Implementation of KSBPM in KOSTAT

Implementation of KSBPM in KOSTAT

April 2013

Ki-bong Park

Contents

I. Background

II. Development of KSBPM v2.0

III. Introduction of Nara Statistical Sys-tem

IV. Policy Management System

V. Statistical Quality Management

VI.Future Works

Ⅰ Background1. 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 ModelDevelopment of standardized statistic management and production system result in needs of statistic business process standardization

KSBPM Based on KSBPM, statistic

process is designed

KSBPM processes are mapped to functions of Nara system

Standardization for quality improvement and data sharing

Nara System is based on KSBPM

1. 기획

1.1통계 수요

파악

1.2통계수요검토

및 구체화

1.3산출목표

수립

1.4통계적 개념

정립

1.5데이터

가용성 검토

1.6통계생산

계획안 수립

2.1통계산출물

설계

2.2통계 항목

설정

2.3자료 수집 방법 설계

2.4모집단 및 표본설계

2.5자료 처리 방법 설계

2.6통계생산체계

설계

3.1 자료수집 도구 구현

3.2생산시스템

구성

3.3업무 절차

설정

3.4시스템

통합테스트

3.5생산프로세스

점검

3.6통계생산체계

확정

4.1 자료수집대상 선정

4.2자료 수집

준비

4.3자료수집

진행

4.4자료 수집

점검 및 완료

5.1 자료 통합

5.2분류 및 코딩

5.3자료검토 및

보완

5.4결측치 처리

5.5신규 변수 및

통계 단위 도출

5.6가중치의

계산

5.7집계

5.8자료 처리

완료

6.1 통계산출물

작성

6.2통계산출물

검증

6.3상세 분석 및

설명 작성

6.4정보 공개 범위 설정

6.5통계산출물

확정

7.1 공표자료

점검 및 적재

7.2공표 자료

작성

7.3자료 배포

관리

7.4자료 배포

촉진

7.5이용자

지원 관리

8.1 자료보관

규칙 정의

8.2자료 보관

관리

8.3통계 및 관련

자료 보존

8.4통계 및 관련

자료 처분

9.1 평가 계획

수립

9.2수행 및

보고서 작성

9.3개선과제

도출 , 실행 계획수립

2. 설계

4. 수집

3. 구축

6. 분석

7. 배포

8. 보관

9. 평가

5. 처리

경상남도 General Survey– 현행 업무절차

등록관리 통계표 관리

공동서식관리

공통모듈 설계

매뉴얼관리

수집 마감관리

분석 마감관리

산출물 작성

자료이관 일정관리

정보공개 관리

상세설명 작성

시스템 관리

수집자료 내검

KOSIS 관리

입력 포털 구현

기획 설계 구축 수집 처리 분석 배포

관광사업체 기초통계조사 – 현행 업무절차

기획 설계 구축 수집 처리 분석 배포

등록관리 조사표 설계

표본설계

집계표 설계

내검 설계

공통모듈 설계

매뉴얼관리

표본추출

명부관리

조사입력

분류 및 코딩

결측치 처리

처리 마감관리

처리결과 내검

수집 마감관리

분석 마감관리

산출물 작성

자료이관 일정관리

정보공개 관리

상세설명 작성

시스템 관리

수집자료 내검

KOSIS 관리

입력 포털 구현

Based on GSBPM, KSBPM is edited for Korea statistical environment

Differences in business process in each statistic cases and agencies

2. Introduction of GSBPM

1.1 Determine needs for

information1.2

Consult and confirm needs

1.3Establish output

objectives

1.4Identify

concepts

1.5Check data availability

1.6Prepare

business case

2.1Design outputs

2.2Design variable

descriptions2.3 Design

data collection

methodology2.4 Design frame and

sample methodology

2.5Design statistical processing

methodology2.6 Design production

systems and workflow

3.1 Build data collection

instrument3.2 Build or

enhance process

components

3.3 Configure workflows

3.4Test production

system3.5Test

statistical business process

3.6Finalize production

system

4.1 Select sample

4.2Set up

collection

4.3Run collection

4.4Finalize

collection

5.1 Integrate data

5.2Classify and

code

5.3 Review, validate and

edit

5.4 Impute

5.5Derive new variables and statistical

units5.6

Calculate weights

5.7 Calculate aggregates

5.8Finalize data

files

6.1 Prepare draft

outputs

6.2Validate outputs

6.3Scrutinize and

explain

6.4 Apply disclosure

control

6.5Finalize outputs

7.1 Update output

systems7.2 Produce

dissemination products

7.3 Manage release of


7.4 Promote dissemination

products

7.5Manage user

support

8.1 Define

archive rules

8.2 Manage archive

repository8.3 Preserve

data and associated metadata

8.4 Dispose of data and

associated metadata

9.1 Gather evaluation

inputs

9.2Conduct evaluation

9.3 Agree action plan

Quality Management / Meta Data Management1.

Specify Needs

2Design

3Build

4Collect

5Process

6Analyze

7Disseminate

8Archive

9Evaluate

- 9 Mega phases and 47 sub-processes

3. The Role of KSBPM• KSBPM guides to high-quality, low-cost, high-efficiency statistic

production system by standardizing and automating process

Standardization

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

Automation

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

Standardized Process-Driven Automation

High-quality StatisticLow-cost

ProductionHigh-

efficiency Production

Expectation

WHYKSBPM?

4. Statistical Environment(1)

Features of Korean Statistical System

CentralizedCentralized producing agency

eg) Canada, Germany, Sweden, Australia, Netherlands

DecentralizedEach government Agencies produce

their own statisticseg) USA, Korea, Japan, UK, France

Korean Statistical System is decentralized system which is partly centralized

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 statis-tics while most investment is on economic statistics

4. Statistical Environment(2)

Disadvantage of Decentralized Statistical System

Ambiguity on information searching siteTime consuming process for searching information

Difficulty in data comparison due to non-standardization

Budget wasting due to non-integrated system development

Decentralized Statistical Information

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 managementHelp

understanding the systemic

production of statistics

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

Base of statistic production automation

Provide systemic analysis process (i.e.Nara System) in automation of statistic production through IT technology (for Data collection, process, analysis)

Reference of data and metadata

standardization

Reference for the management of metadata in decentralized statistic production system

Usage of KSBPM

Ⅱ Development of KSBPM v2.0

1. Trends for International Standard2. 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

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

GSIM(Information

Concept)

Methods(Statistical How

To)

Conc

eptu

al

Used for realization

Prac

tical

Standard Concept of Analysis Object

※ Source : United Nations Economic and Social Council (2011). Strategic vision of the High- level group for strategic developments in business architecture in statistics.

1

2

3

Generic Statistical Busines Process Model (GSBPM)

Generic Statistical Information Model (GSIM)

MACRO/ MICRO Data Exchange (SDMX, DDI)Technology

(ProductionHow To)

GSBPM(Business Concept)

Common Generic

Industrial Statistics

2. Implications for developing KSBPM v2.0

KSBPM v2.0 Concept

Enhance general

reference model

Rename standard

termsDDI

SDMX

Role of generic reference model in producing official statistics should be strengthened.

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.

Life cycle of statistical data can be referenced using just GSBPM, and therefore does not require direct changes to KSBPM v2.0.

As SDMX is data and meta data transmission regulation, it does not require any changes to KSBPM v2.0.

Implications for developing KSBPM v2.0 based on assessment of current status

GSBPM

GSIM

Add quality assessment

process

Analyze Trends in

International Standards

Examine Current State of

Nara Statistical System

KSBPM v1.0

Guidelines of

Official Statistics

Functions for generic model and processes should be redefined and renamed.

Duplicate processes (i.e. budget appropriation, determining survey coverage) should be integrated

Standard names for common use by organization both in- and outside Statistics Korea should be defined.

Inclusion of statistical quality assessment should be considered.

3. Steps Taken for Development of KSBPM v2.0

Guidelines of OfficialStatistics

7. Disseminate

6. Process Non-Responses and Analyze Data

5. Process

4. Collect

3. Design & Manage Sample

2. Design

1. Plan

Statistical Quality

AssessmentHandbook

7. Follow-up

6. Document & Disseminate

5. Analyze Data and Evaluate Quality

4. Enter & Process Data

3. Collect

2. Design

1. Plan

KSBPM v2.0

8. Archive

7. Disseminate

9.Evaluae

6. Analyze

5. Process

4. Collect

3. Build

2. Design

1. Plan

Government Manual

for Statistics

7. Disseminate

6. Analyze

5. Process

4. Collect

3. Prepare Collection

2. Design

1. Plan

8. Archive

9. Evaluate

Task ForceTeam

Meetings

8. Archive

7. Disseminate

9. Evaluate

6. Analyze

5. Process

4. Collect

3. Build

2. Design

1. Plan

KSBPM v1.0

8. Archive

7. Disseminate

9. Evaluate

6. Analyze

5. Process

4. Collect

3. Build

2. Design

1. Plan

GSBPM v4.0

8. Archive

7. Disseminate

9. Evaluate

6. Analyze

5. Process

4. Collect

3. Build

2. Design

1. Specify Needs

4. Changes of Processes for KSBPM v2.0

1. Plan

1.1Determine statistical demand

1.2Verify & Specify

statistical demand

1.3Establish output

objectives

1.4Identify

statistical concepts


1.6Make production

plan

2.1Design output

2.2Design variables

2.3Design collection

methodology

2.4Design universe

& sample

2.5Design

processing methodology

2.6Design

production system

3.1 Build collection

instrument

3.2Build production

system

3.3Configure workflows

3.4Test production

system

3.5Test business

process

3.6Finalize

production system

4.1 Select sample

4.2Prepare

collection

4.3Run collection

4.4Finalize

collection

5.1 Integrate data

5.2Classify & code

5.3Review, validate

& edit

5.4Impute

5.5Derive new variables &

statistical units

5.6Calculate weights

5.7Calculate

aggregates

5.8Finalize data

processing

6.1 Prepare draft

outputs

6.2Validate outputs

6.3Scrutinize &

explain

6.4Apply disclosure

control

6.5Finalize outputs

7.1 Prepare dissemination

data

7.2Produce disseminate

products



7.4Promote

dissemination Products

7.5Manage user

support

8.1 Define archive

rules

8.2Manage archive

repository

8.3Preserve data &

associated metadata

8.4Dispose of data

& associated metadata

9.1 Make evaluation

plan

9.2Conduct

evaluation & produce reports

9.3 Derive improvement plans & make

action plan

2. Design

4. Collect3. Build 6.

Analyze7.

Disseminate8.

Archive9.

Evaluate5.

Process

Processes revised from KSBPM v1.0

9 mega processes renamed and 21 sub-pro-cesses revised

5. Establishment of KSBPM v2.0

1. Plan

1.1Determine statistical demand

1.2Verify & Specify

statistical demand

1.3Establish output

objectives1.4

Identify statistical concepts


1.6Make

production plan

2.1Design output

2.2Design

variables

2.3Design

collection methodology

2.4Design

universe & sample

2.5Design

processing methodology

2.6Design

production system

3.1 Build collection

instrument

3.2Build

production system

3.3Configure workflows

3.4Test

production system

3.5Test business

process

3.6Finalize

production system

4.1 Select sample

4.2Prepare

collection

4.3Run collection

4.4Finalize

collection

5.1 Integrate data

5.2Classify & code

5.3Review,

validate & edit

5.4Impute

5.5Derive new variables &

statistical units

5.6Calculate weights

5.7Calculate

aggregates

5.8Finalize data processing

6.1 Prepare draft

outputs

6.2Validate outputs

6.3Scrutinize &

explain

6.4Apply

disclosure control

6.5Finalize outputs

7.1 Prepare dissemination

data

7.2Produce disseminate

products



7.4Promote

dissemination Products

7.5Manage user

support

8.1 Define archive

rules

8.2Manage archive

repository8.3

Preserve data & associated

metadata8.4

Dispose of data &

associated metadata

9.1 Make

evaluation plan9.2

Conduct evaluation &

produce reports

9.3 Derive improvement plans & make

action plan

2. Design

4. Collect3. Build 6.

Analyze7.

Disseminate8.

Archive9.

Evaluate5.

Process

※ KSBPM : 9 phases and 47 pro-cesses

Ⅲ Introduction of Nara Sys-tem1. Development of GSIS2. Configuration of Nara Statistical System3. Sub-system’s Outline

1. Development of GSIS

• Integrating and streamlining statistical policy, production, and metadata mgmt. systems

• Common use system based on standardized statistical business process※ Application of Global Standard

(GSBPM)

• Interface with existing systems(KOSIS, MDSS, etc)

Policymakers

KOSIS

MDSS

Macrodata

Microdata Standard Prcs.

Metadata

Service Polic

y

Production

DataMgm

t.

Common use

System

Agencies

Int’lOrg.

Research

People

2. Configuration of Nara Statistical System

Object systemDBStatistical design

Registration of surveys

Questionnaire Design

Edit design

Summary table designSurvey

methodologySystem architecture

management

Data collection

Register management

Assignment of enumerator business

Data collection management

Input edit

Ending of data collection

Population management

National statistics portal

User information

DB

Integrated

national statistic

s DB (KOSIS)

GIS DB

Establishment

Administrative data DB

Data processing &analysis system

Treatment ofmissing values

Tabulation

Batch processediting

Ending of data processing

Tabulation and analysis edit Weighting

Raw data Microdata

Data manage-ment system

Manage dissemination data

Prepare dissemination data

Dissemination data

Policy makers

Research institutes

User groups

Policy makers

Research institutes

General users

Self & regular check

Transfer

Statistical production agencies

KOSTAT

Metadata on statistics

Statistical termsmetadata

Metadata on statistical production

Population/Establishment

Storage DB

Demand informationData

storage

ApprovalRequest for approvalTransfer

/ storage

DW DB MDSS

Statistical production agencies

Productionagencies

Central government(36 agencies)

Local governments

(260 agencies)

Private designated agencies

(77 agencies)

Stat

isti

cal

Prod

ucti

on s

yste

m

Statistical metadata management system

Macrodata

Statisticalstandards

Standard DB

Statistical policy

Statistical demand

DBQuality

management

Quality check

DBStatistical

review

Review DB

Statistical approval

Approval DBIntegration Integration Integration

Stat

isti

cal

polic

y

3. Sub-system’s Outline

• Standard Production System supporting comprehensive business processes based on KSBPM

• Share and reuse of variables, questions, surveys, tables and editing rules based on statistical metadata

• Approval, Evaluation, Quality Management of Statistics• Share of information among related works

• 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

PolicyManagement

StatisticalProduction

MetadataManagement

Web-Portal

Ⅳ Policy Management Sys-tem

1. Configuration of Statistical Policy Management System(1)

2. Configuration of Statistical Policy Management System(2)

1. Configuration of Stat. Policy Mgmt. System

Statistical Policy Management System

Statistical Policy

• Management Evidence based policy making system

• Long/Medium term de-velopment plan

• Management of na-tional statistical system

• Agency selection• Approval on the official

statistics (production, modification, cancela-tion, etc)

Evaluation

Coordination

• Regular inspection• Support for self-inspec-

tion

Quality Mgmt.

KOSTATIntranetsystem

StatisticalProduction

systemQualityMgmt.officer

PolicyMgmt.officer

2. Configuration of Stat. Policy Mgmt. SystemSt

atis

tica

l Pro

duct

ion

Syst

em

Plan Design Collect Enter & ProcessData Analyze Disseminate Follow-up

Plan Report Request forapproval

Quality Assessment

Quality Assessment

Quality Assessment

Quality Assessment

Quality Assessment

Request forchange

Quality Assessment

Quality Assessment

Official StatisticsDevelopments

Overall demand

Select target

Explain and check tasks

Statistical Demand

Statistical demand

Demand survey

Check implementation

Evaluation

Pre-evaluation

Evaluation management

Pilot evaluation

Actual evaluation

Policy Support Service

System-wide search

Search on approved statistics

Statistical historymanagement

Statistical developmentstatus

Chief StatisticsOfficer status

Relevant agencies status

Quality Management

Regular Assessment

Regular quality assessment

Areas for improvement basedon regular assessment

Table of regularassessment results

Statistical Approval

Agency designation

Revoke agency designation

Designation of statistics

Revoke designationof statistics

Approve compilation(consultation)

Approve modification(consultation)

Approve suspension(consultation)

Revocation of approval

Approve non-release

Statistical results

Consultation on disseminationafter non-release

Self Assessment

Self-administeredquality assessment

Table of self assessment results

Ad-hoc assessment

Ad-hoc quality assessment

Register laws

Infra management

Register policies

Register statisticalindicators Statistics producing

agencies status

Approve statistics status

Subject evaluation

Subject area evaluation

Regional statistical demand

Regional statisticaldemand survey

Check implementation

Statistical Policy System

V Stat. Quality Manage-ment

1. Introduction of Quality Assessment

2. Procedure of Regular Quality Assessment


4. Structure of Self Assessment Procedure

5. Procedure of Self-administered assessment

1. Introduction of Quality Assessment

• 기능–

Fitness for use Multi-dimensional concept

Accuracy, Coherence, Compatibility, Timeliness, Accessibility, Relevance

Definition of Quality

Regular Quality Assessment Non-Regular Quality Assessment Self Quality Assessment

Kinds of Quality Assessment


11. Basis/Environment

2. Users’ satis-faction & needs

3.Process-review

4.Accuracy in data collection

5.Data Service

Put together

• Identify problems• Draw assignments for quality improve-ment• Feed assignments back to statistical agencies Implementation

Statistics Agencies

5 sector assessment


Screen for regular assess-ment functions (pop-up

window)

Table of quality management infrastruc-tureQuality evaluation report for individual statistical procedureError check table for dissemination dataReference materials

List of statistics for regular assessment

Select statis-

tics

Select func-tion

• Information on statistics for regular assessment

• Information on organizationand user

List of regular as-sessment functions

Portal Quality-Pol-icy

• Basic information• Information on human resources • Information on physical resources • Interviews on views on statistical management

Quality-Policy

• Information on user• Response information • Supporting materials• Information on researchers

Quality-Policy

• Information on dissemination data• Information on responses for check table• Information on researchers

Quality-PolicyReference materials

• Information on statistics for regular assessment

Quality-Pol-icy

• Information on Quality Evaluation Team

Quality-Policy

4. Structure of Self Assessment Procedure

1Conduct-ing as-sess-ment

Printing the as-sess-ment sheet

Verifica-tion of derived assignm-ent

Determi-nation of assignm-ents

Imple-menta-tion of past as-signm-ents

Self as-sess-ment report

Approval

5. Procedure of Self-administered assessment

UploadEvaluation ReportList of Statistics for

Self-AssessmentSelect

Statistics

• Information on organization• Information on user

Portal

• Response information in evaluation reportsQ&A in evaluation reports• Reviews on evaluation reports

Policy-Quality

• Information on statis-tics for self-assessment• Information statistics under responsibility

Policy-Quality

• Information on prior evaluation reports

Policy-Quality

Submit for Re-view

Review & Approval Screen for Chief Statistics Officer (Pop-

up Window)

• Final approval by Chief Statistics Officer

Policy-Quality

Ⅵ Future Works

VI. Future Works

Reinforcing Quality Assessment Function• Improvement of step by step Quality Assessment in the

Production System Strengthening Linkage with other Systems for Export

• GSIM based Integrated Meta System, transition to SDMX integration module,

Making Continuous Efforts to go with International Standard Trends including GSIM

Kobong Park

Deputy Director

Informatics Planning Division

Tel : +82.42.481.2351

Fax : +82.42.481.2474

E-mail : [email protected]

Thank you for watching

Documents

Implementation of KSBPM in KOSTAT