM community…
First, the herd mentality. While each cow has to be treated as a separate entity, once a cow enters a ranch it is nearly impossible to control or track the movements of every cow so you need to be able to model behavior for the entire herd. Single view of customer implementations focus on each customer’s specific information and characteristics to market and sell better to that customer. But customer behavior is often determined by the behavior of other customers. Marketers in industries like Retail, Consumer Packaged Goods, Clothing, and Consumer Electronics are very familiar with this phenomenon, sometimes called the fad effect. Today, the social networking revolution is pushing the fad effect a wide range of B2C and even B2B markets. How does your MDM deal with the fad effect in your customer data?
Second, the transformation effect. At some point in the animal supply chain, one cow is transformed into multiple components, each of which has to independently tracked from that point onwards. In a number of product industries, a best practice is to delay finalizing the product until as late as possible to take advantage of economies of scale. For example, a printer company may manufacture a generic printer that works across all countries and only at the distribution center are the local language manual, power supply and other materials added to make a SKU to be sold in a specific country. Supply chain experts refer to this technique as delayed differentiation. How does your MDM deal with delayed differentiation in your product data?
Third, in-situ data collection. In the animal supply chain, a lot of the places where the data is captured are out in the fields and farms where computers and network access are not prevalent. Critical to success is being able to take the data in wherever and whenever it is produced and in whatever format it is produced - scanned papers, mobile devices, or even spreadsheets or documents. How does your MDM un-structured or semi-structured data?
If you have a non-conventional MDM example that we can all learn from, let me know.
Btw, no animals were used or hurt in the writing of this blog post.