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GSIS based on KSBPM
GSIS : Generic Statistical Information System
Establishment of KSBPMEstablishment of KSBPM
System DevelopmentSystem Development
PlansPlans
ⅡⅡ
ⅢⅢ
ⅣⅣ
OverviewOverviewⅠⅠ
3
ⅠⅠ OverviewOverview
1. Current Status
2. Problems
Production of National StatisticsProduction 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 58 262 157 142 21
- Local agencies 260 366 16 350 82 260 24
Designated agencies 77 146 16 130 92 41 13
(As of April 1st, 2011)
Current statusCurrent status11
Statistical Personnel Statistical Personnel
Year 2004 2006 2008 2010
Officials (person) 4,135 4,507 4,415 4,530
Percent change (%) 9.0 9.0 -2.0 2.6
(Source: Statistical Workforce and Budget Survey 2010 )
＊ Statistical personnel refer to officials whose statistical work occupies more than 50 percent of the their responsibilities.
Statistical personnel recorded 4,530 persons in 2010,which rose by 115 persons from 2008. Out of them,enumerators occupied 56.7 percent.
Current statusCurrent status11
Information SystemsInformation Systems
The majority of statistical agencies produce statistics through outsourcing due to the absences of the statistical production and management system.
ClassificationCentral
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
Current statusCurrent status11
ProblemsProblems
ProblemsProblems22
Central ALocal AOther A
PlanningSurvey Design
DataCollection Prep.
DataCollection
DataProcessing
Analysis
Release
Archive
Meta DataMgmt
Quality Control
7.0
8
ⅡⅡ Establishment of the KSBPMEstablishment of the KSBPM
1. Backgrounds
2. Derivation of production process pool
3. Establishment of the KSBPM
4. Major characteristics of the KSBPM
9
Internal and external conditions
Necessary to standardize the production and dissemination processes of national statistics
Necessary to establish governance over national
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
A waste of resources due to individual production and management of statistics
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’
Poor statistical quality caused by lack of statistical production systems
Necessary to standardize different production processes of individual surveys
Necessary to integrate and share statistical information that is managed by each statistical agency
Poor infrastructure for production and management of national statistics due to non-standardized processes
BackgroundBackground11
Necessary to establish the standardized produc-tion and management processes of national sta-
tistics
10
Derivation of a process pool (1/3)Derivation of a process pool (1/3)22
Analyze the statistical production processes of model candidatesAnalyze 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
11
Derivation of a process pool (2/3)Derivation of a process pool (2/3)22
Survey guidelines
7. Dissemination
6. Imputation and analysis
5. Processing
4. Collection
3. Sample design &
management
2. Questionnaire
design
1. Survey planning
Quality manage-ment
handbook
7. Follow-up
6. Documentation and dissemination
5. Analysis and quality evaluation
4. Input and
processing
3. Collection
2. Design
1. Planning
GSBPM
8. Archive
7. Disseminate
9. Evaluate
6. Analyze
5. Process
4. Collect
3. Build
2. Design
1. Specify needs
Final draft
8. Archive
7. Disseminate
9. Evaluate
6. Analyze
5. Process
4. Collect
3. Build
2. Design
1. Plan & specify
needs
KOSTAT business manuals
+ survey results
7. Dissemination
6. Analysis
5. Processing
4. Collection
3. Preparation for
data collection
2. Design
1. Survey planning
8. Archiving
9. Evaluation
Reorganize the KSBPM after analyzing, linking and supplementing model candidates
Reorganize the KSBPM after analyzing, linking and supplementing model candidates
12
Derivation of a process pool (3/3)Derivation of a process pool (3/3)22
Phases and sub-processes of the KSBPMPhases and sub-processes of the KSBPM1. Plan & specify needs
2. De-sign
3. Build4. Col-lect
5. Process6. Ana-lyze
7. Dissem-inate
8. Archive 9. Evaluate
1.1 Specify Needs1.1 Specify Needs
1.2 Consult & Review needs
1.2 Consult & Review needs
1.3 EstablishStatistical con-cepts
1.3 EstablishStatistical con-cepts
1.4 EstablishOutput objec-tives
1.4 EstablishOutput objec-tives
1.5 Draw up bud-get
1.5 Draw up bud-get
1.6 Make produc-tion plan
1.6 Make produc-tion plan
2.1 Designoutputs
2.1 Designoutputs
2.2 Design variables de-scriptions
2.2 Design variables de-scriptions
2.3 Design a frame2.3 Design a frame
2.4 Design collection methodology
2.4 Design collection methodology
2.5 Design a sample methodology
2.5 Design a sample methodology
2.6 Design Processing methodology
2.6 Design Processing methodology
2.7 Design work-flow
2.7 Design work-flow
3.1 Build/ supplement data collection tools
3.1 Build/ supplement data collection tools
3.2 Config-ure system functions
3.2 Config-ure system functions
3.3 Check/supplement the system
3.3 Check/supplement the system
3.4 Test the sys-tem
3.4 Test the sys-tem
3.5 Finalize the production system
3.5 Finalize the production system
4.1Select a sam-ple
4.1Select a sam-ple
4.2Prepare for col-lection
4.2Prepare for col-lection
4.3Collect data4.3Collect data
4.4Finalize collec-tion
4.4Finalize collec-tion
5.1Integrate data5.1Integrate data
5.2Classify & code5.2Classify & code
5.3Validate & supplement
5.3Validate & supplement
5.4Impute5.4Impute
5.5 Derive new variables & statistical units
5.5 Derive new variables & statistical units
5.6Calculate weights
5.6Calculate weights
5.7 Tabulate5.7 Tabulate
5.8 Finalize data files
5.8 Finalize data files
6.1 Prepare output draft
6.1 Prepare output draft
6.2Validate out-puts
6.2Validate out-puts
6.3Scrutinize & explain
6.3Scrutinize & explain
6.4Apply disclo-sure control
6.4Apply disclo-sure control
6.5Finalize out-puts
6.5Finalize out-puts
7.1 Load/ check tabulation data
7.1 Load/ check tabulation data
7.2 Produce dissemination data
7.2 Produce dissemination data
7.3Disseminate7.3Disseminate
7.4Promote dis-semination
7.4Promote dis-semination
7.5Support users7.5Support users
8.1 Define archiv-ing rules
8.1 Define archiv-ing rules
8.2Archive8.2Archive
8.3 Archive asso-ciated data
8.3 Archive asso-ciated data
8.4 Dispose of as-sociated data
8.4 Dispose of as-sociated data
9.1Decide a checklist
9.1Decide a checklist
9.2Evaluate9.2Evaluate
9.3 Derive challenges and make action plans
9.3 Derive challenges and make action plans
Chech data availabilityChech data availability
Configure workflowConfigure workflow
Removed sub-process from GSBPMRemoved sub-process from GSBPM
Added sub-process from GSBPMAdded sub-process from GSBPM
13
Establishment of the KSBPMEstablishment of the KSBPM33Derivation of the KSBPMDerivation of the KSBPM
Governance
Statistics-based policy man-
agement
Policy management
Quality management
Statistical coordination
Planning Collection Dissemination
Design Processing Archiving
Implementation Analysis Evaluation
Quality support by production phase
Quality check by phase
Production status management
Popula-tion
Informa-tion
support
Sample design support
ED and map
support
Production support
Production process pool
Statistical business
knowledge sharing
Meta-data use & refer-
ence
Help desk
Statistical in-formation sharing
Specify the definitions and roles of business processes by phase
Metadata use and reference for the entire statistical business
Quality management at all times
Improvement
Composition of the KSBPM
Composition of the KSBPM
Governance11
Production management22
Production support33
Statistical metadata44
14
Establishment of the KSBPMEstablishment of the KSBPM33
[G] Statistical Policy Management [G] Statistical Policy Management [G4] Policy Support by Statistics [G4] Policy Support by Statistics
G4.1Preliminary evaluation
G4.2Practical evaluation
G4.3Tabulation of evaluation results and Reporting
[G1] Statistical Demand Management
[G1] Statistical Demand Management
G1.1Demand Management
G1.2Development and improvement of national statistics
G1.3Human resources management
[G3] Statistical Quality Control [G3] Statistical Quality Control
G3.1Regular quality evaluation
G3.2Self quality evaluation
G3.3Occasional quality evaluation
G3.4Quality managementconsulting
[G2] Statistical Coordination [G2] Statistical Coordination
G2.1Designate agencies
G2.2Cancel designated agencies
G2.3Designate statistics
G2.4Change designatedstatistics
G2.5Cancel the designation of designated statistics
G2.6Approve the production of statistics
G2.7Approve the change in the production of statistics
G2.8Approval the stop of statistical production 협의 )
G2.9Cancel the approval of production
G2.10Demand the improvementof statistical work
G2.11Prevent the redundancyand repetition
G2.12Coordinate survey items
[G5] Statistical Records Management[G5] Statistical Records ManagementG5.2Classify records that should be managed
G5.3Share records information
[G6] Statistical Production Process Monitoring[G6] Statistical Production Process MonitoringG6.1Monitoring and policy-related consulting
G6.2Notify and check results
G5.1Receive records that should be managed
[Q] Statistical Production Quality Assessment Support[Q] Statistical Production Quality Assessment Support[Q1] Self Assessment by Statistical Production Process[Q1] Self Assessment by Statistical Production ProcessQ1.1Refer to production guideline
Q1.2Refer to the quality requirements
Q1.3Check the quality components step by step
Q1.4Check the quality after the completion of production
[S] Statistical Production Data Support
[S] Statistical Production Data Support
[S2] Sampling Data Supply[S2] Sampling Data SupplyS2.1Ask for sample design support
S2.2Ask for sampling support
S2.3Investigate the support
S2.4Provide design andsampling
[K] Shared Info. Service[K] Shared Info. Service
[P] Statistical Production Process Pool[P] Statistical Production Process Pool
S2.5Manage user feedback
[P1] Plan & Specify Needs[P1] Plan & Specify NeedsP1.1Specify needs
P1.3Establish statistical concepts
P1.5Draw up budget
P1.2Consult & confirm needs
P1.4Establish output objectives
P1.6Make production plan
[P2] Design[P2] DesignP2.1Design outputs
P2.2Design variable descriptions
P2.3Design frame
P2.4Design data collectionmethodology
P2.5Design sample methodologyP2.6Design statistical processing methodology
P2.7Design workflow
[P3] Build[P3] BuildP3.1Build data collec-tion instrument
P3.2Configure work-flows
P3.3Test production system
P3.4Test statistical business process
P3.5Finalize production system
[P4] Collect[P4] CollectP4.1Select sample
P4.2Set up collection
P4.3Run collection
P4.4Finalize collection
[P5] Process[P5] ProcessP5.1Integrate data
P5.2Classify & code
P5.3Validate & supplement
P5.4Impute
P5.5Derive new vari-ables & statistical unitsP5.6Calculate weights
P5.7Calculate aggregatesP5.8Finalized data files
[P6] Analyze[P6] AnalyzeP6.1Prepare draft out-put
P6.2Validate outputs
P6.4Apply disclosure control
P6.5Finalize outputs
P6.3Scrutinize & explain
[P7] Disseminate[P7] DisseminateP7.1Update output system
P7.2Produce dissemination products
P7.3Manage release of dissemination productsP7.4Promote dissemination productsP7.5Manage user support
[P8] Archive[P8] ArchiveP8.1Define archive rules
P8.2Manage archive repository
P8.3Preserve data and associated meta-dataP8.4Dispose of data & associated meta-data
[P9] Evaluate[P9] Evaluate
P9.1Decide checklist
P9.2Conduct evaluation
P9.3Derive challenges and make action plan
[K1] Statistical Knowledge Mgn’t
[K1] Statistical Knowledge Mgn’tK1.1Query & use knowledge K1.2Register, modify & delete knowledgeK1.3Investigate the registration, modificationand deletion of knowledge
K1.4Manage knowledge maps
[K2] Metadata Reference[K2] Metadata ReferenceK2.1Statistical metadata reference
[K3] Help desk[K3] Help desk
K3.4Deal with requests
K3.5Ask for additional handling
K3.6Feedback
K3.1Query & use existing information
K3.2Receive new entries
K3.3Investigate reception details
[S1] Population Data Supply[S1] Population Data SupplyS1.1Ask for populationinformation
S1.2Investigate the supportof information
S1.3Support population information
S1.4Manage user feedback
[S3] Enumeration Districts Data Supply
[S3] Enumeration Districts Data SupplyS3.1Ask for support
S3.2Investigate the support
S3.3Provide information
S3.4Manageuser feedback
KSBPM Framework KSBPM Framework
15
Characteristics of the KSBPMCharacteristics of the KSBPM44
Major characteristics 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 qualityDerivation 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
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
Expectation effects of the KSBPM
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
Strengthen the sharing of associated knowledge and information
• Strengthen the sharing of associated knowledge and information to positively reflect opinions of statistical users
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
16
ⅢⅢ GSIS GSIS
1. Purpose
2. System Architecture
Purpose of GSISPurpose of GSIS11
Direction
Objective
System
A single window of Statistical business(Collaboration)
Reasonable statisticaladministration
(Governance)
Improving the reliability of national
statistics using metadata (Trust)
Standard process-based
Production with lowcost and high
efficiency (Quality)
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 terms and processes
Manage statistical outputs step by step
Provision of statistical production standards by using metadata
Standardization of processes
Integrated system for the maximization of business efficiency
Automatic business from questionnaire design to data transfer
Collaboration Portal GovernanceIntegrated Metadata
ManagementGeneric
Statistical Production
Generic Statistical Information System ArchitectureGeneric Statistical Information System Architecture
Users Generic Statistical Information System
Governance system
Common service-based system
Statistical collaboration
KOSTAT systems
Survey system(CAPI, CATI, ICR)
International organizations
Production agencies
Outside systems
Integrated login
Knowledge management
Communication
Support for production
Help desk
DB linkage
RMI
Integrated Metadata Management System
Link with the classification system of national statistics
Statisticians
Contract-based production agencies
Enumerators/
Survey managers
Academia/Research institutes
The general public
Mobile applicatio
n
Demand management
Coordination management
Quality management
Inspection management
Policy consulting
Statistical metadataBusiness reference
metadataStandardization metadata
Mobile channels
Microdata archive
PDA UMPC
Survey design
Data collection
Data processing
Data dissemination
and management
Support for statistical
quality Evaluation
Integrated information link system
SecurityBackup
KPI management
History management
Support for common services
Generic Statistical Production SystemKOSIS
MDSS
Integrated Administrative Data Management System
Population System(establishments/
enterprises)
e-National Indicators
Statistical Metadata System
Statistical DW System
Linking system
Web services
System ArchitectureSystem Architecture22
19
ⅣⅣ PlansPlans
1. Plans by Year
2. Expectation Effects
2011 2012 2013
Integrate statistical policies (Demand, approval and quality)
Build the model statistical sys-tem
(30 agencies)- Statistics Korea (1), Ministry of Public Ad-
ministration 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 sys-tem
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)
Phase 1 Phase 2 Phase 3
Expand the generic statistical information system
Establish the infrastructure for the generic statistical information system
Strengthen the generic statis-tical information system
Action Plans by Year Action Plans by Year
Plans by YearPlans by Year11
Qualitative effect
Quantitative effect
Efficient statistical activities via the standardized processes
(Survey planning, dissemination and data management)
Budget reduction and common use of the statistical production system
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)
Expectation EffectsExpectation Effects22
Chanil Seo Director Informatics Planning Division
Phone: 82.42.481.2377
Fax : 82.42.481.2474
E-mail: [email protected]
