Changes for page Guidelines for SDMX Data Structure Definitions

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

From 1.11 to 1.12 From 1.14 to 1.15

From version 1.12

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

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To version 1.14

edited by Helena K.
on 2026/01/15 12:44

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@@ -325,36 +325,22 @@
  **Table 7. Data structuring approaches by level of data exchange**
--|**Level of data exchange**|**Data structuring approa one DSD**|(% colspan="2" %)(((
--**ch**
--
--**master + satellite DSDs**
--)))|**multiple, indep. DSDs**
++|(% colspan="1" rowspan="2" %)**Level of data exchange**|(% colspan="4" rowspan="1" %)**Data structuring approach**
++|**one DSD**|(% colspan="2" %)**master + satellite DSDs**|**multiple, indep. DSDs**
  |**within organization**|(((
--best for single-domain, single-purpose can be created on the
--
--fly from structured databases
++best for single-domain, single-purpose can be created on the fly from structured databases
  )))|(% colspan="2" %)use if harmonization is important in covered domains or purposes or if such a set of DSDs is already available at international level|easier to do than master + satellite approach each domain/purpose can maintain DSDs independently can be created on the fly from structured databases
  |**between national organizations**|(% colspan="4" %)the same applies as to the “within organization” scenario
--|**Level of data exchange**|(% colspan="3" %)(((
--**Data structuring approach**
--
--**one DSD                        master + satellite DSDs**
--)))|**multiple, indep. DSDs**
  |**between int. organization and national organizations**|(% colspan="2" %)best for single domain, single purpose scenarios that are usually rather restricted with very clear specification of what needs to be exchanged|preferable over multiDSD approach in case of multi-domain and/or multi-purpose scenarios with highly correlated data flows for maintenance reasons|(((
--for multi-domain and/or multipurpose scenarios; only recommended if overlap of domains/purposes is minor (e.g. just w.r.t. cross-domain concepts)
--
--equivalent to multiple “one DSD” solutions, one for each domain / purpose
++for multi-domain and/or multipurpose scenarios; only recommended if overlap of domains/purposes is minor (e.g. just w.r.t. cross-domain concepts) equivalent to multiple “one DSD” solutions, one for each domain / purpose
  )))
  |**between international organizations**|(% colspan="3" %)comparable to “national to international” scenario|
  |**dissemination to public**|(% colspan="2" %)for single-domain, single-purpose cases in more complex cases this may be the preferable approach for data discovery tools (one data structure to find and access all data)|(% colspan="2" %)(((
  in multi-purpose or –domain scenarios:
--if it is relevant for the public to see the relationship between the data structures: use master + satellites approach
--
--otherwise the multi-DSD option is preferable, although with the highest possible degree of re-use of code lists and concepts
--
--in both cases: important to include only concepts, code lists, and codes actually available / used by the data
++* if it is relevant for the public to see the relationship between the data structures: use master + satellites approach
++* otherwise the multi-DSD option is preferable, although with the highest possible degree of re-use of code lists and concepts
++* in both cases: important to include only concepts, code lists, and codes actually available / used by the data
  )))
  In general, finding the “perfect” data structure is less important for bilateral data exchange. Independent, custom-tailored DSDs may do the job quite well, as harmonization and standardization are typically not of high importance. If the data exchange is just a part of a more comprehensive scenario (e.g. multi-purpose, multi-domain, gateway, or data-sharing scenarios), a master DSD with satellite DSDs is preferable.
@@ -363,20 +363,17 @@
  **Table 8. Data structuring approaches by role in data exchange**
--|**Role in data exchange**|**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
--|**Data provider**|It is easier to set up a data submission process against a single DSD (= less initial costs) than against multiple DSDs.
--|**Data collector**|(((
++|(% style="width:216px" %)**Role in data exchange**|(% style="width:1399px" %)**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
++|(% style="width:216px" %)**Data provider**|(% style="width:1399px" %)It is easier to set up a data submission process against a single DSD (= less initial costs) than against multiple DSDs.
++|(% style="width:216px" %)**Data collector**|(% style="width:1399px" %)(((
  Data validation is easier with DSDs that only cover what needs to be collected. This is achieved via constraints in the master + satellites approach or via tailor-made independent DSDs. If a single DSD is used in a multi-domain or –purpose scenario, necessary constraints can be specified in the data flow definition or data provision agreement.
--
  Further processing of collected data is more flexible and easier if relations are transparent and code lists are shared as in the one DSD or master + satellite DSDs approaches. The “shared context” created through the master DSD increases harmonization and standardization and this way facilitates combined usage of data.
  )))
--|**Role in data exchange**|**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
--|**DSD maintenance**|(((
++|(% style="width:216px" %)**DSD maintenance**|(% style="width:1399px" %)(((
  The complexity and initial costs for developing and maintaining master + satellite DSDs are higher than for independent DSDs as this involves managing constraints and managing impacts of changes in shared code lists to all DSDs.
--
  In the multiple independent DSDs approach, development and maintenance efforts may be distributed. This can be seen as an advantage, but on the other hand requires coordination in case the DSDs are only partially independent (i.e. share some code lists).
  )))
--|**End user (“the public”)**|For data discovery and retrieval the user needs to know what data is actually available (instead of what might be collected/disseminated with a certain data structure). This means that the potential sparseness should be hidden from the user. A reduced DSD derived from the data structure used in the background is more useful in most cases. Whether this is done via one DSD and constraints, master + satellite DSDs, or independent DSDs does not matter that much for the user.
++|(% style="width:216px" %)**End user (“the public”)**|(% style="width:1399px" %)For data discovery and retrieval the user needs to know what data is actually available (instead of what might be collected/disseminated with a certain data structure). This means that the potential sparseness should be hidden from the user. A reduced DSD derived from the data structure used in the background is more useful in most cases. Whether this is done via one DSD and constraints, master + satellite DSDs, or independent DSDs does not matter that much for the user.
  = 5 MINIMUM STRUCTURAL AND SEMANTIC REQUIREMENTS =