The document "Guidelines for representing methodological changes in Data Structure Definitions" (Version 1.0, April 2019) provides recommendations on how to represent methodological changes for several use cases.

When designing SDMX artefacts for an implementation project, one major design choice is the dimensionality of the Data Structure Definition(s), that is, which and how many dimensions are used to uniquely identify the relevant time series. Various trade-offs related to this design choice, such as between DSD complexity and parsimony, are discussed in the Modelling Guidelines.

One aspect that is mentioned in the Modelling Guidelines, but not elaborated in detail, is the one of structural stability in case of methodological changes. In other words, how future-proof is the DSD? How many and what kind of changes to the DSD are required when certain aspects of the underlying data change and the DSD needs to represent both, pre-change and post-change data, in different time series?

If certain future changes are already expected when the DSD is originally designed, dimensions (or attributes) covering these changes will be included in the DSD. If the DSD was designed without expecting changes, additional dimensions or attributes will have to be introduced at a later stage.