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GSIS based on KSBPM

Learn about the development of KSBPM for efficient statistical management, addressing system challenges and boosting statistical quality and dissemination planning.

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GSIS based on KSBPM

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  1. 2011 METIS Meeting 5 ~ 8 Oct 2011, Geneva GSIS based on KSBPM GSIS : Generic Statistical Information System

  2. Contents Contents Overview Ⅰ Establishment of KSBPM Ⅱ Ⅲ System Development Ⅳ Plans

  3. Overview Ⅰ • Current Status • Problems 2

  4. 1 Current status Production of National Statistics (As of April 1st, 2011)

  5. 1 Current status Statistical Personnel ＊Statistical personnel refer to officials whose statistical work occupies more than 50 percent of the their responsibilities. (Source: Statistical Workforce and Budget Survey 2010) Statistical personnel recorded 4,530 persons in 2010, which rose by 115 persons from 2008. Out of them, enumerators occupied 56.7 percent.

  6. 1 Current status Information Systems The majority of statistical agencies produce statistics through outsourcing due to the absences of the statistical production and management system.

  7. 2 Problems Problems Planning Survey Design Quality Control Data Collection Prep. Meta Data Mgmt Archive Data Collection Release Data Processing Central A Local A Other A 7.0 Analysis

  8. Establishment of the KSBPM Ⅱ • Backgrounds • Derivation of production process pool • Establishment of the KSBPM • Major characteristics of the KSBPM 7

  9. 1 Background Internal and external conditions Necessary to establish governance over national statistics Necessary to standardize the production and dissemination processes of national statistics Necessary to establish the standardized production and management processes of national statistics Poor statistical quality caused by lack of statistical production systems Poor infrastructure for production and management of national statistics due to non-standardized processes A waste of resources due to individual production and management of statistics • Social and economic loss owing to the production of inaccurate statistics • Public confusion due to similar or redundant statistics • More demand for the systemization of statistical production and dissemination • Necessary to establish the efficient management system of national statistics under the decentralized statistical system • Necessary to switch post quality management into ‘pre- and post-management’ • Necessary to standardize different production processes of individual surveys • Necessary to integrate and share statistical information that is managed by each statistical agency

  10. 2 Derivation of a process pool (1/3) Analyze the statistical production processes of model candidates

  11. 2 Derivation of a process pool (2/3) Reorganize the KSBPM after analyzing, linking and supplementing model candidates KOSTAT business manuals + survey results Survey guidelines Quality management handbook GSBPM Final draft Survey planning 1. Survey planning 1. Planning 1. Specify needs Plan & specify needs 2. Design 2. Questionnaire design 2. Design 2. Design 2. Design 3. Preparation for data collection 3. Sample design & management 3. Collection 3. Build 3. Build 4. Collection 4. Collection 4. Input and processing 4. Collect 4. Collect 5. Processing 5. Processing 5. Analysis and quality evaluation 5. Process 5. Process 6. Analysis 6. Imputation and analysis 6. Documentation and dissemination 6. Analyze 6. Analyze 7. Dissemination 7. Dissemination 7. Follow-up 7. Disseminate 7. Disseminate 8. Archiving 8. Archive 8. Archive 9. Evaluation 9. Evaluate 9. Evaluate

  12. 2 Derivation of a process pool (3/3) Phases and sub-processes of the KSBPM 1. Plan & specify needs 2. Design 3. Build 4. Collect 5. Process 6. Analyze 7. Disseminate 8. Archive 9. Evaluate 1.1 Specify Needs 2.1 Design outputs 3.1 Build/ supplement data collection tools 4.1 Select a sample 5.1 Integrate data 6.1 Prepare output draft 7.1 Load/ check tabulation data 8.1 Define archiving rules 9.1 Decide a checklist 1.2 Consult & Review needs 2.2 Design variables descriptions 3.2 Configure system functions 4.2 Prepare for collection 8.2 Archive 6.2 Validate outputs 7.2 Produce dissemination data 5.2 Classify & code 9.2 Evaluate 1.3 Establish Statistical concepts 2.3 Design a frame 5.3 Validate & supplement 9.3 Derive challenges and make action plans 4.3 Collect data 8.3 Archive associated data 6.3 Scrutinize & explain 7.3 Disseminate Configure workflow 2.4 Design collection methodology 4.4 Finalize collection 8.4 Dispose of associated data 6.4 Apply disclosure control 7.4 Promote dissemination 1.4 Establish Output objectives 5.4 Impute 3.3 Check/ supplement the system 6.5 Finalize outputs 7.5 Support users 1.5 Draw up budget 2.5 Design a sample methodology 5.5 Derive new variables & statistical units 3.4 Test the system Chech data availability 5.6 Calculate weights 2.6 Design Processing methodology 3.5 Finalize the production system Removed sub-process from GSBPM 1.6 Make production plan 2.7 Design workflow 5.7 Tabulate Added sub-process from GSBPM 5.8 Finalize data files

  13. 3 Establishment of the KSBPM 3 2 1 4 Policy management Statistical coordination Quality management Statistics-based policy management Population Information support Quality support by production phase Quality check by phase Production status management Sample design support Statistical information sharing Production support Statistical business knowledge sharing ED and map support Planning Collection Dissemination Metadata use & reference Design Processing Archiving Help desk Implementation Analysis Evaluation Derivation of the KSBPM Governance Composition of the KSBPM Governance Production management Production support Statistical metadata Production process pool Improvement • Specify the definitions and roles of business processes by phase • Metadata use and reference for the entire statistical business • Quality management at all times

  14. 3 Establishment of the KSBPM G2.3 Designate statistics G2.5 Cancel the designation of designated statistics G2.7 Approve the change in the production of statistics G2.9 Cancel the approval of production G2.11 Prevent the redundancy and repetition G2.1 Designate agencies G2.2 Cancel designated agencies G2.8 Approval the stop of statistical production협의) G2.6 Approve the production of statistics G2.4 Change designated statistics G2.12 Coordinate survey items G2.10 Demand the improvement of statistical work K1.1 Query & use knowledge K1.2 Register, modify & delete knowledge K1.3 Investigate the registration, modification and deletion of knowledge K1.4 Manage knowledge maps KSBPM Framework [G] Statistical Policy Management [G1] Statistical Demand Management [G2] Statistical Coordination [G3] Statistical Quality Control [G4] Policy Support by Statistics G3.1 Regular quality evaluation G3.2 Self quality evaluation G4.1 Preliminary evaluation G4.2 Practical evaluation G1.1 Demand Management G1.2 Development and improvement of national statistics G3.4 Quality management consulting G4.3 Tabulation of evaluation results and Reporting G3.3 Occasional quality evaluation G1.3 Human resources management [G5] Statistical Records Management [G6] Statistical Production ProcessMonitoring G5.1 Receive records that should be managed G5.2 Classify records that should be managed G5.3 Share records information G6.1 Monitoring and policy-related consulting G6.2 Notify and check results [S] Statistical Production Data Support [Q] Statistical Production Quality Assessment Support [K] Shared Info. Service [Q1] Self Assessment by Statistical Production Process Q1.1 Refer to production guideline Q1.2 Refer to the quality requirements Q1.3 Check the quality components step by step Q1.4 Check the quality after the completion of production [K1] Statistical Knowledge Mgn’t [S1] Population Data Supply S1.3 Support population information S1.1 Ask for population information [P] Statistical Production Process Pool [P1] Plan & Specify Needs [P4] Collect [P7] Disseminate S1.2 Investigate the support of information S1.4 Manage user feedback P4.3 Run collection P4.1 Select sample P7.1 Update output system P7.3 Manage release of dissemination products P1.1 Specify needs P1.3 Establish statistical concepts P1.5 Draw up budget P4.4 Finalize collection P4.2 Set up collection P7.4 Promote dissemination products P1.2 Consult & confirm needs P1.4 Establish output objectives P1.6 Make production plan [S2] Sampling Data Supply P7.2 Produce dissemination products P7.5 Manage user support [P5] Process S2.1 Ask for sample design support S2.4 Provide design and sampling [K2] Metadata Reference P5.5 Derive new variables & statistical units P5.1 Integrate data [P2] Design [P8] Archive S2.2 Ask for sampling support P2.1 Design outputs P2.3 Design frame P2.5 Design sample methodology K2.1 Statistical metadata reference P5.6 Calculate weights P5.2 Classify & code S2.5 Manage user feedback P8.1 Define archive rules P8.3 Preserve data and associated metadata P2.4 Design data collection methodology P2.2 Design variable descriptions P2.6 Design statistical processing methodology [K3] Help desk S2.3 Investigate the support P5.7 Calculate aggregates P5.3 Validate & supplement P8.2 Manage archive repository P8.4 Dispose of data & associated metadata K3.1 Query & use existing information P2.7 Design workflow P5.8 Finalized data files P5.4 Impute K3.2 Receive new entries [S3] Enumeration Districts Data Supply [P9] Evaluate [P6] Analyze [P3] Build K3.3 Investigate reception details P6.1 Prepare draft output P6.3 Scrutinize & explain P9.1 Decide checklist P9.3 Derive challenges and make action plan P3.1 Build data collection instrument P3.3 Test production system P3.4 Test statistical business process S3.1 Ask for support S3.3 Provide information K3.4 Deal with requests P6.4 Apply disclosure control K3.5 Ask for additional handling P6.2 Validate outputs P9.2 Conduct evaluation P3.2 Configure workflows P3.5 Finalize production system S3.2 Investigate the support S3.4 Manage user feedback P6.5 Finalize outputs K3.6 Feedback

  15. 4 Characteristics of the KSBPM Major characteristics of the KSBPM Expectation effects of the KSBPM Derivation of quality support process to secure statistical quality Organic linkage between policy and production • Statistical quality is monitored during all the production processes. And these monitoring results will strengthen the quality of national statistics and governance functions. • Add a process to check statistical quality during all the processes and to manage essential components of each process • Internalize the quality management process in the statistical production process • Manage statistics efficiently and improve statistical quality • Help officials concerned to understand statistical quality Change into quality management at all times • Upgrade the quality of official statistics by changing into quality management during all the production processes • In the case of survey statistics, 98 out of 208 items (47%) can be checked through the GSIS Derivation of data sharing process to share statistical knowledge Strengthen production support process • Improve business efficiency of statistical agencies and data accuracy by activating the systematic support process such as population management and sample management • Enhance business efficiency through the sharing of knowledge and information • Minimize trial and trial when producing statistics • Secure business continuity despite frequent changes in officials concerned • Minimize the burden of new staff members Strengthen the sharing of associated knowledge and information Derivation of statistical production support process • Strengthen the sharing of associated knowledge and information to positively reflect opinions of statistical users • Activate the current production support process • Support efficient statistical production by deriving a support process needed for field survey management

  16. GSIS Ⅲ • Purpose • System Architecture 15

  17. 1 Purpose of GSIS Standard process-based Production with low cost and high efficiency (Quality) A single window of Statistical business (Collaboration) Reasonable statistical administration (Governance) Improving the reliability of national statistics using metadata (Trust) Objective • Collaboration among producers, and customized services • Communication and knowledge transfer between the KOSTAT and production agencies • Consolidated account for different type of users • Link for the efficiency of approval management • System-based quality management • Integrated history management to reduce workload of production agencies • Standardization of processes • Integrated system for the maximization of business efficiency • Automatic business from questionnaire design to data transfer • Standardization of terms and processes • Manage statistical outputs step by step • Provision of statistical production standards by using metadata Direction Generic Statistical Production Integrated MetadataManagement Collaboration Portal Governance System

  18. 2 System Architecture KPI management Integrated information link system Support for common services Backup Security History management Generic Statistical Information System Architecture Users Generic Statistical Information System KOSTAT systems Statistical collaboration Governance system Linking system Generic Statistical Production System KOSIS Statisticians Survey design Data collection Integrated login Demand management e-National Indicators Web services Coordination management MDSS Contract-based production agencies Knowledge management DB linkage Quality management Data processing Data dissemination and management Integrated Administrative Data Management System RMI Communication Enumerators/ Survey managers Inspection management Population System (establishments/enterprises) Statistical Metadata System Support for production Policy consulting Support for statistical quality Evaluation Microdata archive Academia/ Research institutes Statistical DWSystem Linkwith the classification system of national statistics Help desk Outside systems Integrated Metadata Management System The general public Statistical metadata Business reference metadata Standardization metadata Production agencies Mobile channels Common service-based system Survey system (CAPI, CATI, ICR) PDA UMPC International organizations Mobile application

  19. Plans Ⅳ • Plans by Year • Expectation Effects 18

  20. 1 Plans by Year Action Plans by Year 2011 2012 2013 Phase 1 Phase 2 Phase 3 Establish the infrastructure for the generic statistical information system Expand the generic statistical information system Strengthen the generic statistical information system • Integrate statistical policies • (Demand, approval and quality) • Build the model statistical system • (30 agencies) • Statistics Korea (1), Ministry of Public Administration and Security (1) • Ministry of Culture, Sports and Tourism (3) • Gyeongnam and basic local governments (12) • Jeonbuk and basic local governments (9) • Social surveys(Jeonbuk, Jeonju, Gunsan, Gyeongnam,4) • Build the integrated metadata system • Build the edit, tabulation and analysis system • Expand the statistical system(120 agencies) • Develop the generic sampling system • Establish a support system for non-designated statistics • Improve the functions in the system • Expand the statistical system(Other statistical agencies) • Expand the functions of quality management • Build a system for data sharing and linkage among agencies • Support a specialized function of respective agencies ※ Information Strategy Planning (ISP) (2010)

  21. 2 Expectation Effects Qualitative effect • Efficient statistical activities via the standardized processes • (Survey planning, dissemination and data management) • Budget reduction and common use of the statistical production system Quantitative effect • Economic benefit of 24.4 billion KRW per year via the standardized statistical production system • (Reduction of time spent on the production of administrative statistics, KOSIS data input and self-evaluation) • Budget reduction of 73.4 billion KRW per year by saving the costs of the development and maintenance of the statistical production system (*According to the 2010 Statistical Manpower and Budget Survey)

  22. Chanil Seo Director Informatics Planning Division Phone: 82.42.481.2377 Fax : 82.42.481.2474 E-mail: charlie88@korea.kr

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