Transcript GSIS based on KSBPM GSIS : Generic Statistical Information System Ⅰ Overview Ⅱ Establishment of KSBPM Ⅲ System Development Ⅳ Plans.

GSIS based on KSBPM
GSIS : Generic Statistical Information System
Ⅰ
Overview
Ⅱ
Establishment of KSBPM
Ⅲ
System Development
Ⅳ
Plans
Ⅰ Overview
1. Current Status
2. Problems
2
1
Current status
Production of National Statistics
Number of
agencies
Number of
statistics
Total
375
Government
Classification
By kind
By compiling method
Designated
statistics
General
statistics
Survey
statistics
Administrative Analytics
statistics
statistics
832
90
742
331
443
58
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)
1
Current status
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
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
Planning
Survey
Design
Quality
Control
Data
Collection
Prep.
Meta Data
Mgmt
Archive
Data
Collection
Release
Central A
Local A
Other A
Data
Processing
Analysis
7.0
Ⅱ Establishment of the KSBPM
1. Backgrounds
2. Derivation of production process pool
3. Establishment of the KSBPM
4. Major characteristics of the KSBPM
7
1
Background
Necessary to establish the standardized production and
management processes of national statistics
Internal and external conditions
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
governance over national
statistics
Necessary to standardize the
production and dissemination
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
Necessary to establish the
efficient management system
of national statistics under the
decentralized statistical system
Necessary to switch post
quality management into ‘preand post-management’
8
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
• 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
Statistics Act
• The only detailed description of statistical
Production
• The handbook doesn’t cover the entire production
processes by phase in relation to quality
processes in a
processes. In particular, processes after Phase
management
‘documentation and dissemination’ are focused on
quality management • Consider characteristics of survey statistics as well
quality management
handbook
as administrative and analytic statistics
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
9
• The GSBPM needs to be customized to Korean
Circumstances. It’s necessary to redefine the
business model
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
1. Survey planning
1. Survey planning
1. Planning
1. Specify needs
1. Plan & specify needs
2. Design
2. Questionnaire design
2. Design
2. Design
2. Design
3. Preparation for data
3. Sample design &
3. Collection
3. Build
3. Build
4. Collect
4. Collect
collection
management
4. Input and
4. Collection
4. Collection
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
processing
10
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
5.2
Classify & code
6.2
Validate outputs
7.2 Produce
dissemination
data
8.2
Archive
9.2
Evaluate
1.3
Establish
Statistical
concepts
2.3
Design a frame
Configure
workflow
4.3
Collect data
5.3
Validate &
supplement
6.3
Scrutinize &
explain
7.3
Disseminate
8.3
Archive
associated data
9.3 Derive
challenges and
make action
plans
1.4
Establish
Output
objectives
2.4 Design
collection
methodology
3.3 Check/
supplement the
system
4.4
Finalize
collection
5.4
Impute
6.4
Apply disclosure
control
7.4
Promote
dissemination
8.4
Dispose of
associated data
1.5
Draw up budget
2.5 Design a
sample
methodology
3.4
Test the system
5.5 Derive new
variables &
statistical units
6.5
Finalize outputs
7.5
Support users
Chech data
availability
2.6 Design
Processing
methodology
3.5 Finalize the
production
system
5.6
Calculate
weights
1.6
Make production
plan
2.7
Design workflow
5.7
Tabulate
5.8
Finalize data
files
11
Removed sub-process
from GSBPM
Added sub-process
from GSBPM
3
Establishment of the KSBPM
Derivation of the KSBPM
Composition of
the KSBPM
1
Governance
2
Production management
3
Production support
4
Statistical metadata
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
Governance
Policy
management
Statistical
coordination
Quality
management
Statistics-based
policy
management
Quality support by production phase
Quality check by
phase
Production status
management
Statistical
information
sharing
Production process pool
Production
support
Population
Information
support
Planning
Collection
Dissemination
Sample
design
support
Design
Processing
Archiving
ED and
map
support
Implementation
Analysis
Evaluation
12
Statistical
business
knowledge
sharing
Metadata
use &
reference
Help desk
3
Establishment of the KSBPM
KSBPM Framework
[G] Statistical Policy Management
[G1] Statistical Demand
Management
G1.1
Demand
Management
[G2] Statistical Coordination
G1.2
Development and
improvement of
national statistics
G1.3
Human resources management
G2.1
Designate agencies
G2.3
Designate statistics
G2.5
G2.7
G2.9
Cancel the designationApprove
of
the change in Cancel the approval
designated statistics the production of statistics
of production
G2.2
Cancel designated
agencies
G2.4
Change designated
statistics
G2.6
G2.8
G2.10
G2.12
Approve the productionApproval the stop of Demand the improvement
Coordinate
of statistics
statistical production협의)
of statistical work
survey items
[G5] Statistical Records Management
G5.1
Receive records that should
be managed
[S] Statistical Production
Data Support
[S1] Population Data Supply
S1.1
Ask for population
information
S1.3
Support population
information
S1.2
S1.4
Investigate the support Manage
of information
user feedback
[S2] Sampling Data Supply
S2.1
Ask for sample design S2.4
support
Provide design and
sampling
S2.2
Ask for sampling
support
S2.5
S2.3
Manage
Investigate
user feedback
the support
[S3] Enumeration Districts
Data Supply
G2.11
Prevent the redundancy
and repetition
[G3] Statistical Quality
Control
[G4] Policy Support by
Statistics
G3.1
Regular quality
evaluation
G3.2
Self quality
evaluation
G3.3
Occasional quality
evaluation
G3.4
Quality management
consulting
G4.2
G4.1
Practical
Preliminary
evaluation
evaluation
G4.3
Tabulation of evaluation results and
Reporting
[G6] Statistical Production Process Monitoring
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
[Q] Statistical Production Quality Assessment Support
[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
[P] Statistical Production Process Pool
[P1] Plan & Specify Needs
[P4] Collect
P1.1
Specify needs
P1.3
Establish
statistical concepts
P1.5
Draw up budget
P1.2
Consult & confirm
needs
P1.4
Establish output
objectives
P1.6
Make production
plan
[P2] Design
P2.1
Design outputs
P2.2
Design variable
descriptions
P2.5
P2.3
Design sample
Design frame
methodology
P2.4
P2.6
Design data collection Design statistical
methodology
processing methodology
P2.7
Design workflow
S3.1
Ask for support
S3.3
Provide information
S3.2
Investigate
the support
S3.4
Manage
user feedback
P3.2
Configure workflows
P4.3
Run collection
P4.2
Set up collection
P4.4
Finalize collection
[P5] Process
P5.1
Integrate data
P5.5
Derive new variables
& statistical units
P5.2
Classify & code
P5.6
Calculate weights
P5.7
P5.3
Calculate
Validate & supplement aggregates
P5.8
P5.4
Finalized data files
Impute
P3.3
Test production
system
P3.4
Test statistical
business process
P6.1
Prepare draft output
P3.5
Finalize production
system
P6.2
Validate outputs
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P7.3
Manage release of
dissemination products
P7.4
Promote
dissemination products
P7.2
P7.5
Produce
dissemination products Manage user support
P7.1
Update output
system
[P8] Archive
P8.1
Define archive rules
P8.3
Preserve data and
associated metadata
P8.2
Manage archive
repository
P8.4
Dispose of data &
associated metadata
[P9] Evaluate
[P6] Analyze
[P3] Build
P3.1
Build data collection
instrument
[P7] Disseminate
P4.1
Select sample
P6.3
Scrutinize & explain
P6.4
Apply disclosure
control
P6.5
Finalize outputs
P9.1
Decide checklist
P9.2
Conduct evaluation
[K] Shared Info.
Service
[K1] Statistical
Knowledge Mgn’t
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
[K2] Metadata
Reference
K2.1
Statistical metadata reference
[K3] Help desk
K3.1
Query & use existing information
K3.2
Receive new entries
K3.3
Investigate reception details
P9.3
Derive challenges
and make action
plan
K3.4
Deal with requests
K3.5
Ask for additional handling
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
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
Change into quality management at all times
• Manage statistics efficiently and improve statistical quality
• Upgrade the quality of official statistics by changing into
quality management during all the production processes
• Help officials concerned to understand statistical quality
• 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
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
• 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
14
Ⅲ GSIS
1. Purpose
2. System Architecture
15
1
Objective
Direction
Purpose of GSIS
A single window of
Statistical business
Reasonable statistical
administration
(Collaboration)
(Governance)
Collaboration among
producers, and
customized services
Link for the efficiency
of approval
management
Communication and
knowledge transfer
between the KOSTAT
and production
agencies
System-based quality
management
Integrated system for
the maximization of
business efficiency
Integrated history
management to reduce
workload of production
agencies
Automatic business
from questionnaire
design to data transfer
Consolidated account
for different type of
users
System
Collaboration Portal
Governance
Standard process-based
Production with low
cost and high efficiency
Improving the reliability
of national statistics
using metadata
(Quality)
(Trust)
Standardization of
processes
Generic
Statistical Production
Standardization of
terms and processes
Manage statistical
outputs step by step
Provision of statistical
production standards
by using metadata
Integrated Metadata
Management
2
System Architecture
Generic Statistical Information System Architecture
Generic Statistical Information System
Users
Statisticians
Contract-based
production
agencies
Enumerators/
Survey
managers
Academia/
Research
institutes
The general
public
Statistical
collaboration
Governance
system
Integrated login
Demand
management
UMPC
Mobile
application
Web services
Data
processing
Data
dissemination and
management
RMI
e-National
Indicators
Integrated Administrative
Data Management System
Population System
(establishments/enterprises)
Statistical Metadata
System
Policy consulting
Link with the classification system
of national statistics
Support for
statistical quality
Evaluation
Microdata
archive
Business reference metadata
Statistical DW
System
Outside
systems
Integrated Metadata Management System
Statistical metadata
KOSIS
MDSS
Inspection
management
Mobile
channels
PDA
Data
collection
Linking
system
DB linkage
Quality
management
Communication
Help desk
Survey
design
Coordination
management
Knowledge
management
Support
for production
Generic Statistical Production System
KOSTAT
systems
Standardization metadata
Common service-based system
KPI management
Integrated information link system
Support for common services
Backup
Security
History management
Production
agencies
Survey system
(CAPI, CATI, ICR)
International
organizations
Ⅳ Plans
1. Plans by Year
2. Expectation Effects
18
1
Plans by Year
Action Plans by Year
2011
Phase 1
Establish the infrastructure
for 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
2012
2013
Phase 2
Expand the generic statistical
information system
Build the edit, tabulation and analysis
system
Phase 3
Strengthen the generic statistical
information system
Improve the functions in the system
Expand the statistical system
(Other statistical agencies)
Expand the statistical system
(120 agencies)
Develop the generic sampling system
Establish a support system for nondesignated statistics
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: [email protected]