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

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

Ⅰ Overview Current Status Problems 2

1 Current status Production of National Statistics Classification Number of agencies Number of statistics By kind By compiling method Designated statistics General statistics Survey statistics Administrative statistics Analytics statistics Total 375 832 90 742 331 443 58 Government 298 686 74 612 239 402 45 - Central agencies 38 320 262 157 142 21 - Local agencies 260 366 16 350 82 24 Designated agencies 77 146 130 92 41 13 (As of April 1st, 2011)

Statistical Personnel Current status 1 Statistical Personnel ＊Statistical personnel refer to officials whose statistical work occupies more than 50 percent of the their responsibilities. Year 2004 2006 2008 2010 Officials (person) 4,135 4,507 4,415 4,530 Percent change (%) 9.0 -2.0 2.6 (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.

1 Current status Information Systems Classification Central government agencies Local government agencies Designated agencies Total Statistical agencies 38 260 78 376 Agencies with their own information systems 6 1 20 27 Percentage (%) 15 0.3 25 7.0 The majority of statistical agencies produce statistics through outsourcing due to the absences of the statistical production and management system.

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

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

Background 1 Necessary to establish the standardized production and management processes of national statistics Internal and external conditions Necessary to standardize the production and dissemination processes of national statistics Necessary to establish governance over national statistics A waste of resources due to individual production and management of 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 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

2 Derivation of a process pool (1/3) Analyze the statistical production processes of model candidates Classification Characteristics Considerations Statistics Act The Statistics Act presents the definitions and requirements of production processes of national statistics The Statistics Act doesn’t present production processes by phase and their sub-processes specifically Business manuals The KOSTAT, a central statistical agency of Korea, has business manuals for the production of 52 kinds of official statistics Business manuals don’t describe official production processes Manuals can be used when verifying applicability and usability of the standard production processes Guidelines of national statistics Guidelines describe the official production model for survey statistics Guidelines are focused on data input and processing Guidelines don’t present sub-processes that should be implemented The KOSTAT don’t have guidelines on administrative and analytic statistics Production processes in a quality management handbook The only detailed description of statistical processes by phase in relation to quality management Consider characteristics of survey statistics as well as administrative and analytic statistics The handbook doesn’t cover the entire production processes. In particular, processes after Phase ‘documentation and dissemination’ are focused on quality management GSBPM Generic Statistical Business Process Model v 4.0 The GSBPM covers the business processes for survey statistics as well as administrative and analytic statistics The GSBPM needs to be customized to Korean Circumstances. It’s necessary to redefine the business model

KOSTAT business manuals Derivation of a process pool (2/3) 2 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

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 4.2 Prepare for collection 4.3 Collect data 4.4 Finalize collection 5.1 Integrate data 5.2 Classify & code 5.3 Validate & supplement 5.4 Impute 5.5 Derive new variables & statistical units 5.6 Calculate weights 5.7 Tabulate 5.8 Finalize data files 6.1 Prepare output draft 6.2 Validate outputs 6.3 Scrutinize & explain 6.4 Apply disclosure control 6.5 Finalize outputs 7.1 Load/ check tabulation data 7.2 Produce dissemination data 7.3 Disseminate 7.4 Promote dissemination 7.5 Support users 8.1 Define archiving rules 8.2 Archive 8.3 Archive associated data 8.4 Dispose of associated data 9.1 Decide a checklist 9.2 Evaluate 9.3 Derive challenges and make action plans 1.2 Consult & Review needs 2.2 Design variables descriptions 3.2 Configure system functions 1.3 Establish Statistical concepts 2.3 Design a frame Configure workflow 1.4 Establish Output objectives 2.4 Design collection methodology 3.3 Check/ supplement the system 1.5 Draw up budget 2.5 Design a sample methodology 3.4 Test the system Chech data availability 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 Added sub-process from GSBPM

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

3 Establishment of the KSBPM KSBPM Framework [G] Statistical Policy Management [G1] Statistical Demand Management [G2] Statistical Coordination [G3] Statistical Quality Control [G4] Policy Support by Statistics G2.1 Designate agencies G2.2 Cancel designated agencies G2.3 Designate statistics G2.4 Change designated statistics G2.5 Cancel the designation of designated statistics G2.6 Approve the production of statistics G2.7 Approve the change in the production of statistics G2.8 Approval the stop of statistical production협의) G2.9 Cancel the approval of production G2.10 Demand the improvement of statistical work G2.11 Prevent the redundancy and repetition G2.12 Coordinate survey items G1.1 Demand Management G1.2 Development and improvement of national statistics G3.1 Regular quality evaluation G3.2 Self quality evaluation G4.1 Preliminary evaluation G4.2 Practical evaluation G1.3 Human resources management G3.3 Occasional quality evaluation G3.4 Quality management consulting G4.3 Tabulation of evaluation results and Reporting [G5] Statistical Records Management [G6] Statistical Production Process Monitoring 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 S1.1 Ask for population information S1.3 Support population information [P] Statistical Production Process Pool 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 [P1] Plan & Specify Needs [P4] Collect [P7] Disseminate S1.2 Investigate the support of information S1.4 Manage user feedback P1.1 Specify needs P1.3 Establish statistical concepts P1.5 Draw up budget P4.1 Select sample P4.3 Run collection P7.1 Update output system P7.3 Manage release of dissemination products [S2] Sampling Data Supply P1.2 Consult & confirm needs P1.4 Establish output objectives P1.6 Make production plan P4.2 Set up collection P4.4 Finalize collection P7.4 Promote dissemination products P7.2 Produce dissemination products S2.1 Ask for sample design support S2.4 Provide design and sampling [P5] Process P7.5 Manage user support [P2] Design P5.1 Integrate data P5.5 Derive new variables & statistical units [K2] Metadata Reference S2.2 Ask for sampling support P2.1 Design outputs P2.3 Design frame P2.5 Design sample methodology [P8] Archive K2.1 Statistical metadata reference S2.5 Manage user feedback P5.2 Classify & code P5.6 Calculate weights P8.1 Define archive rules P8.3 Preserve data and associated metadata S2.3 Investigate the support P2.2 Design variable descriptions P2.4 Design data collection methodology P2.6 Design statistical processing methodology [K3] Help desk P5.3 Validate & supplement P5.7 Calculate aggregates P2.7 Design workflow P8.2 Manage archive repository P8.4 Dispose of data & associated metadata K3.1 Query & use existing information P5.4 Impute P5.8 Finalized data files [S3] Enumeration Districts Data Supply K3.2 Receive new entries [P3] Build [P6] Analyze [P9] Evaluate K3.3 Investigate reception details S3.1 Ask for support S3.3 Provide information P3.1 Build data collection instrument P3.3 Test production system P3.4 Test statistical business process P6.1 Prepare draft output P6.3 Scrutinize & explain P9.1 Decide checklist P9.3 Derive challenges and make action plan K3.4 Deal with requests P6.4 Apply disclosure control K3.5 Ask for additional handling S3.2 Investigate the support S3.4 Manage user feedback P3.2 Configure workflows P3.5 Finalize production system P6.2 Validate outputs P9.2 Conduct evaluation P6.5 Finalize outputs K3.6 Feedback

4 Characteristics of the KSBPM Major characteristics of the KSBPM Expectation effects of the KSBPM Derivation of quality support process to secure statistical quality 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 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. 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 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 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 Strengthen the sharing of associated knowledge and information Strengthen the sharing of associated knowledge and information to positively reflect opinions of statistical users Derivation of statistical production support process Activate the current production support process Support efficient statistical production by deriving a support process needed for field survey management

Ⅲ GSIS Purpose System Architecture 15

1 Purpose of GSIS (Quality) (Collaboration) (Governance) (Trust) 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 Metadata Management System Collaboration Portal Governance

System Architecture 2 Generic Statistical Information System Architecture Users Generic Statistical Information System KOSTAT systems Statistical collaboration Governance system Generic Statistical Production System Linking system KOSIS Statisticians Integrated login Demand management Survey design Data collection Web services e-National Indicators Coordination management Contract-based production agencies Knowledge management MDSS 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) Support for production Policy consulting Statistical Metadata System Support for statistical quality Evaluation Microdata archive Academia/ Research institutes Statistical DW System Link with 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 Backup KPI management Integrated information link system Security History management Support for common services Survey system (CAPI, CATI, ICR) PDA UMPC Mobile application International organizations 17

Ⅳ Plans Plans by Year Expectation Effects I'll talk about the plans. 18

1 Plans by Year Action Plans by Year Phase 1 Phase 2 Phase 3 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)

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)

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