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Focused on energy statistics and relation IRES Hans Pouwelse Statistics Netherlands

Statistical process as a structured chain of successive actions and intermediate products, supported by the coherent use of metadata . Focused on energy statistics and relation IRES Hans Pouwelse Statistics Netherlands. content. Brief description Business Architecture statistical process

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Focused on energy statistics and relation IRES Hans Pouwelse Statistics Netherlands

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  1. Statistical process as a structured chain of successive actions and intermediate products, supported by the coherent use of metadata  Focused on energy statistics and relation IRES Hans Pouwelse Statistics Netherlands

  2. content • Brief description Business Architecture statistical process • Role of coherent meta data • Focused on Energy Statistics • Relation to IRES

  3. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  4. Conceptual metadataOutput world • Definitions variables and classifications in output terms (‘language’ of users) = definitions rows en colums output tables • Reporting period (year, month…) • Units of measurement • Statistical units (micro level output register)

  5. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  6. Conceptual metadataInput world Questionnaires • Design questionnaires: questions, definitions (in the ‘language’ of respondents) • Reporting period • Observation units (observable units) Registrations (‘administrative data’) • Definitions of variables and type of units

  7. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  8. Process metadata To provide methods and rules for the process to go from stage to stage (from database to database) • Sampling scemes • Methods and rules for editing, validation, imputation, aggregation and disclosure control • Transformation rules to bridge the gap between input concepts and output concepts

  9. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  10. Qualtity metadata • Define quality standards (required output quality) • Rules to measure resulting quality

  11. Focused on Energy Statistics • Energy statistics are normal statistics: logic stages sceme and metadata fully applicable to energy statistics • Some elements specific for energy statistics:

  12. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - - National energy policy International EU (en stat reg) IEA (ESM) UN (IRES)

  13. Conceptual metadataOutput world • Classification Energy products(IRES chapter 3 (SIEC); InterEnerStat, ESM) • Classification Energy Flows, Energy balance(IRES chapter 5 and 8; InterEnerStat, ESM) • Classification economic activity(ISIC, NACE) • Joint Annual Quest, JODI, MOS etc • Units of measurement (Joule, toe, tonnes, kWh etc.) (IRES chapter 4) • Caloric values (IRES chapter 4)

  14. Conceptual metadataInput world Questionnaires • Design energy questionnaires Neth: commodity/energy balance format Registrations (‘administrative data’) • Definitions of variables and type of unitsNeth: client files energy companies (unit= connection adress)

  15. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - - National energy policy International EU (en stat reg) IEA (ESM) UN (IRES)

  16. Relation to IRES • Attempt to link IRES chapters with a logical place in the business arcitecture sceme:

  17. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S U S E R S Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a Chapters IRES 3, 4, 5, 8 6b? The Statistical Process 6b? 6a? 6a? - - - 1,2 11 7a 10 9 Metadata Quality 7b?

  18. summary (1) • Statistical process seen as a logical output oriented cycle • Starts with users (identification user needs) • Ends with users (provide desired statistical results) • Structured chain of successive actions • Delimited by intermediate products (logical databases) • Supported by the coherent use of metadata (conceptual, process, quality)

  19. summary (2) • Important to make clear distinction between input world and output world • Explicitly bridge the gap between input concepts and output concepts:-input definitions output definitions-observation units statistical units

  20. summary (3) • Logical model applicable to energy statistics (as being normal statistics) • IRES may be structured according to the lines of the model • Which seems not completely be the case right now! (in particular: distinction input/output world)

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