![]() You cannot do that without the location golden records. You will want to have deep knowledge of the location in time of the thing. For that, you need the product (model) golden record. You will want to know a lot about the product model of the thing in order to make sense of the produced big data. When a thing (a machine, a vehicle, an appliance) becomes intelligent and now produces big data, master data management and indeed multi-domain master data management becomes imperative. ![]() With the raise of the Internet of Things (IoT) we will have to manage a lot more assets (or things) than we usually have considered. There are namely financial purposes and logistic purposes that have to aligned, but also a lot of others purposes depending on the industry and the type of asset. In asset master data management you also have different purposes where having a single view of a real world asset helps a lot. With product master data we must, in my eyes, rely more on second party master data meaning sharing product master data within the business ecosystems where you operate. Classification systems and data pools do exist, but will certainly not take you all the way. While third party reference data helps a lot with handling golden records for party and location, this is lesser the case for product master data. Even a global SAP rollout will usually not clarify this – rather the contrary. A given product may be a finished product to one unit but a raw material to another unit. In large organizations that have many business units around the world you struggle with having a local view and a global view of products. The self-service approach in online sales also drew the requirements of managing a lot more product attributes than seen before, which again points to a solution of handling the product entity centralized. Product Information Management (PIM) solutions became popular with the raise of multi-channel where having the same representation of a product in offline and online channels is essential. For example, if you have two records with the name “John Smith” on the same address, the probability of that being the same real world entity is dependent on whether that location is a single-family house or a nursing home. Knowing the properties of a location also supports the party deduplication process. Utility and insurance are other examples of industries where the location golden record (should) matter a lot. Also, the brewery wants to keep records of logistics around that place and the historic volumes delivered to that place. ![]() However, even though the owner of that place changes, which happens a lot, the brewery is still interested in being the brand served at that place. One example is that a brewery makes business with the legal entity (party) that owns a bar, café, restaurant. Location management have different meanings and importance for different industries. Nevertheless, striving for that concept will solve many data quality conundrums. Having the same location only represented once in a golden record and applying any party, product and asset record, and ultimately golden record, to that record may be seen as quite academic. In lesser degree we see the same challenges in getting a single view of suppliers and, which is one of my favourite subjects, you ultimately will want to have a single view on any business partner, also where the same real world entity have both customer, supplier and other roles to your organization. When identifying a duplicate you must be able to intelligently merge any conflicting views into a golden record as examined in the post Three Master Data Survivorship Approaches. If you are not able to prevent duplicate records from entering your MDM world, which is the best approach, then you have to apply data matching capabilities. Managing customer records and dealing with duplicates of those is the most frequent data quality issue around. Having a golden record that facilitates a single view of customer is probably the most known example of using the golden record concept. The golden record concept applies to all of these entity types, but in slightly different ways. In Multidomain MDM we work with a range of different entity types as party (with customer, supplier, employee and other roles), location, product and asset. Holding the most current and accurate data values for the entity described.Having a complete description of that entity covering all purposes of use in the enterprise.Being a unique representation of the real world entity described.A golden record is a representation of a real world entity that may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.Ī golden record is optimized towards meeting data quality dimensions as: The term golden record is a core concept within Master Data Management (MDM) and Data Quality Management (DQM).
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