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

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

2. Ⅰ
Overview
Current Status
Problems 2

3. 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)

4. 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 Out of them,
enumerators occupied 56.7 percent.

5. 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.

6. 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

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

8. 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

9. 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

10. 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

11. 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
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
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
Derive challenges and make action plans
1.2
Consult &
Review needs
Design variables descriptions
Configure system functions
1.3
Establish
Statistical concepts
2.3
Design a frame
Configure workflow
1.4
Establish
Output objectives
Design
collection
methodology
Check/
supplement the system
1.5
Draw up budget
Design a sample methodology
3.4
Test the system
Chech data availability
Design Processing
methodology
Finalize the production system
Removed sub-process from GSBPM
1.6
Make production plan
2.7
Design workflow
Added sub-process from GSBPM

12. 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

13. 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

14. 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

15. Ⅲ
GSIS
Purpose
System Architecture 15

16. 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

17. 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

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

19. 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)

20. 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)

21. Chanil Seo Director Informatics Planning Division
Phone:
Fax :