Changes for page Guidelines for SDMX Data Structure Definitions

Last modified by Artur K. on 2026/05/29 14:28

From 1.9 to 1.10 From 1.12 to 1.13

From version 1.10

edited by Helena K.
on 2026/01/15 12:40

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To version 1.12

edited by Helena K.
on 2026/01/15 12:41

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@@ -247,24 +247,22 @@
  **Table 4. General comparison of data structuring approaches**
--|(% style="width:360px" %)**Many pure concepts**|(% style="width:1255px" %)**Few composite concepts**
--|(% style="width:360px" %)cleaner data structure|(% style="width:1255px" %)(((
++|(% style="width:416px" %)**Many pure concepts**|(% style="width:1199px" %)**Few composite concepts**
++|(% style="width:416px" %)cleaner data structure|(% style="width:1199px" %)(((
  Mixed dimensions may be composed inconsistently making the decomposition into purer concepts and code lists difficult (requiring complex mapping etc.). Information that corresponds to the same concept may be included in different dimensions, e.g. reference year is contained in the indicator dimension in the first example but in the unit in the second example below. The optimal common data structure would consist of Economic Indicator, Unit, and Base period.
  [[image:1768469652632-803.png||height="106" width="352"]]
--
--
  )))
--|(% style="width:360px" %)shorter and simpler code lists|(% style="width:1255px" %)code lists longer and more complex, may require hierarchy to be “readable”
--|(% style="width:360px" %)more flexible in terms of defining constraints, but constraints more complex|(% style="width:1255px" %)simpler constraints, but some constraints may be difficult to be represented because of mixed dimensions. Consider for instance a constraint “Base period = 1995” in the above example, where some observations include the base period in the Economic Indicator dimension, others in the Unit dimension. Instead of specifying a constraint on a pure Base Period dimension, the constraints may have to be specified at observation (or time series) level
--|(% style="width:360px" %)more flexible in terms of mapping to other data structures (used by other systems), further processing and analysis (e.g. tabulation, dissemination format), and future needs|(% style="width:1255px" %)“mixed” dimensions make data structure less flexible in these respects
--|(% style="width:360px" %)longer (i.e. more complex) observation keys|(% style="width:1255px" %)shorter keys
--|(% style="width:360px" %)special values of code lists such as “not applicable”, “total” may be rather heavily used|(% style="width:1255px" %)less usage of these special values
--|(% style="width:360px" %)creates sparse data if many observations use “not applicable”|(% style="width:1255px" %)way to avoid sparseness
--|(% style="width:360px" %)many constraints may be necessary due to sparseness|(% style="width:1255px" %)typically fewer constraints required because data are less sparse
--|(% style="width:360px" %)many dimensions are tantamount to many attachment levels for attributes (i.e. DSD more flexible in terms of attribute attachment)|(% style="width:1255px" %)less dimensions = less possible attribute attachment levels
--|(% style="width:360px" %)more difficult to handle by an end user|(% style="width:1255px" %)presumably more easily comprehensible and manageable by an end user
--|(% style="width:360px" %)more flexible in terms of defining queries; can be mapped to any “mixed” representation|(% style="width:1255px" %)less flexible in terms of search and retrieval
++|(% style="width:416px" %)shorter and simpler code lists|(% style="width:1199px" %)code lists longer and more complex, may require hierarchy to be “readable”
++|(% style="width:416px" %)more flexible in terms of defining constraints, but constraints more complex|(% style="width:1199px" %)simpler constraints, but some constraints may be difficult to be represented because of mixed dimensions. Consider for instance a constraint “Base period = 1995” in the above example, where some observations include the base period in the Economic Indicator dimension, others in the Unit dimension. Instead of specifying a constraint on a pure Base Period dimension, the constraints may have to be specified at observation (or time series) level
++|(% style="width:416px" %)more flexible in terms of mapping to other data structures (used by other systems), further processing and analysis (e.g. tabulation, dissemination format), and future needs|(% style="width:1199px" %)“mixed” dimensions make data structure less flexible in these respects
++|(% style="width:416px" %)longer (i.e. more complex) observation keys|(% style="width:1199px" %)shorter keys
++|(% style="width:416px" %)special values of code lists such as “not applicable”, “total” may be rather heavily used|(% style="width:1199px" %)less usage of these special values
++|(% style="width:416px" %)creates sparse data if many observations use “not applicable”|(% style="width:1199px" %)way to avoid sparseness
++|(% style="width:416px" %)many constraints may be necessary due to sparseness|(% style="width:1199px" %)typically fewer constraints required because data are less sparse
++|(% style="width:416px" %)many dimensions are tantamount to many attachment levels for attributes (i.e. DSD more flexible in terms of attribute attachment)|(% style="width:1199px" %)less dimensions = less possible attribute attachment levels
++|(% style="width:416px" %)more difficult to handle by an end user|(% style="width:1199px" %)presumably more easily comprehensible and manageable by an end user
++|(% style="width:416px" %)more flexible in terms of defining queries; can be mapped to any “mixed” representation|(% style="width:1199px" %)less flexible in terms of search and retrieval
  The latter two aspects mentioned in the table could be summarized as the “many pure dimensions” approach being more difficult to handle for a “basic” user, but providing fewer options for an “advanced” user. When it comes to dissemination to end users, a purer data structure is the appropriate format for consumption by applications and advanced users. For less advanced user groups it makes sense to hide the (for them: unnecessary) complexity by means of concatenating dimensions, for instance to create a time series view.
@@ -298,15 +298,14 @@
  **Table 6. Data structuring approaches by role in data exchange**
--|**Role in data exchange**|**Pure vs. composite concepts approach**
--|**Data provider**|(((
++|(% style="width:215px" %)**Role in data exchange**|(% style="width:1400px" %)**Pure vs. composite concepts approach**
++|(% style="width:215px" %)**Data provider**|(% style="width:1400px" %)(((
  If the composition of the concepts in the data provider's production system largely differs from the one in the DSD, mapping it to a few composite concepts may be more complex than mapping it to many pure concepts. (Mapping to just one mixed concept is straightforward, though.) This is due to the need to decompose and recombine concepts in case of a “mixed concepts” DSD. If the data provider’s internal data structure is very granular or very similar to the DSD, it does not make a huge difference if the concepts in that DSD are pure or not.
--
  For a “final” data provider disseminating data to the public, the flexibility offered by a pure data structure in terms of defining different output formats may be beneficial.
  )))
--|**Data collector**|Defining constraints for data validation is more complex for a highdimensional, pure DSD. But such a DSD provides more flexibility in terms of consumption and reuse, i.e. mapping to the data collector’s internal data model mapping easier.
--|**DSD maintenance**|Pure concepts usually have shorter, less complex code lists and are thus easier to maintain. In contrast, the maintenance of constraints, hierarchical code lists, and derived, composite concepts (e.g. for dissemination) requires more effort.
--|**End user (“the public”)**|Consumption and reuse are more flexible in a pure data structure, but it is more difficult to identify observation keys that actually have data because of the created sparseness. (Constraints may help in this respect.) Frequent occurrences of “non applicable” values may also make data usage cumbersome.
++|(% style="width:215px" %)**Data collector**|(% style="width:1400px" %)Defining constraints for data validation is more complex for a highdimensional, pure DSD. But such a DSD provides more flexibility in terms of consumption and reuse, i.e. mapping to the data collector’s internal data model mapping easier.
++|(% style="width:215px" %)**DSD maintenance**|(% style="width:1400px" %)Pure concepts usually have shorter, less complex code lists and are thus easier to maintain. In contrast, the maintenance of constraints, hierarchical code lists, and derived, composite concepts (e.g. for dissemination) requires more effort.
++|(% style="width:215px" %)**End user (“the public”)**|(% style="width:1400px" %)Consumption and reuse are more flexible in a pure data structure, but it is more difficult to identify observation keys that actually have data because of the created sparseness. (Constraints may help in this respect.) Frequent occurrences of “non applicable” values may also make data usage cumbersome.
  == 4.2 Number and relations of DSDs ==