Implementation of KSBPM in KOSTAT Contents

Implementation of KSBPM in KOSTAT
April 2013
Ki-bong Park
Contents
I. Background
II. Development of KSBPM v2.0
III. Introduction of Nara Statistical System
IV. Policy Management System
V. Statistical Quality Management
VI. Future Works
1
Background
1. Needs of Business Process Model
2. Introduction of GSBPM
3. The Role of KSBPM
4. Statistical Environment
5. Usage Cases of KSBPM
1. Needs of Business Process Model
Development of standardized statistic management and production system
result in needs of statistic business process standardization
경상남도 General Survey–
Survey 현행 업무절차
등록관리
통계표
관리
시스템
관리
수집자료
내검
산출물
작성
자료이관
일정관리
공동서식
관리
매뉴얼
관리
수집
마감관리
상세설명
작성
KOSIS
관리
공통모듈
설계
입력 포털
구현
정보공개
관리
분석
마감관리
Differences in business process in
each statistic cases and agencies
관광사업체 기초통계조사 – 현행 업무절차
기획 설계 구축 수집 처리 분석 배포
등록관리
조사표
설계
시스템
관리
분류 및
코딩
산출물
작성
자료이관
일정관리
표본추출
표본설계
매뉴얼관
리
집계표
설계
입력 포털
구현
명부관리
결측치
처리
상세설명
작성
KOSIS
관리
조사입력
처리결과
내검
정보공개
관리
내검 설계
수집자료
내검
처리
마감관리
분석
마감관리
공통모듈
설계
수집
마감관리
Nara System is based
on KSBPM
KSBPM
기획 설계 구축 수집 처리 분석 배포
4.
수집
5.
처리
6.
분석
1.1
통계 수요
파악
1.
기획
2.1
통계산출물
설계
3.1
자료수집
도구 구현
4.1
자료수집
대상 선정
5.1
자료 통합
6.1
통계산출물
작성
7.1
공표자료
점검 및 적재
1.2
통계수요검토
및 구체화
2.2
통계 항목
설정
3.2
생산시스템
구성
4.2
자료 수집
준비
5.2
분류 및 코딩
6.2
통계산출물
검증
1.3
산출목표
수립
2.3
자료 수집
방법 설계
3.3
업무 절차
설정
4.3
자료수집
진행
5.3
자료검토 및
보완
6.3
상세 분석 및
설명 작성
1.4
통계적 개념
정립
2.4
모집단 및
표본설계
3.4
시스템
통합테스트
4.4
자료 수집
점검 및 완료
5.4
결측치 처리
1.5
데이터
가용성 검토
2.5
자료 처리
방법 설계
3.5
생산프로세스
점검
5.5
신규 변수 및
통계 단위
도출
1.6
통계생산
계획안 수립
2.
설계
3.
구축
2.6
3.6
통계생산체계 통계생산체계
설계
확정
7.
배포
8.
보관
9.
평가
8.1
자료보관
규칙
정의
9.1
평가 계획
수립
7.2
공표 자료
작성
8.2
자료 보관
관리
9.2
수행 및
보고서 작성
7.3
자료 배포
관리
8.3
통계 및 관련
자료 보존
9.3
개선과제
도출, 실행
계획수립
6.4
정보 공개
범위 설정
7.4
자료 배포
촉진
8.4
통계 및 관련
자료 처분
6.5
통계산출물
확정
7.5
이용자
지원 관리
5.6
가중치의
계산
5.7
집계
 Based on KSBPM, statistic
process is designed
 KSBPM processes are
mapped to functions of Nara
system
 Standardization for quality
improvement and data
sharing
5.8
자료 처리
완료
Based on GSBPM, KSBPM is edited for Korea
statistical environment
2
2. Introduction of GSBPM
Quality Management / Meta Data Management
1.
Specify
Needs
2
Design
3
Build
4
Collect
5
Process
6
Analyze
7
Disseminate
8
Archive
9
Evaluate
1.1 Determine
needs for
information
2.1
Design outputs
3.1 Build data
collection
instrument
4.1
Select sample
5.1
Integrate data
6.1
Prepare draft
outputs
7.1
Update output
systems
8.1
Define archive
rules
9.1 Gather
evaluation
inputs
1.2
Consult and
confirm needs
2.2
Design variable
descriptions
3.2 Build or
enhance
process
components
4.2
Set up
collection
5.2
Classify and
code
6.2
Validate outputs
7.2 Produce
dissemination
products
8.2 Manage
archive
repository
9.2Conduct
evaluation
1.3Establish
output
objectives
2.3 Design data
collection
methodology
3.3 Configure
workflows
4.3
Run collection
5.3 Review,
validate and
edit
6.3
Scrutinize and
explain
7.3 Manage
release of
dissemination
products
3.4Test
production
system
4.4
Finalize
collection
5.4
Impute
6.4 Apply
disclosure
control
7.4 Promote
dissemination
products
8.3 Preserve
data and
associated
metadata
8.4 Dispose of
data and
associated
metadata
3.5Test
statistical
business
process
5.5Derive new
variables and
statistical units
6.5
Finalize
outputs
7.5
Manage user
support
3.6Finalize
production
system
5.6
Calculate
weights
1.4
Identify
concepts
1.5
Check data
availability
1.6
Prepare
business case
2.4 Design
frame and
sample
methodology
2.5Design
statistical
processing
methodology
2.6 Design
production
systems and
workflow
5.7 Calculate
aggregates
9.3 Agree
action plan
- 9 Mega phases and 47 subprocesses
5.8
Finalize data
files
3. The Role of KSBPM
• KSBPM guides to high-quality, low-cost, high-efficiency statistic
production system by standardizing and automating process
Standardized Process-Driven Automation
Expectation
WHY
KSBPM?
Standardization
Automation
Provide guide-line of
business process and
quality check for each
statistic produce agencies
Encourage re-usage of
data and statistic
production
Enhance the international
status of Statistics Korea
by following International
standard
Shorten the period of
statistic production and
improve work efficiency
Save expense by
preventing development of
duplicated system
Promote co-operation by
automating data links
among statistic produce
agencies
High-quality
Statistic
Low-cost
Production
Highefficiency
Production
3
4. Statistical Environment(1)
Features of Korean Statistical System
Centralized
Decentralized
C
Centralized
t li d producing
d i
agency
eg) Canada, Germany, Sweden,
Australia, Netherlands
E
Each
h governmentt A
Agencies
i produce
d
their own statistics
eg) USA, Korea, Japan, UK, France
Inefficiency of Decentralized Statistical System
The absence of system for statistical development and
management for whole country
Less investment on social-well fare and regional statistics
while most investment is on economic statistics
4. Statistical Environment(2)
Disadvantage of Decentralized Statistical System
Decentralized Statistical Information
Ambiguity on information searching site
Time consuming process for
searching information
Budget wasting due to non
non-integrated
integrated
system development
Difficulty in data comparison due to
non-standardization
4
5. Usage Cases of KSBPM
•
•
KSBPM helps understanding of systemic statistic production
KSBPM is base of automatic statistic production and reference of data
and quality management
Help understanding
the systemic
production of
statistics
Usage of
KSBPM
Base of statistic
production
automation
Reference of data
and metadata
standardization
Easy adoption to model users
Improvement of process can be derived by comparing
business process and high-quality statistics
Helps the communication between statistic providers and
statistic communities
Provide systemic analysis process (i.e.Nara System) in
automation of statistic production through IT
technology (for Data collection
collection, process
process, analysis)
Reference for the management of metadata in
decentralized statistic production system
Development of KSBPM v2
1. Trends for International Standard
2. Implications for developing KSBPM v2.0
3. Steps Taken for Development of KSBPM v2.0
4. Changes of Processes for KSBPM v2.0 5. Establishment of KSBPM v2.0
f
5
1. Trends for International Standard
In order to build KSBPM v2.0, international standard GSBPM for analysis,
information model GSIM, and data exchange standard SDMX and DDI are
selected
Standard Concept of Analysis Object
Practical
Conceptual
•
GSIM
GSBPM
(Business Concept)
Common
Generic
Industrial
Statistics
(Information
Concept)
Technology
Methods
(Statistical How To)
(Production
How To)
1
Generic Statistical Busines
Process Model (GSBPM)
2
Generic Statistical Information
Model (GSIM)
3
MACRO/ MICRO Data
Exchange (SDMX, DDI)
Used for realization
※ Source : United Nations Economic and Social Council (2011). Strategic vision of the High-
level group for strategic developments in business architecture in statistics.
2. Implications for developing KSBPM v2.0
Implications for developing KSBPM v2.0 based on assessment of current status
 Role of generic reference model in producing official
statistics should be strengthened.
GSBPM
Analyze
Trends in
International
Standards
GSIM
 As a generic model, standard names for common use by
organization both in- and outside Statistics Korea should
be used.
 GSIM v1.0 (currently under development for release in
2013) should be reflected in KSBPM v2.0.
DDI
 Life cycle of statistical data can be referenced using just
GSBPM, and therefore does not require direct changes to
KSBPM v2.0.
SDMX
g
,
 As SDMX is data and meta data transmission regulation,
it does not require any changes to KSBPM v2.0.
 Functions for generic model and processes should be
redefined and renamed.
Examine
Current State
of Nara
Statistical
System
KSBPM v1.0
 Duplicate processes (i.e. budget appropriation,
determining survey coverage) should be integrated
Guidelines of
Official
Statistics
 Standard names for common use by organization both
in- and outside Statistics Korea should be defined.
KSBPM v2.0 Concept
Enhance
general
reference
model
Rename
standard
terms
Add quality
assessment
process
 Inclusion of statistical quality assessment should be
considered.
6
3. Steps Taken for Development of KSBPM v2.0
KSBPM v1.0
GSBPM v4.0
Task Force
Team
Meetings
Government
Manual
for Statistics
Statistical
Quality
Assessment
Handbook
Guidelines
of Official
Statistics
KSBPM v2.0
1. Plan
1. Specify
Needs
1. Plan
1. Plan
1. Plan
1. Plan
1. Plan
2. Design
2. Design
2. Design
2. Design
2. Design
2. Design
2. Design
3. Prepare
Collection
3. Design &
Manage
Sample
3. Collect
4. Collect
4. Collect
3. Build
4. Collect
5. Process
3. Build
3. Build
4. Collect
4. Collect
5. Process
5. Process
5. Process
5. Process
3. Build
4. Enter &
Process Data
4. Collect
5. Analyze Data
and Evaluate
Quality
5. Process
6. Document &
Disseminate
6. Analyze
7. Follow-up
7.
Disseminate
6. Analyze
6. Analyze
6. Analyze
6. Analyze
6. Process NonResponses and
Analyze Data
7.
Disseminate
7.
Disseminate
7.
Disseminate
7.
Disseminate
7. Disseminate
8. Archive
8. Archive
8. Archive
8. Archive
8. Archive
9. Evaluate
9. Evaluate
9. Evaluate
9. Evaluate
9.Evaluae
4. Changes of Processes for KSBPM v2.0 9 mega processes renamed and 21 sub-processes
revised
7.
Disseminate 8. Archive
5.1
Integrate data
6.1
Prepare draft
outputs
7.1 Prepare
dissemination
data
8.1
Define archive
rules
9.1
Make evaluation
plan
4.2
Prepare
collection
5.2
Classify & code
6.2
Validate outputs
7.2Produce
disseminate
products
8.2
Manage archive
repository
9.2
Conduct
evaluation &
produce reports
4.3
Run collection
5.3
Review, validate
& edit
6.3
Scrutinize &
explain
7.3 Manage
release of
dissemination
products
8.3
Preserve data &
associated
metadata
9.3 Derive
improvement
plans & make
action plan
5.4
Impute
6.4
Apply disclosure
control
7.4
Promote
dissemination
Products
8.4
Dispose of data
& associated
metadata
6.5
Finalize outputs
75
7.5
Manage user
support
2. Design
1.1
Determine
statistical
demand
2.1
Design output
3.1
Build collection
instrument
4.1
Select sample
1.2
Verify & Specify
statistical
demand
2.2
Design variables
3.2
Build production
system
3.3
Configure
workflows
1.3
2.3
Establish output Design collection
objectives
methodology
3. Build
6. Analyze
1. Plan
4. Collect
5. Process
1.4
Identify
statistical
concepts
2.4
Design universe
& sample
15
1.5
Check data
availability
2.5
Design
processing
methodology
35
3.5
Test business
process
5.5
Derive new
variables &
statistical units
1.6
Make production
plan
2.6
Design
production
system
3.6
Finalize
production
system
5.6
Calculate
weights
3.4
Test production Finalize4.4
collection
system
5.7
Calculate
aggregates
9.
Evaluate
Processes
revised from
KSBPM v1.0
5.8
Finalize data
processing
7
5. Establishment of KSBPM v2.0
1. Plan
2. Design
1.1
Determine
statistical
demand
2.1
Design output
1.2
Verify & Specify
statistical
demand
2.2
Design variables
1.3
2.3
Establish output Design collection
objectives
methodology
3. Build
4. Collect
5. Process
3.1
Build collection
instrument
4.1
Select sample
5.1
Integrate data
6.1
Prepare draft
outputs
3.2
Build production
system
4.2
Prepare
collection
5.2
Classify & code
3.3
Configure
workflows
4.3
Run collection
9. Evaluate
7.1 Prepare
dissemination
data
8.1
Define archive
rules
9.1
Make evaluation
plan
6.2
Validate outputs
7.2Produce
disseminate
products
8.2
Manage archive
repository
9.2
Conduct
evaluation &
produce reports
5.3
Review, validate
& edit
6.3
Scrutinize &
explain
7.3 Manage
release of
dissemination
products
8.3
Preserve data &
associated
metadata
9.3 Derive
improvement
plans & make
action plan
5.4
Impute
6.4
Apply disclosure
control
7.4
Promote
dissemination
Products
8.4
Dispose of data
& associated
metadata
6.5
p
Finalize outputs
7.5
Manage user
support
2.4
Design universe
& sample
1.5
Check data
availability
2.5
Design
processing
p
g
methodology
3.5
Test business
process
5.5
Derive new
variables &
statistical units
1.6
Make production
plan
2.6
Design
production
system
3.6
Finalize
production
system
5.6
Calculate weights
3.4
Test production Finalize4.4
collection
system
7.
Disseminate
8. Archive
1.4
Identify
statistical
concepts
6. Analyze
※ KSBPM : 9 phases and 47 processes
5.7
Calculate
aggregates
5.8
Finalize data
processing
Introduction of Nara Syste
1. Development of GSIS
2. Configuration of Nara Statistical System
3. Sub‐system’s Outline
8
1. Development of GSIS
• Integrating and streamlining
statistical policy, production, and
metadata mgmt. systems
Research
People
Policy
makers
Int’l
Org.
Service
• Common use system based on
standardized statistical business
process
Policy
※ Application of Global Standard
(GSBPM)
Metadata
Data
Mgmt.
M
t
Common
use
System
Production
Macrodata
Agencies
Standard Prcs.
Microdata
• Interface with existing
systems(KOSIS, MDSS, etc)
2. Configuration of Nara Statistical System
Production
agencies
Central government
(36 agencies)
User groups
User
information
DB
National statistics
portal
Policy makers
Research institutes
Approva
Statistical
Review
Quality
Integration check Integration
l DB
demand Integration
DB
DB
DB
Statistical
Quality
Statistical
Statistical
policy
management
review
approval
Request for approval
Approval
Administrativ
e data DB
Statistical design
Data collection
Registration
of surveys
Population
management
Questionnaire
Design
Register
management
Edit design
g
Assignment of
enumerator business
Summary table
design
Data collection
management
Statis
stical
Produ
uction
sys
stem
Data
storage
Establishment
Demand information
Local governments
(260 agencies)
Pi t
Private
designated
agencies
(77 agencies)
Statistical
olicy
po
Statistical
production agencies
Self & regular check
Transfer
/
storage
Data processing &
analysis system
DW DB
Data management system
Macrodata
Raw data
Microdata Transfer
Treatment of
missing values
Batch process
editing
Weighting
Ending of
data processing
Survey
methodology
Input edit
Tabulation and
analysis edit
System architecture
management
Ending
of data collection
Tabulation
Statistical metadata
management system
Storage
DB
Metadata
on statistics
Disseminatio
Di
i ti
n data
Standard
DB
Statistical
standards
KOSTAT
MDSS
Object system
DB
Integrated
national
statistics
DB
(KOSIS)
Statistical
production
agencies
General users
Population/
Establishment
Manage
dissemination data
GIS DB
Policy makers
Prepare
dissemination data
Metadata on
statistical production
Statistical terms
metadata
Research institutes
9
3. Sub‐system’s Outline
Policy
M
Management
t
• Approval,
• Share
Evaluation, Quality Management of Statistics
of information among related works
• Standard
Statistical
Production
Metadata
Management
Web-Portal
Production System supporting comprehensive business
processes based on KSBPM
• Share
and reuse of variables, questions, surveys, tables and editing
rules based on statistical metadata
• Provides
framework for the share and reuse of statistics
• Unification
of metadata of existing information systems
• Single
Sign On for policy management, statistical production, and
metadata management of the statistical agencies
Policy Management Syste
1. Configuration of Statistical Policy Management System(1)
2. Configuration of Statistical Policy Management System(2)
10
1. Configuration of Stat. Policy Mgmt. System
Statistical Policy Management System
Evaluation
Statistical Policy
y
• Management Evidence
based policy making
system
Policy
Mgmt.
officer
• Long/Medium term
development plan
• Management of national
statistical system
Coordination
Quality Mgmt.
• Agency selection
• Approval on the official
statistics (production,
modification, cancelation,
etc)
St ti ti l
Statistical
Production
system
KOSTAT
Intranet
system
• Regular inspection
• Support for selfinspection
Quality
Mgmt.
officer
Statis
stical Production
System
2. Configuration of Stat. Policy Mgmt. System
Plan
Design
Plan Report
Request for
approval
Collect
Enter & Process
Data
Analyze
Disseminate
Follow-up
Quality
Assessment
Quality
Assessment
Quality
Assessment
Quality
Assessment
Quality
Assessment
Request for
change
Quality
Assessment
Quality
Assessment
Quality Management
Official Statistics
Developments
Statistical Demand
Evaluation
Policy Support
Service
Regular quality assessment
Overall demand
Demand survey
Evaluation management
System-wide search
Statistical Approval
Agency designation
Revoke agency designation
Regular Assessment
Select target
Designation of statistics
Areas for improvement based
on regular assessment
Explain and check tasks
Revoke designation
of statistics
Table of regular
assessment results
Approve compilation
(consultation)
Approve modification
(consultation)
Approve suspension
(consultation)
Self-administered
quality assessment
Self Assessment
Statistical demand
Pre-evaluation
Check implementation
Pilot evaluation
Infra management
Statistical development
status
Register laws
Check implementation
Chief Statistics
Officer status
Register policies
Relevant agencies status
Table of self assessment results
Register statistical
indicators
Revocation of approval
Ad-hoc quality assessment
Approve non-release
Statistical history
management
Regional statistical
demand survey
Regional statistical demand
Search on
approved statistics
Actual evaluation
Statistics producing
agencies status
Approve statistics status
Ad-hoc assessment
Statistical results
Consultation on dissemination
after non-release
Subject area evaluation
Subject evaluation
Statistical Policy System
11
Stat. Quality Management
1. Introduction of Quality Assessment
2. Procedure of Regular Quality Assessment 3. Procedure of Regular Quality Assessment
4. Structure of Self Assessment Procedure
5. Procedure of Self‐administered assessment
1. Introduction of Quality Assessment
Definition of Quality
f
 Fitness for use
 Multi‐dimensional concept
 Accuracy, Coherence, Compatibility, Timeliness, Accessibility, Relevance Kinds of Quality Assessment
• 기능
– Regular Quality Assessment  Non‐Regular Quality Assessment
 Self Quality Assessment
12
2. Procedure of Regular Quality Assessment 5 sector assessment
1
1.
Basis/
Environment
2.
Users’
satisfaction
& needs
4.
Accuracy
in data
collection
3.
Processreview
5.
Data
Service
Put together
• Identify problems
• Draw assignments for quality
improvement
• Feed assignments back to statistical
agencies
Statistics Agencies
Implementation
3. Procedure of Regular Quality Assessment
List of statistics
for regular assessment
Select
statistic
s
List of regular
assessment functions
Select
function
Screen for regular
assessment functions (popup window)
Quality-Policy
Portal
• Information
on
organization
and user
Quality-Policy
• Information on
statistics for regular
assessment
Quality-Policy
• Information on
statistics for
regular
assessment
• Basic information
• Information on
human resources
• Information on
physical resources
• Interviews on
views on statistical
managementt
Quality-Policy
• Information on
Quality
Evaluation Team
Quality-Policy
Table of quality management infrastructure
Quality evaluation report for individual
statistical procedure
Error check table for dissemination data
Reference materials
Quality-Policy
• Information on user
• Response
information
• Supporting materials
• Information on
researchers
Reference
materials
• Information on
dissemination data
• Information on
responses for check table
• Information on
researchers
13
4. Structure of Self Assessment Procedure
Conduct
ing
g
assessment
Printing
the
assessment
sheet
Verificat
ion of
derived
assignm
-ent
Determi
nation
of 1
assignm
-ents
Impleme
ntation
of past
assignm
-ents
Self
assessment
report
Approval
5. Procedure of Self‐administered assessment
List of Statistics for
S lf A
Self-Assessment
t
Portal
• Information on
organization
• Information on
user
Policy-Quality
• Information on statistics
for self-assessment
• Information statistics
under
d responsibility
ibilit
Select
Statistics
Upload
Evaluation Report
Policy-Quality
• Response information
in evaluation reports
Q&A in evaluation
reports
• Reviews on evaluation
reports
Submit
for
Review
Policy-Quality
• Information on prior
evaluation reports
Review & Approval Screen for
Chief Statistics Officer (Pop-up
Window)
Policy-Quality
• Final approval by
Chief Statistics Officer
14
Future Works
VI. Future Works  Reinforcing Quality Assessment Function
• IImprovement of step by step Quality Assessment in the f
b
Q li A
i h
Production System
 Strengthening Linkage with other Systems for Export
• GSIM based Integrated Meta System, transition to SDMX integration module,  Making
Making Continuous Efforts to go with International Continuous Efforts to go with International
Standard Trends including GSIM
15
Thank you for watching
Kobong Park
Deputy Director
Informatics Planning Division
Informatics
Planning Division
Tel : +82.42.481.2351
Fax : +82.42.481.2474
E‐mail : [email protected]
16