1. Implementation of KSBPM in KOSTAT April 2013 Ki-bong Park

2. Contents I.Background II.Development of KSBPM v2.0 III.Introduction of Nara Statistical System IV.Policy Management System V.Statistical Quality Management VI.Future Works

3. Background 1. Needs of Business Process Model 2. Introduction of GSBPM 3. The Role of KSBPM 4. Statistical Environment 5. Usage Cases of KSBPM

4. 1. Needs of Business Process Model Development of standardized statistic management and production system result in needs of statistic business process standardization KSBPM  Based on KSBPM, statistic process is designed  KSBPM processes are mapped to functions of Nara system  Standardization for quality improvement and data sharing Nara System is based on KSBPM 1. 기획 1.1 통계 수요 파악 1.2 통계수요검토 및 구체화 1.3 산출목표 수립 1.4 통계적 개념 정립 1.5 데이터 가용성 검토 1.6 통계생산 계획안 수립 2.1 통계산출물 설계 2.2 통계 항목 설정 2.3 자료 수집 방법 설계 2.4 모집단 및 표본설계 2.5 자료 처리 방법 설계 2.6 통계생산체계 설계 3.1 자료수집 도구 구현 3.2 생산시스템 구성 3.3 업무 절차 설정 3.4 시스템 통합테스트 3.5 생산프로세스 점검 3.6 통계생산체계 확정 4.1 자료수집 대상 선정 4.2 자료 수집 준비 4.3 자료수집 진행 4.4 자료 수집 점검 및 완료 5.1 자료 통합 5.2 분류 및 코딩 5.3 자료검토 및 보완 5.4 결측치 처리 5.5 신규 변수 및 통계 단위 도출 5.6 가중치의 계산 5.7 집계 5.8 자료 처리 완료 6.1 통계산출물 작성 6.2 통계산출물 검증 6.3 상세 분석 및 설명 작성 6.4 정보 공개 범위 설정 6.5 통계산출물 확정 7.1 공표자료 점검 및 적재 7.2 공표 자료 작성 7.3 자료 배포 관리 7.4 자료 배포 촉진 7.5 이용자 지원 관리 8.1 자료보관 규칙 정의 8.2 자료 보관 관리 8.3 통계 및 관련 자료 보존 8.4 통계 및 관련 자료 처분 9.1 평가 계획 수립 9.2 수행 및 보고서 작성 9.3 개선과제 도출, 실행 계획수립 2. 설계 4. 수집 3. 구축 6. 분석 7. 배포 8. 보관 9. 평가 5. 처리 Based on GSBPM, KSBPM is edited for Korea statistical environment Differences in business process in each statistic cases and agencies

5. 2. Introduction of GSBPM 1.1 Determine needs for information 1.2 Consult and confirm needs 1.3Establish output objectives 1.4 Identify concepts 1.5 Check data availability 1.6 Prepare business case 2.1 Design outputs 2.2 Design variable descriptions 2.3 Design data collection methodology 2.4 Design frame and sample methodology 2.5Design statistical processing methodology 2.6 Design production systems and workflow 3.1 Build data collection instrument 3.2 Build or enhance process components 3.3 Configure workflows 3.4Test production system 3.5Test statistical business process 3.6Finalize production system 4.1 Select sample 4.2 Set up collection 4.3 Run collection 4.4 Finalize collection 5.1 Integrate data 5.2 Classify and code 5.3 Review, validate and edit 5.4 Impute 5.5Derive new variables and statistical units 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data files 6.1 Prepare draft outputs 6.2 Validate outputs 6.3 Scrutinize and explain 6.4 Apply disclosure control 6.5 Finalize outputs 7.1 Update output systems 7.2 Produce dissemination products 7.3 Manage release of dissemination products 7.4 Promote dissemination products 7.5 Manage user support 8.1 Define archive rules 8.2 Manage archive repository 8.3 Preserve data and associated metadata 8.4 Dispose of data and associated metadata 9.1 Gather evaluation inputs 9.2Conduct evaluation 9.3 Agree action plan Quality Management / Meta Data Management 1. Specify Needs 2 Design 3 Build 4 Collect 5 Process 6 Analyze 7 Disseminate 8 Archive 9 Evaluate  9 Mega phases and 47 sub- processes

6. 3. The Role of KSBPM KSBPM guides to high-quality, low-cost, high-efficiency statistic production system by standardizing and automating process Standardization  Provide guide-line of business process and quality check for each statistic produce agencies  Encourage re-usage of data and statistic production  Enhance the international status of Statistics Korea by following International standard Automation  Shorten the period of statistic production and improve work efficiency  Save expense by preventing development of duplicated system  Promote co-operation by automating data links among statistic produce agencies Standardized Process-Driven Automation High-quality Statistic Low-cost Production High-efficiency Production Expectation WHY KSBPM?

7. 4. Statistical Environment(1) Features of Korean Statistical System Centralized Centralized producing agency eg) Canada, Germany, Sweden, Australia, Netherlands Decentralized Each government Agencies produce their own statistics eg) USA, Korea, Japan, UK, France Inefficiency of Decentralized Statistical System The absence of system for statistical development and management for whole country Less investment on social-well fare and regional statistics while most investment is on economic statistics

8. 4. Statistical Environment(2) Disadvantage of Decentralized Statistical System Ambiguity on information searching site Time consuming process for searching information Difficulty in data comparison due to non-standardization Budget wasting due to non-integrated system development Decentralized Statistical Information

9. 5. Usage Cases of KSBPM KSBPM helps understanding of systemic statistic production KSBPM is base of automatic statistic production and reference of data and quality management Help understanding the systemic production of statistics  Easy adoption to model users  Improvement of process can be derived by comparing business process and high-quality statistics  Helps the communication between statistic providers and statistic communities Base of statistic production automation  Provide systemic analysis process (i.e.Nara System) in automation of statistic production through IT technology (for Data collection, process, analysis) Reference of data and metadata standardization  Reference for the management of metadata in decentralized statistic production system Usage of KSBPM

10. Development of KSBPM v2.0 1. Trends for International Standard 2. Implications for developing KSBPM v2.0 3. Steps Taken for Development of KSBPM v2.0 4. Changes of Processes for KSBPM v2.0 5. Establishment of KSBPM v2.0

11. 1. Trends for International Standard In order to build KSBPM v2.0, international standard GSBPM for analysis, information model GSIM, and data exchange standard SDMX and DDI are selected GSIM (Information Concept) Methods (Statistical How To) Conceptual Used for realization Practical Standard Concept of Analysis Object ※ Source : United Nations Economic and Social Council (2011). Strategic vision of the High- level group for strategic developments in business architecture in statistics. 1 2 3 Generic Statistical Busines Process Model (GSBPM) Generic Statistical Information Model (GSIM) MACRO/ MICRO Data Exchange (SDMX, DDI) Technology (Production How To) GSBPM (Business Concept) Common Generic Industrial Statistics

12. 2. Implications for developing KSBPM v2.0 KSBPM v2.0 Concept Enhance general reference model Rename standard terms DDI SDMX  Role of generic reference model in producing official statistics should be strengthened.  As a generic model, standard names for common use by organization both in- and outside Statistics Korea should be used.  GSIM v1.0 (currently under development for release in 2013) should be reflected in KSBPM v2.0.  Life cycle of statistical data can be referenced using just GSBPM, and therefore does not require direct changes to KSBPM v2.0.  As SDMX is data and meta data transmission regulation, it does not require any changes to KSBPM v2.0. Implications for developing KSBPM v2.0 based on assessment of current status GSBPM GSIM Add quality assessment process Analyze Trends in International Standards Examine Current State of Nara Statistical System KSBPM v1.0 Guidelines of Official Statistics  Functions for generic model and processes should be redefined and renamed.  Duplicate processes (i.e. budget appropriation, determining survey coverage) should be integrated  Standard names for common use by organization both in- and outside Statistics Korea should be defined.  Inclusion of statistical quality assessment should be considered.

13. 3. Steps Taken for Development of KSBPM v2.0

14. 4. Changes of Processes for KSBPM v2.0 1. Plan 1.1 Determine statistical demand 1.2 Verify & Specify statistical demand 1.3 Establish output objectives 1.4 Identify statistical concepts 1.5 Check data availability 1.6 Make production plan 2.1 Design output 2.2 Design variables 2.3 Design collection methodology 2.4 Design universe & sample 2.5 Design processing methodology 2.6 Design production system 3.1 Build collection instrument 3.2 Build production system 3.3 Configure workflows 3.4 Test production system 3.5 Test business process 3.6 Finalize production system 4.1 Select sample 4.2 Prepare collection 4.3 Run collection 4.4 Finalize collection 5.1 Integrate data 5.2 Classify & code 5.3 Review, validate & edit 5.4 Impute 5.5 Derive new variables & statistical units 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data processing 6.1 Prepare draft outputs 6.2 Validate outputs 6.3 Scrutinize & explain 6.4 Apply disclosure control 6.5 Finalize outputs 7.1 Prepare dissemination data 7.2Produce disseminate products 7.3 Manage release of dissemination products 7.4 Promote dissemination Products 7.5 Manage user support 8.1 Define archive rules 8.2 Manage archive repository 8.3 Preserve data & associated metadata 8.4 Dispose of data & associated metadata 9.1 Make evaluation plan 9.2 Conduct evaluation & produce reports 9.3 Derive improvement plans & make action plan 2. Design4. Collect3. Build6. Analyze 7. Disseminate 8. Archive9. Evaluate5. Process Processes revised from KSBPM v1.0 9 mega processes renamed and 21 sub-processes revised

15. 5. Establishment of KSBPM v2.0 1. Plan 1.1 Determine statistical demand 1.2 Verify & Specify statistical demand 1.3 Establish output objectives 1.4 Identify statistical concepts 1.5 Check data availability 1.6 Make production plan 2.1 Design output 2.2 Design variables 2.3 Design collection methodology 2.4 Design universe & sample 2.5 Design processing methodology 2.6 Design production system 3.1 Build collection instrument 3.2 Build production system 3.3 Configure workflows 3.4 Test production system 3.5 Test business process 3.6 Finalize production system 4.1 Select sample 4.2 Prepare collection 4.3 Run collection 4.4 Finalize collection 5.1 Integrate data 5.2 Classify & code 5.3 Review, validate & edit 5.4 Impute 5.5 Derive new variables & statistical units 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data processing 6.1 Prepare draft outputs 6.2 Validate outputs 6.3 Scrutinize & explain 6.4 Apply disclosure control 6.5 Finalize outputs 7.1 Prepare dissemination data 7.2Produce disseminate products 7.3 Manage release of dissemination products 7.4 Promote dissemination Products 7.5 Manage user support 8.1 Define archive rules 8.2 Manage archive repository 8.3 Preserve data & associated metadata 8.4 Dispose of data & associated metadata 9.1 Make evaluation plan 9.2 Conduct evaluation & produce reports 9.3 Derive improvement plans & make action plan 2. Design4. Collect3. Build6. Analyze 7. Disseminate 8. Archive 9. Evaluate 5. Process ※ KSBPM : 9 phases and 47 processes

16. Introduction of Nara System 1. Development of GSIS 2. Configuration of Nara Statistical System 3. Sub-system’s Outline

17. 1. Development of GSIS Integrating and streamlining statistical policy, production, and metadata mgmt. systems Common use system based on standardized statistical business process ※ Application of Global Standard (GSBPM) Interface with existing systems(KOSIS, MDSS, etc) Policy makers KOSIS MDSS Macrodata Microdata Standard Prcs. Metadata Service Policy Production Data Mgmt. Common use System Agencies Int’l Org. Research People

18. 2. Configuration of Nara Statistical System

19. 3. Sub-system’s Outline Standard Production System supporting comprehensive business processes based on KSBPM Share and reuse of variables, questions, surveys, tables and editing rules based on statistical metadata Approval, Evaluation, Quality Management of Statistics Share of information among related works Provides framework for the share and reuse of statistics Unification of metadata of existing information systems Single Sign On for policy management, statistical production, and metadata management of the statistical agencies Policy Management Statistical Production Metadata Management Web-Portal

20. Policy Management System 1. Configuration of Statistical Policy Management System(1) 2. Configuration of Statistical Policy Management System(2)

21. 1. Configuration of Stat. Policy Mgmt. System Statistical Policy Management System Statistical Policy Management Evidence based policy making system Long/Medium term development plan Management of national statistical system Agency selection Approval on the official statistics (production, modification, cancelation, etc) Evaluation Coordination Regular inspection Support for self-inspection Quality Mgmt. KOSTAT Intranet system Statistical Production system Quality Mgmt. officer Policy Mgmt. officer

22. 2. Configuration of Stat. Policy Mgmt. System Statistical Production System PlanDesign Collect Enter & Process Data AnalyzeDisseminateFollow-up Plan Report Request for approval Quality Assessment Quality Assessment Quality Assessment Quality Assessment Quality Assessment Request for change Quality Assessment Quality Assessment Official Statistics Developments Overall demand Select target Explain and check tasks Statistical Demand Statistical demand Demand survey Check implementation Evaluation Pre-evaluation Evaluation management Pilot evaluation Actual evaluation Policy Support Service System-wide search Search on approved statistics Statistical history management Statistical development status Chief Statistics Officer status Relevant agencies status Quality Management Regular Assessment Regular quality assessment Areas for improvement based on regular assessment Table of regular assessment results Statistical Approval Agency designation Revoke agency designation Designation of statistics Revoke designation of statistics Approve compilation (consultation) Approve modification (consultation) Approve suspension (consultation) Revocation of approval Approve non-release Statistical results Consultation on dissemination after non-release Self Assessment Self-administered quality assessment Table of self assessment results Ad-hoc assessment Ad-hoc quality assessment Register laws Infra management Register policies Register statistical indicators Statistics producing agencies status Approve statistics status Subject evaluation Subject area evaluation Regional statistical demand Regional statistical demand survey Check implementation Statistical Policy System

23. Stat. Quality Management 1. Introduction of Quality Assessment 2. Procedure of Regular Quality Assessment 3. Procedure of Regular Quality Assessment 4. Structure of Self Assessment Procedure 5. Procedure of Self-administered assessment

24. 1. Introduction of Quality Assessment 기능 – Fitness for use Multi-dimensional concept  Accuracy, Coherence, Compatibility, Timeliness, Accessibility, Relevance Definition of Quality Regular Quality Assessment Non-Regular Quality Assessment Self Quality Assessment Kinds of Quality Assessment

25. 2. Procedure of Regular Quality Assessment 1 1. Basis/ Environment 2. Users’ satisfaction & needs 3. Process- review 4. Accuracy in data collection 5. Data Service Put together Identify problems Draw assignments for quality improvement Feed assignments back to statistical agencies Implementation Statistics Agencies 5 sector assessment

26. 3. Procedure of Regular Quality Assessment Screen for regular assessment functions (pop-up window) Table of quality management infrastructure Quality evaluation report for individual statistical procedure Error check table for dissemination data Reference materials List of statistics for regular assessment Select statistics Select function Information on statistics for regular assessment Information on organization and user List of regular assessment functions Portal Quality- Policy Basic information Information on human resources Information on physical resources Interviews on views on statistical management Quality-Policy Information on user Response information Supporting materials Information on researchers Quality-Policy Information on dissemination data Information on responses for check table Information on researchers Quality-Policy Reference materials Information on statistics for regular assessment Quality- Policy Information on Quality Evaluation Team Quality-Policy

27. 4. Structure of Self Assessment Procedure 1 Conduct ing assess- ment Printing the assess- ment sheet Verificat ion of derived assignm -ent Determi nation of assignm -ents Impleme ntation of past assignm -ents Self assess- ment report Approval

28. 5. Procedure of Self-administered assessment Upload Evaluation Report List of Statistics for Self-Assessment Select Statistics Information on organization Information on user Portal Response information in evaluation reports Q&A in evaluation reports Reviews on evaluation reports Policy-Quality Information on statistics for self-assessment Information statistics under responsibility Policy-Quality Information on prior evaluation reports Policy-Quality Submit for Review Review & Approval Screen for Chief Statistics Officer (Pop-up Window) Final approval by Chief Statistics Officer Policy-Quality

29. Future Works

30. VI. Future Works  Reinforcing Quality Assessment Function Improvement of step by step Quality Assessment in the Production System  Strengthening Linkage with other Systems for Export GSIM based Integrated Meta System, transition to SDMX integration module,  Making Continuous Efforts to go with International Standard Trends including GSIM

31. Kobong Park Deputy Director Informatics Planning Division Tel : +82.42.481.2351 Tel : +82.42.481.2351 Fax : +82.42.481.2474 Fax : +82.42.481.2474 E-mail : kbpark@korea.kr E-mail : kbpark@korea.kr Thank you for watching