Changes for page Guidelines for SDMX Data Structure Definitions

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

From 1.11 to 1.10 From 4.1 to 3.3

From version 3.3

edited by Helena K.
on 2026/01/15 13:11

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To version 1.11

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

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@@ -61,7 +61,7 @@
  === 2.1.3 Suitability of available DSDs and code lists ===
--In case an existing DSD is close to but differs from what is needed, it may: {{{(i)}}} contain irrelevant concepts, (ii) lack some required concepts, (iii) use the concepts in different roles than required, (iv) deviate with respect to some of the code lists, or (v) contain pure dimensions when mixed dimensions would make more sense or vice versa. More complex situations that are combinations of several (or even all) of these five cases may occur as well. For example, an existing DSD could contain unnecessary concepts and lack other concepts at the same time.
++In case an existing DSD is close to but differs from what is needed, it may: (i) contain irrelevant concepts, (ii) lack some required concepts, (iii) use the concepts in different roles than required, (iv) deviate with respect to some of the code lists, or (v) contain pure dimensions when mixed dimensions would make more sense or vice versa. More complex situations that are combinations of several (or even all) of these five cases may occur as well. For example, an existing DSD could contain unnecessary concepts and lack other concepts at the same time.
  ==== 2.1.3.1 Irrelevant concepts ====
@@ -243,7 +243,7 @@
  The decision on content and number of concepts in a DSD usually leads to the question of how far the “//indicator//” dimension should be decomposed. There are some (cross-domain) concepts, such as geographical and temporal reference and unit of measure, that are relevant in most DSDs. Once those are defined (the usage of the SDMX COG is highly recommended!) the actual “//subject-matter//” or “//domain//” concepts remain. One option is to combine all those concepts into one “indicator” dimension which may make sense in certain scenarios, for example for smaller single-domain, single-purpose DSDs with few or no crossclassifications or for display in an end-user dissemination tool. The other extreme strategy is to decompose into as many components as possible by splitting any breakdown concepts from the core indicator concept.
--The range of options between the “//just one//” (mixed) and “//all component//” subject-matter dimensions approaches is subject to the comprehensiveness (i.e. size, coverage) of the data exchange that the DSD is being developed for. If using a “mixed dimensions” approach, rules for the composition of the mixed dimension(s) may be specified (e.g. concatenate concepts A, B, and C to get mixed dimension X), allowing their easy re-decomposition. In general composite dimensions should be avoided as previously recommended by the SDMX Technical Notes, but there are cases that suggest the usage of composite dimensions. Table 4 juxtaposes general pros and cons of the “//many pure concepts//” and “//fewer composite concepts//” approaches.
++The range of options between the “//just one//”// //(mixed) and “//all component//” subject-matter dimensions approaches is subject to the comprehensiveness (i.e. size, coverage) of the data exchange that the DSD is being developed for. If using a “mixed dimensions” approach, rules for the composition of the mixed dimension(s) may be specified (e.g. concatenate concepts A, B, and C to get mixed dimension X), allowing their easy re-decomposition. In general composite dimensions should be avoided as previously recommended by the SDMX Technical Notes, but there are cases that suggest the usage of composite dimensions. Table 4 juxtaposes general pros and cons of the “//many pure concepts//” and “//fewer composite concepts//” approaches.
  **Table 4. General comparison of data structuring approaches**
@@ -296,14 +296,15 @@
  **Table 6. Data structuring approaches by role in data exchange**
--|(% style="width:215px" %)**Role in data exchange**|(% style="width:1400px" %)**Pure vs. composite concepts approach**
--|(% style="width:215px" %)**Data provider**|(% style="width:1400px" %)(((
++|**Role in data exchange**|**Pure vs. composite concepts approach**
++|**Data provider**|(((
  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.
  )))
--|(% 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.
++|**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.
  == 4.2 Number and relations of DSDs ==
@@ -315,7 +315,7 @@
  The “one DSD” approach works best for single-domain and/or single-purpose scenarios. In more complex scenarios, more complex approaches are more suitable. Usage of the “one DSD” approach in a multi-domain or multi-purpose scenario actually means that one master DSD containing all concepts, code lists, and codes relevant in any (but most likely not all) domains and/or purposes is used by all domains and/or purposes without constraints. If a “many pure concepts” approach is used, the DSD will be sparse and require many “not applicable” values or structure maps.
--In those more complex scenarios, multi-DSD approaches have more potential. The “master DSD + satellite DSDs” approach imposes more restrictions and aims at a higher degree of content harmonization than the more loosely coupled (or even independent) multi-DSD approach. While the former specifies the concepts and code lists to be used by all derived DSDs, the latter is more flexible. Therefore, the master + satellites approach is suggested for data exchange scenarios with a high degree of harmonization/standardization required such as at the international level or between national and international organizations. Please note that what is termed “master DSD + satellite DSDs” approach here may also be implemented as master DSD plus constrained data flows with or without using structure maps.
++In those more complex scenarios, multi-DSD approaches have more potential. The “master DSD + satellite DSDs” approach imposes more restrictions and aims at a higher degree of content harmonization than the more loosely coupled (or even independent) multi-DSD approach. While the former specifies the concepts and code lists to be used by all derived DSDs, the latter is more flexible. Therefore, the master + satellites approach is suggested for data exchange scenarios with a high degree of harmonization / standardization required such as at the international level or between national and international organizations. Please note that what is termed “master DSD + satellite DSDs” approach here may also be implemented as master DSD plus constrained data flows with or without using structure maps.
  Even in the multiple independent DSDs approach, sharing of concepts and code lists by reference is recommended. This may be problematic if additional codes are needed by certain DSDs, as neither the addition of codes to a code list used by reference nor the concatenation of multiple code lists included by reference is supported by the current SDMX Technical Standards. The only way of implementing “combined” code lists by reference is to reference each single code from each relevant partial code list.
@@ -325,22 +325,36 @@
  **Table 7. Data structuring approaches by level of data exchange**
--|(% 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**
++|**Level of data exchange**|**Data structuring approa one DSD**|(% colspan="2" %)(((
++**ch**
++
++**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.
@@ -349,17 +349,20 @@
  **Table 8. Data structuring approaches by role in data exchange**
--|(% 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" %)(((
++|**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**|(((
  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.
  )))
--|(% style="width:216px" %)**DSD maintenance**|(% style="width:1399px" %)(((
++|**Role in data exchange**|**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
++|**DSD maintenance**|(((
  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).
  )))
--|(% 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.
++|**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.
  = 5 MINIMUM STRUCTURAL AND SEMANTIC REQUIREMENTS =
@@ -367,7 +367,7 @@
  Certain concepts can be broadly agreed upon as being relevant in any data exchange, although their roles may differ between scenarios. The SDMX Content-Oriented Guidelines define many of these cross-domain concepts and, thus, should be referred to for further details on their specification.
--In general, multi-purpose and multi-domain scenarios may require more concepts than single-purpose and/or – domain scenarios. This mainly applies to subject-matter (or domainspecific) concepts and concepts that inform about the data source, provider, or process.
++In general, multi-purpose and multi-domain scenarios may require more concepts than single-purpose and/or –domain scenarios. This mainly applies to subject-matter (or domainspecific) concepts and concepts that inform about the data source, provider, or process.
  Exchanges between organizations, especially on an international level, typically require more concepts to cover context information, as data are transferred out of their usual context, meaning that users in the new context do not have the same knowledge of the data and may need additional background information. For exchanges of data within an organization, some context information may be common (implicit) knowledge so that it does not need to be made explicit in the data structure.
@@ -389,20 +389,19 @@
  **Table 9. Minimum requirements for DSDs~*~***
--(% style="width:1308.83px" %)
--|(% style="width:205px" %)**Question**|(% style="width:272px" %)**Concept**|(% style="width:178px" %)**COG**|(% style="width:270px" %)**Code list**|(% style="width:290px" %)**Time series**|(% style="width:221px" %)**Cross-section**
--|(% style="width:205px" %)Where?|(% style="width:272px" %)reference area|(% style="width:178px" %)X|(% style="width:270px" %)revision|(% colspan="2" rowspan="1" style="width:478px" %)mand. attribute or dimension
--|(% style="width:205px" %)What?|(% style="width:272px" %)“indicator”|(% style="width:178px" %)-|(% style="width:270px" %)domain|(% colspan="2" rowspan="1" style="width:478px" %)one or multiple dimensions
--|(% style="width:205px" %)How?|(% style="width:272px" %)unit of measure|(% style="width:178px" %)X|(% style="width:270px" %)development|(% colspan="2" rowspan="1" style="width:478px" %)mand. attribute or dimension
--|(% style="width:205px" %)How?|(% style="width:272px" %)unit multiplier|(% style="width:178px" %)X|(% style="width:270px" %)available|(% colspan="2" rowspan="1" style="width:478px" %)mandatory attribute
--|(% style="width:205px" %)How?|(% style="width:272px" %)decimals|(% style="width:178px" %)X|(% style="width:270px" %)available|(% colspan="2" rowspan="1" style="width:478px" %)mandatory attribute
--|(% style="width:205px" %)How?|(% style="width:272px" %)//adjustment//|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:290px" %)mand. att.|(% style="width:221px" %) not relevant
--|(% style="width:205px" %)When?|(% style="width:272px" %)time period|(% style="width:178px" %)X|(% style="width:270px" %)format|(% style="width:290px" %)dimension|(% style="width:221px" %)mand. att.
--|(% style="width:205px" %)When?|(% style="width:272px" %)time format|(% style="width:178px" %)X|(% style="width:270px" %)available|(% colspan="2" rowspan="1" style="width:478px" %)mandatory attribute
--|(% style="width:205px" %)When?|(% style="width:272px" %)time period – collection|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:290px" %)mand. att.|(% style="width:221px" %)cond. att.
--|(% style="width:205px" %)When?|(% style="width:272px" %)data update – last update|(% style="width:178px" %)X|(% style="width:270px" %)time stamp|(% colspan="2" rowspan="1" style="width:478px" %)mandatory attribute
--|(% style="width:205px" %)How often?|(% style="width:272px" %)//frequency//|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:290px" %)mand. att. or|(% style="width:221px" %)not relevant
--|(% style="width:205px" %)How much?|(% style="width:272px" %)observation value|(% style="width:178px" %)-|(% style="width:270px" %)numeric|(% colspan="2" rowspan="1" style="width:290px" %) measure
++|**Question**|**Concept**|**COG**|**Code list**|**Time series      Cross-section**
++|Where?|reference area|X|revision|mand. attribute or dimension
++|What?|“indicator”|-|domain|one or multiple dimensions
++|How?|unit of measure|X|development|mand. attribute or dimension
++|How?|unit multiplier|X|available|mandatory attribute
++|How?|decimals|X|available|mandatory attribute
++|How?|//adjustment//|X|development|mand. att.        not relevant
++|When?|time period|X|format|dimension       mand. att.
++|When?|time format|X|available|mandatory attribute
++|When?|time period – collection|X|development|mand. att.         cond. att.
++|When?|data update – last update|X|time stamp|mandatory attribute
++|How often?|//frequency//|X|available|mand. att. or    not relevant
++|(% colspan="2" %)How much? observation value|-|numeric|dimension measure
  ~*~*Concepts in //italics// are only relevant for time series DSDs. An “X” in the COG column means the concept is defined in the COG. Code list “development” means that the SWG will develop a code list to be recommended in the COG; “revision” means that the code list is recommended by the COG and under revision by the SWG; “format” means that a format is defined by another concept; “text”, “time stamp”, and “numeric” provide data types used for uncoded concepts.
@@ -410,19 +410,25 @@
  **Table 10. Suggested additional concepts for certain scenarios~*~***
--|**Question**|**Concept**|**COG**|**Code list**|**TS**|**CS**|**Scenario**
++|**Question**|**Concept**|**COG**|**Code list**|**TS                  CS**|**Scenario**
  |Who?|compiling agency|X|development|(((
--conditional (sibling)
--)))|conditional (obs. level)|data provider different from data compiler
++conditional          conditional
++
++    (sibling)          (obs. level)
++)))|data provider different from data compiler
  |Who?|(((
--confidentiality status – observation
--)))|X|available|(% colspan="2" rowspan="1" %)mandatory (obs. level)|except dissemination
--|How?|observation status|X|available|(% colspan="2" rowspan="1" %)conditional (obs. level)|except orig. collection
++confidentiality
++
++status – observation
++)))|X|available|mandatory (obs. level)|except dissemination
++|How?|observation status|X|available|conditional (obs. level)|except orig. collection
  |How much?|(((
--//observation pre-break value//
--)))|-|numeric|cond. (obs.)|not relevant|except orig. collection
--|What and how?|//time series title//|X|text|cond. (TS)|not relevant|dissemination
++//observation pre-//
++//break value//
++)))|-|numeric|cond. (obs.)      not relevant|except orig. collection
++|What and how?|//time series title//|X|text|cond. (TS)        not relevant|dissemination
++
  ~** The legend of Table 9 applies to Table 10 as well. The suggested attachment level of attributes (if any) is provided in parentheses in the TS (time series) or CS (cross-section) columns. In case an attribute does not vary at that level in a certain use case, it should be attached at the highest possible level.
  == 5.2 Attribute attachment levels and definition of groups ==
@@ -444,8 +444,10 @@
  * //ID//: a unique identifier of the message
  * //Test//: a Boolean attribute that indicates whether the message is for test purposes or not
  * //Prepared//: the date the message was prepared
--* //Sender//: the identification of the organization that is transmitting the message (recommended: code from the agency code list in the SDMX COG)
++* //Sender//: the identification of the organization that is transmitting the message
++(recommended: code from the agency code list in the SDMX COG)
++
  From a business perspective, the inclusion of the //Name// element is highly recommended, as it can help to understand the purpose of the exchange message. Other header elements such as //Receiver// are optional.
  = 6 STEP-BY-STEP GUIDE =
@@ -456,15 +456,13 @@
  Figure 1 provides an overview of the overall process. As a first step, the context of the data exchange(s) that should be covered by the DSD(s) is defined in terms of purpose, domains, level of exchange, type of data, type of recipient, role of in data exchange, process pattern, and GSBPM phase (see Figure 2). Since reusing existing artefacts is one of the guiding principles, the second step identifies existing DSDs that may be reused (see Figure 3). In case relevant DSDs are available, their suitability in the present context is evaluated in step 3. Aspects to be taken into account are concept coverage, concept roles, attribute attachment levels, and code lists (see Figure 4). Step 4 is subject to the outcome of step 3. In case of a favorable assessment, the DSDs are simply reused. If the DSDs are partly suitable, modified versions can be derived. See section 2. for a summary of possible DSD modification scenarios. If the DSDs are not suitable or if no relevant DSDs are available at all, new DSDs will be defined as described in section 3. Finally, supporting artefacts such as data flow definitions and data provision agreements are defined (see Figure 5).
--(% class="wikigeneratedid" %)
--[[image:1768470533088-795.png]]
  (% class="wikigeneratedid" id="HFigure1.OverviewoftheDSDdesignprocess" %)
  Figure 1. Overview of the DSD design process
++
  Figure 2 summarizes the characteristics of the data exchange context that is defined in step 1. These characteristics affect the decision on the data structuring approach that is part of the process of defining the concepts of a new DSD (step 4.3. in Figure 1; see Figure 7 in section 2.).
--[[image:1768470575978-226.png]]
  (% class="wikigeneratedid" id="HFigure2.Characteristicsofdataexchangecontext" %)
  Figure 2. Characteristics of data exchange context
@@ -471,23 +471,20 @@
  Figure 3 recaps the priorities given to different types of existing DSDs when searching for candidates for reuse in step 2. Global DSDs maintained by the SDMX consortium are ranked the highest. They can be found via the Global SDMX Registry.
--(% class="wikigeneratedid" %)
--[[image:1768470596130-305.png]]
  (% class="wikigeneratedid" id="HFigure3.PriorityrankingofexistingDSDsforreuse" %)
  Figure 3. Priority ranking of existing DSDs for reuse
++
  Figure 4 summarizes the aspects to be considered in the assessment of the suitability of existing DSDs in step 3. For a detailed description of the cases of partial unsuitability see section 2.1. above.
--(% class="wikigeneratedid" %)
--[[image:1768470626558-321.png]]
  (% class="wikigeneratedid" id="HFigure4.AspectsofDSDsuitability" %)
  Figure 4. Aspects of DSD suitability
++
  Figure 5 lists the most relevant artefacts required in addition to a DSD, its concept scheme, and code lists.
--[[image:1768470646456-652.png]]
  Figure 5. Supporting artefacts
@@ -495,83 +495,48 @@
  Figure 6 briefly recapitulates the actions that can be taken to overcome partial unsuitability of DSDs. As far as possible, existing artefacts should be reused in this case. This means that even if a DSD cannot be reused as a whole, concepts and code lists from that DSD can be included in the new DSD by reference.
--[[image:1768470678965-391.png]]
++**Figure 6. DSD modification scenarios**
--Figure 6. DSD modification scenarios
--
  == 6.3 Defining new DSDs ==
  In case no (suitable) DSD is available, the actual process of specifying a new DSD is started. Figure 7 depicts this process (step 4.3. in Figure 1). It encompasses the specification of concepts, code lists, and data formats. All three specification steps include the identification of already existing artefacts that could be reused or modified to satisfy the requirements at hand and the definition of new artefacts in case no suitable artefacts are detected. Several iterations of steps 1 (specification of concepts; see Figure 8) and 2 (specification of code lists; see Figure°13) may be necessary, including revisions of the decision concerning the data structuring approach. Finally all artefacts defined in the previous steps are put together into a DSD.
--(% class="wikigeneratedid" %)
--[[image:1768470705894-724.png]]
++==== Figure 7. New DSD specification process ====
--(% class="wikigeneratedid" id="HFigure7.NewDSDspecificationprocess" %)
--Figure 7. New DSD specification process
--
  Figure 8 outlines step 4.3.1, the process of concept specification. It covers the decision on the structuring approach, the identification of relevant concepts and the assessment of their suitability, the definition of new concepts, concept roles, and attribute attachment levels.
--(% class="wikigeneratedid" %)
--[[image:1768470729899-225.png]]
++==== Figure 8. Concept specification process ====
--(% class="wikigeneratedid" id="HFigure8.Conceptspecificationprocess" %)
--Figure 8. Concept specification process
--
  Both, the decision on reuse of existing concepts as well as the definition of new ones, may lead back to a revision of the data structuring approach. For example, it could turn out that a certain concept needs to be broken down further which may lead from a “few composite dimensions” to a “many pure dimensions” approach. Figure 9 provides the design options involved in the decision on a data structuring approach. The options are defined in terms of the number of DSDs and the number of concepts (especially dimensions). The reasonability and feasibility of these options depend on the context of the present data exchange(s) as defined in the first step of the overall design process and on the content of the data exchange with respect to concepts.
--(% class="wikigeneratedid" %)
--[[image:1768470752201-691.png]]
++==== Figure 9. DSD design options ====
--(% class="wikigeneratedid" id="HFigure9.DSDdesignoptions" %)
--Figure 9. DSD design options
--
  In the second step of new DSD design, relevant existing concepts are identified. Figure 10 indicates potential sources of those concepts such as the SDMX COG for cross-domain concepts, global or other DSDs as already identified earlier in the process, and domain standards such as the UN's System of National Accounts Manual 2008 for domain-specific concepts.
--(% class="wikigeneratedid" %)
--[[image:1768470775109-874.png]]
++==== Figure 10. Potential sources of concepts and definitions ====
--(% class="wikigeneratedid" id="HFigure10.Potentialsourcesofconceptsanddefinitions" %)
--Figure 10. Potential sources of concepts and definitions
--
  The definition of new concepts (step 4.3.1.4.2.) is necessary if no (suitable) concept can be reused. It entails giving each concept a name, a code, and a definition. Further details about the usage of the concepts in the DSD are specified in steps 4.3.1.5. (concept roles), 4.3.1.6. (dimension groups), and 4.3.1.7. (attribute attachment levels). Figure 11 and 12 summarize the possible concept roles and attribute attachment levels.
  The second step in the process of defining a new DSD is the specification of code lists for all coded concepts. All dimensions must be coded (with time being an exception to this rule); attributes may be coded. For uncoded concepts, a data format has to be specified. Existing formats may be reused or new ones defined. An example is the time format that is specified in the SDMX COG. Figure 13 illustrates the code list specification process. If no relevant and suitable code list exists, a new one will be defined or a partially suitable one will be adapted (see Figure 16). Suitable code lists can simply be reused via reference.
--[[image:1768470796725-270.png]]
--(% class="wikigeneratedid" %)
--Figure 11. Possible concept roles
--
--(% class="wikigeneratedid" %)
--[[image:1768470829131-599.png]]
--
--(% class="wikigeneratedid" %)
--Figure 12. Possible attribute attachment levels
--
--(% class="wikigeneratedid" %)
--[[image:1768470860119-204.png]]
--
  (% class="wikigeneratedid" id="HFigure13.Codelistspecificationprocess" %)
  Figure 13. Code list specification process
--(% class="wikigeneratedid" %)
++
  Figure 14 recaps the priorities given to different types of existing code lists when searching for candidates for reuse (step 4.3.2.1.). Code lists recommended by the SDMX COG (and maintained by the SDMX consortium) are ranked the highest.
--[[image:1768470878394-873.png]]
  (% class="wikigeneratedid" id="HFigure14.Priorityrankingofexistingcodelistsforreuse" %)
  Figure 14. Priority ranking of existing code lists for reuse
--(% class="wikigeneratedid" %)
++
  Figure 15 summarizes the aspects to be considered in the evaluation of the suitability of existing code lists (step 4.3.2.2.). Figure 16 summarizes the scenarios of adapting existing code lists that do not fully meet the specified needs (step 4.3.2.3.2). For a detailed description of the cases of partial unsuitability see section 2.1. above.
--[[image:1768470896763-366.png]]
  (% class="wikigeneratedid" id="HFigure15.Aspectsofcodelistsuitability" %)
  Figure 15. Aspects of code list suitability
--(% class="wikigeneratedid" %)
--[[image:1768470911321-123.png]]
  (% class="wikigeneratedid" id="HFigure16.Codelistmodificationscenarios" %)
  Figure 16. Code list modification scenarios
@@ -584,11 +584,8 @@
  Figure 17 provides an overview of all steps in the DSD design process as described in the previous subsections 1. to 3. Figure 18 compiles those steps into a checklist for DSD designers to help them make sure all aspects are considered.
--[[image:1768471052577-528.png]]
--
  Figure 17. DSD design process
--[[image:1768470939545-136.png]]
  Figure 18. Checklist for DSD design process
@@ -638,12 +638,10 @@
  == 9.2 Non-SDMX Documents ==
--6th Edition of the IMF's Balance of Payments Manual (BPM6). Available online at [[http:~~/~~/www.imf.org/external/pubs/ft/bop/2007/bopman6.htm>>https://http:www.imf.orgexternalpubsftbop2007bopman6.htm||target="_blank"]].
++6th Edition of the IMF's Balance of Payments Manual (BPM6). Available online at http:~/~/www.imf.org/external/pubs/ft/bop/2007/bopman6.htm.
--METIS: Generic Statistical Business Process Model (GSBPM). Available online at [[http:~~/~~/www1.unece.org/stat/platform/display/metis/The+Generic+Statistical+Business+Process+Model>>https://http:www1.unece.orgstatplatformdisplaymetisThe+Generic+Statistical+Business+Process+Model||target="_blank"]].
++METIS: Generic Statistical Business Process Model (GSBPM). Available online at http:~/~/www1.unece.org/stat/platform/display/metis/The+Generic+Statistical+Business+Process+Model. UN's System of National Accounts Manual 2008 (SNA2008). Available online at http:~/~/unstats.un.org/unsd/nationalaccount/sna2008.asp.
--UN's System of National Accounts Manual 2008 (SNA2008). Available online at [[http:~~/~~/unstats.un.org/unsd/nationalaccount/sna2008.asp>>https://http:unstats.un.orgunsdnationalaccountsna2008.asp||target="_blank"]].
--
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