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

From version 1.11
edited by Helena K.
on 2026/01/15 12:40
Change comment: There is no comment for this version
To version 1.16
edited by Helena K.
on 2026/01/15 12:48
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -296,15 +296,14 @@
296 296  
297 297  **Table 6. Data structuring approaches by role in data exchange**
298 298  
299 -|**Role in data exchange**|**Pure vs. composite concepts approach**
300 -|**Data provider**|(((
299 +|(% style="width:215px" %)**Role in data exchange**|(% style="width:1400px" %)**Pure vs. composite concepts approach**
300 +|(% style="width:215px" %)**Data provider**|(% style="width:1400px" %)(((
301 301  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.
302 -
303 303  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.
304 304  )))
305 -|**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.
306 -|**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.
307 -|**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.
304 +|(% 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.
305 +|(% 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.
306 +|(% 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.
308 308  
309 309  == 4.2 Number and relations of DSDs ==
310 310  
... ... @@ -326,36 +326,22 @@
326 326  
327 327  **Table 7. Data structuring approaches by level of data exchange**
328 328  
329 -|**Level of data exchange**|**Data structuring approa one DSD**|(% colspan="2" %)(((
330 -**ch**
331 -
332 -**master + satellite DSDs**
333 -)))|**multiple, indep. DSDs**
328 +|(% colspan="1" rowspan="2" %)**Level of data exchange**|(% colspan="4" rowspan="1" %)**Data structuring approach**
329 +|**one DSD**|(% colspan="2" %)**master + satellite DSDs**|**multiple, indep. DSDs**
334 334  |**within organization**|(((
335 -best for single-domain, single-purpose can be created on the
336 -
337 -fly from structured databases
331 +best for single-domain, single-purpose can be created on the fly from structured databases
338 338  )))|(% 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
339 339  |**between national organizations**|(% colspan="4" %)the same applies as to the “within organization” scenario
340 -|**Level of data exchange**|(% colspan="3" %)(((
341 -**Data structuring approach**
342 -
343 -**one DSD master + satellite DSDs**
344 -)))|**multiple, indep. DSDs**
345 345  |**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|(((
346 -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)
347 -
348 -equivalent to multiple “one DSD” solutions, one for each domain / purpose
335 +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
349 349  )))
350 350  |**between international organizations**|(% colspan="3" %)comparable to “national to international” scenario|
351 351  |**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" %)(((
352 352  in multi-purpose or –domain scenarios:
353 353  
354 -if it is relevant for the public to see the relationship between the data structures: use master + satellites approach
355 -
356 -otherwise the multi-DSD option is preferable, although with the highest possible degree of re-use of code lists and concepts
357 -
358 -in both cases: important to include only concepts, code lists, and codes actually available / used by the data
341 +* if it is relevant for the public to see the relationship between the data structures: use master + satellites approach
342 +* otherwise the multi-DSD option is preferable, although with the highest possible degree of re-use of code lists and concepts
343 +* in both cases: important to include only concepts, code lists, and codes actually available / used by the data
359 359  )))
360 360  
361 361  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.
... ... @@ -364,20 +364,17 @@
364 364  
365 365  **Table 8. Data structuring approaches by role in data exchange**
366 366  
367 -|**Role in data exchange**|**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
368 -|**Data provider**|It is easier to set up a data submission process against a single DSD (= less initial costs) than against multiple DSDs.
369 -|**Data collector**|(((
352 +|(% style="width:216px" %)**Role in data exchange**|(% style="width:1399px" %)**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
353 +|(% 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.
354 +|(% style="width:216px" %)**Data collector**|(% style="width:1399px" %)(((
370 370  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.
371 -
372 372  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.
373 373  )))
374 -|**Role in data exchange**|**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
375 -|**DSD maintenance**|(((
358 +|(% style="width:216px" %)**DSD maintenance**|(% style="width:1399px" %)(((
376 376  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.
377 -
378 378  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).
379 379  )))
380 -|**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.
362 +|(% 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.
381 381  
382 382  = 5 MINIMUM STRUCTURAL AND SEMANTIC REQUIREMENTS =
383 383  
... ... @@ -407,19 +407,19 @@
407 407  
408 408  **Table 9. Minimum requirements for DSDs~*~***
409 409  
410 -|**Question**|**Concept**|**COG**|**Code list**|**Time series Cross-section**
411 -|Where?|reference area|X|revision|mand. attribute or dimension
412 -|What?|“indicator”|-|domain|one or multiple dimensions
413 -|How?|unit of measure|X|development|mand. attribute or dimension
414 -|How?|unit multiplier|X|available|mandatory attribute
415 -|How?|decimals|X|available|mandatory attribute
416 -|How?|//adjustment//|X|development|mand. att. not relevant
417 -|When?|time period|X|format|dimension mand. att.
418 -|When?|time format|X|available|mandatory attribute
419 -|When?|time period – collection|X|development|mand. att. cond. att.
420 -|When?|data update – last update|X|time stamp|mandatory attribute
421 -|How often?|//frequency//|X|available|mand. att. or not relevant
422 -|(% colspan="2" %)How much? observation value|-|numeric|dimension measure
392 +|(% style="width:205px" %)**Question**|(% style="width:272px" %)**Concept**|(% style="width:178px" %)**COG**|(% style="width:270px" %)**Code list**|(% style="width:690px" %)**Time series Cross-section**
393 +|(% style="width:205px" %)Where?|(% style="width:272px" %)reference area|(% style="width:178px" %)X|(% style="width:270px" %)revision|(% style="width:690px" %)mand. attribute or dimension
394 +|(% style="width:205px" %)What?|(% style="width:272px" %)“indicator”|(% style="width:178px" %)-|(% style="width:270px" %)domain|(% style="width:690px" %)one or multiple dimensions
395 +|(% style="width:205px" %)How?|(% style="width:272px" %)unit of measure|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:690px" %)mand. attribute or dimension
396 +|(% style="width:205px" %)How?|(% style="width:272px" %)unit multiplier|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mandatory attribute
397 +|(% style="width:205px" %)How?|(% style="width:272px" %)decimals|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mandatory attribute
398 +|(% style="width:205px" %)How?|(% style="width:272px" %)//adjustment//|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:690px" %)mand. att. not relevant
399 +|(% style="width:205px" %)When?|(% style="width:272px" %)time period|(% style="width:178px" %)X|(% style="width:270px" %)format|(% style="width:690px" %)dimension mand. att.
400 +|(% style="width:205px" %)When?|(% style="width:272px" %)time format|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mandatory attribute
401 +|(% style="width:205px" %)When?|(% style="width:272px" %)time period – collection|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:690px" %)mand. att. cond. att.
402 +|(% style="width:205px" %)When?|(% style="width:272px" %)data update – last update|(% style="width:178px" %)X|(% style="width:270px" %)time stamp|(% style="width:690px" %)mandatory attribute
403 +|(% style="width:205px" %)How often?|(% style="width:272px" %)//frequency//|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mand. att. or not relevant
404 +|(% colspan="2" style="width:477px" %)How much? observation value|(% style="width:178px" %)-|(% style="width:270px" %)numeric|(% style="width:690px" %)dimension measure
423 423  
424 424  ~*~*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.
425 425  
... ... @@ -427,25 +427,19 @@
427 427  
428 428  **Table 10. Suggested additional concepts for certain scenarios~*~***
429 429  
430 -|**Question**|**Concept**|**COG**|**Code list**|**TS CS**|**Scenario**
412 +|**Question**|**Concept**|**COG**|**Code list**|**TS**|**CS**|**Scenario**
431 431  |Who?|compiling agency|X|development|(((
432 -conditional conditional
433 -
434 - (sibling) (obs. level)
435 -)))|data provider different from data compiler
414 +conditional (sibling)
415 +)))|conditional (obs. level)|data provider different from data compiler
436 436  |Who?|(((
437 -confidentiality
438 -
439 -status – observation
440 -)))|X|available|mandatory (obs. level)|except dissemination
441 -|How?|observation status|X|available|conditional (obs. level)|except orig. collection
417 +confidentiality status – observation
418 +)))|X|available|(% colspan="2" rowspan="1" %)mandatory (obs. level)|except dissemination
419 +|How?|observation status|X|available|(% colspan="2" rowspan="1" %)conditional (obs. level)|except orig. collection
442 442  |How much?|(((
443 -//observation pre-//
421 +//observation pre-break value//
422 +)))|-|numeric|cond. (obs.)|not relevant|except orig. collection
423 +|What and how?|//time series title//|X|text|cond. (TS)|not relevant|dissemination
444 444  
445 -//break value//
446 -)))|-|numeric|cond. (obs.) not relevant|except orig. collection
447 -|What and how?|//time series title//|X|text|cond. (TS) not relevant|dissemination
448 -
449 449  ~** 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.
450 450  
451 451  == 5.2 Attribute attachment levels and definition of groups ==
... ... @@ -467,10 +467,8 @@
467 467  * //ID//: a unique identifier of the message
468 468  * //Test//: a Boolean attribute that indicates whether the message is for test purposes or not
469 469  * //Prepared//: the date the message was prepared
470 -* //Sender//: the identification of the organization that is transmitting the message
446 +* //Sender//: the identification of the organization that is transmitting the message (recommended: code from the agency code list in the SDMX COG)
471 471  
472 -(recommended: code from the agency code list in the SDMX COG)
473 -
474 474  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.
475 475  
476 476  = 6 STEP-BY-STEP GUIDE =
© Semantic R&D Group, 2026