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Implementation of KSBPM in KOSTAT

Implementation of KSBPM in KOSTAT. April 2013. Ki -bong Park. Contents. Background Development of KSBPM v2.0 Introduction of Nara Statistical System Policy Management System Statistical Quality Management Future Works. Ⅰ. Background. 1. Needs of Business Process Model.

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Implementation of KSBPM in KOSTAT

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

  2. Contents • Background • Development of KSBPM v2.0 • Introduction of Nara Statistical System • Policy Management System • Statistical Quality Management • Future Works

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

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

  6. 3. The Role of KSBPM • KSBPM guides to high-quality, low-cost, high-efficiency statistic production system by standardizing and automating process Standardized Process-Driven Automation Expectation Standardization Automation High-quality Statistic WHY KSBPM? • 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 • 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 Low-cost Production High-efficiency Production

  7. 4. Statistical Environment(1) Features of Korean Statistical System Centralized Decentralized Centralized producing agency eg) Canada, Germany, Sweden, Australia, Netherlands Each government Agencies produce their own statistics eg) USA, Korea, Japan, UK, France Korean Statistical System is decentralized system which is partly centralized 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 Decentralized Statistical Information • Ambiguity on information searching site • Time consuming process for searching information • Budget wasting due to non-integrated system development • Difficulty in data comparison due to non-standardization

  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 • 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 Help understanding the systemic production of statistics Usage of KSBPM Base of statistic production automation • Provide systemic analysis process (i.e.NaraSystem) 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 productionsystem

  10. Ⅱ 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 DDIare selected Standard Concept of Analysis Object • GSBPM(Business Concept) GSIM(Information Concept) Generic Statistical Busines Process Model (GSBPM) 1 Conceptual Common Generic Industrial Statistics Generic Statistical Information Model (GSIM) 2 Technology(Production How To) Methods(Statistical How To) MACRO/ MICRO Data Exchange (SDMX, DDI) 3 Practical Used for realization ※ Source : United Nations Economic and Social Council (2011). Strategic vision of the High- level group for strategic developments in business architecture in statistics.

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

  13. 3. Steps Taken for Development of KSBPM v2.0 KSBPM v1.0 GSBPM v4.0 Task Force Team Meetings Government Manual for Statistics Guidelines of Official Statistics Statistical Quality Assessment Handbook KSBPM v2.0 1. Plan 1. Specify Needs 1. Plan 1. Plan 1. Plan 1. Plan 1. Plan 2. Design 2. Design 2. Design 2. Design 2. Design 2. Design 2. Design 3. Design & Manage Sample 3. Collect 3. Build 3. Build 3. Build 3. Prepare Collection 3. Build 4. Enter & Process Data 4. Collect 4. Collect 4. Collect 4. Collect 4. Collect 4. Collect 5. Analyze Data and Evaluate Quality 5. Process 5. Process 5. Process 5. Process 5. Process 5. Process 6. Process Non-Responses and Analyze Data 6. Analyze 6. Analyze 6. Analyze 6. Analyze 6. Analyze 6. Document & Disseminate 7. Disseminate 7. Disseminate 7. Disseminate 7. Disseminate 7. Disseminate 7. Disseminate 7. Follow-up 8. Archive 8. Archive 8. Archive 8. Archive 8. Archive 9. Evaluate 9. Evaluate 9. Evaluate 9. Evaluate 9.Evaluae

  14. 4. Changes of Processes for KSBPM v2.0 9 mega processes renamed and 21 sub-processes revised 1. Plan 2. Design 3. Build 4. Collect 5. Process 6. Analyze 7. Disseminate 8. Archive 9. Evaluate 1.1 Determine statistical demand 2.1 Design output 3.1 Build collection instrument 4.1 Select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Prepare dissemination data 8.1 Define archive rules 9.1 Make evaluation plan 1.2 Verify & Specify statistical demand 2.2 Design variables 3.2 Build production system 4.2 Prepare collection 5.2Classify & code 6.2Validate outputs 7.2Produce disseminate products 8.2Manage archive repository 9.2Conduct evaluation & produce reports 1.3 Establish output objectives 2.3 Design collection methodology 3.3Configure workflows 4.3Run collection 5.3Review, validate & edit 6.3 Scrutinize & explain 7.3 Manage release of dissemination products 8.3Preserve data & associated metadata 9.3 Derive improvement plans & make action plan 1.4 Identify statistical concepts 2.4 Design universe & sample 3.4 Test production system 4.4 Finalize collection 5.4 Impute 6.4 Apply disclosure control 7.4Promote dissemination Products 8.4Dispose of data & associated metadata 1.5 Check data availability 2.5 Design processing methodology 3.5 Test business process 5.5 Derive new variables & statistical units 6.5 Finalize outputs 7.5 Manage user support 1.6 Make production plan 2.6 Design production system 3.6Finalize production system 5.6Calculate weights Processes revised from KSBPM v1.0 5.7Calculate aggregates 5.8Finalize data processing

  15. 5. Establishment of KSBPM v2.0 1. Plan 2. Design 3. Build 4. Collect 5. Process 6. Analyze 7. Disseminate 8. Archive 9. Evaluate 1.1 Determine statistical demand 2.1 Design output 3.1 Build collection instrument 4.1 Select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Prepare dissemination data 8.1 Define archive rules 9.1 Make evaluation plan 1.2 Verify & Specify statistical demand 2.2 Design variables 3.2 Build production system 4.2 Prepare collection 5.2Classify & code 6.2Validate outputs 7.2Produce disseminate products 8.2Manage archive repository 9.2Conduct evaluation & produce reports 1.3 Establish output objectives 2.3 Design collection methodology 3.3Configure workflows 4.3Run collection 5.3Review, validate & edit 6.3 Scrutinize & explain 7.3 Manage release of dissemination products 8.3Preserve data & associated metadata 9.3 Derive improvement plans & make action plan 1.4 Identify statistical concepts 2.4 Design universe & sample 3.4 Test production system 4.4 Finalize collection 5.4 Impute 6.4 Apply disclosure control 7.4Promote dissemination Products 8.4Dispose of data & associated metadata 1.5 Check data availability 2.5 Design processing methodology 3.5 Test business process 5.5 Derive new variables & statistical units 6.5 Finalize outputs 7.5 Manage user support 1.6 Make production plan 2.6 Design production system 3.6Finalize production system 5.6Calculate weights ※ KSBPM : 9 phases and 47 processes 5.7Calculate aggregates 5.8Finalize data processing

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

  17. 1. Development of GSIS Standard Prcs. • 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) Service Research People Policy Policy makers Common use System Int’l Org. MDSS Data Mgmt. KOSIS Production Metadata Macrodata Agencies Microdata

  18. Statistical demand DB Standard DB Review DB Quality check DB Raw data Microdata Establishment Administrative data DB Storage DB Manage dissemination data Prepare dissemination data 2. Configuration of Nara Statistical System User groups Statistical production agencies Statistical policy User information DB ApprovalDB Integration Integration Integration National statistics portal Statistical standards Production agencies KOSTAT Statistical policy Statistical approval Quality management Statistical review Data storage Self & regular check Demand information MDSS DW DB Transfer/ storage Request for approval Central government (36 agencies) Approval Statistical production agencies Statistical Production system Object system DB Statistical design Data collection Data manage- mentsystem Data processing & analysis system Local governments (260 agencies) Population management Registration of surveys Integrated national statistics DB (KOSIS) Macrodata Transfer Register management Questionnaire Design General users Assignment of enumerator business Edit design Dissemination data Private designated agencies (77 agencies) Treatment of missing values Batch process editing Population/ Establishment Data collection management Summary table design Tabulation and analysis edit Weighting Input edit Survey methodology Policy makers GIS DB Tabulation Ending of data processing Ending of data collection System architecture management Policy makers Statistical metadata management system Metadata on statistics Metadata on statistical production Statistical terms metadata Research institutes Research institutes

  19. 3. Sub-system’s Outline • Approval, Evaluation, Quality Management of Statistics • Share of information among related works Policy Management • 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 Statistical Production Metadata Management • Provides framework for the share and reuse of statistics • Unification of metadata of existing information systems Web-Portal • Single Sign On for policy management, statistical production, and metadata management of the statistical agencies

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

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

  23. V 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 Definition of Quality • 기능 • Fitness for use • Multi-dimensional concept • Accuracy, Coherence, Compatibility, Timeliness, Accessibility, Relevance Kinds of Quality Assessment • Regular Quality Assessment • Non-Regular Quality Assessment • Self Quality Assessment

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

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

  27. 4. Structure of Self Assessment Procedure 1

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

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

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