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A pragmatic approach to master data management.
Master Data Management (MDM) is often explained as a way to prevent organizations from using multiple versions of the same data in different parts of their operations. However, a single data model does not always meet the needs of all stakeholders in the business. Should we therefore allow for different variations of the same data? Or should we always stick to a single data model? We turn to famous mathematician Kurt Gödel for a pragmatic approach.
Large organizations often use different versions of the same master data. This is not always due to a wrong way of working, but rather the result of different use cases. This practice is supported by something that has long been accepted in mathematics, namely that it is impossible to prove all truths with a single model.
This has been brilliantly articulated in Gödel’s incompleteness theorem.
“Any consistent formal system F within which a certain amount of elementary arithmetic can be carried out is incomplete; i.e., there are statements of the language of F which can neither be proved nor disproved in F.”
In computer science, there is a closely related paradigm of this theorem: choose the appropriate data structure depending on the problem.
A data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.
If we extend this logic to the field of master data management, it does not make sense to use one data structure for all problems. Multiple versions (structures) of the same master data will be required depending on the application. That does not mean that master data cannot be defined, but rather that it is pointless to enforce the use of a single data structure.
A more sensible approach is to identify the different use cases of the data, and the required data structures to resolve them efficiently. The master data can only be considered “complete” to the extent that these requirements are covered.
Another implication of Gödel’s theorem is that it’s impossible to cover all possible questions in advance. Applying this to master data management, this means that it is better to follow a grass-roots or bottom-up approach to define the master data.
However, this might sound like a never-ending story. In a way, it is. But it is the role of Master Data Management professionals to identify which new requirements can already be answered by the existing data model, and which requirements actually need a new structure. In practice, the number of required structures will be finite, and only a handful will be valuable for the entire organization.