Implementation of KSBPM in KOSTAT Implementation of KSBPM in KOSTAT - - PDF document

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Implementation of KSBPM in KOSTAT Implementation of KSBPM in KOSTAT

April 2013

Ki-bong Park

Contents Contents

I. I. Background Background II.

• II. Development of KSBPM v2.0

Development of KSBPM v2.0 III.

• III. Introduction of Nara Statistical System

Introduction of Nara Statistical System IV.

• IV. Policy Management System

Policy Management System V.

• V. Statistical Quality Management

Statistical Quality Management VI.

• VI. Future Works

Future Works

2

Background Background Background 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
• 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– 현행 업무절차 경상남도 General Survey– 현행 업무절차

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

• n KSBPM

1. 기획

2. 설계 4. 수집 3. 구축 6. 분석 7. 배포 8. 보관 9. 평가 5. 처리

경상남도 General Survey 현행 업무절차

기획 설계 구축 수집 처리 분석 배포 경상남도 General Survey 현행 업무절차

기획 설계 구축 수집 처리 분석 배포 관광사업체 기초통계조사 – 현행 업무절차 관광사업체 기초통계조사 – 현행 업무절차

Differences in business process in each statistic cases and agencies sharing

기획 설계 구축 수집 처리 분석 배포

기획 설계 구축 수집 처리 분석 배포

Based on GSBPM, KSBPM is edited for Korea statistical environment

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• 2. Introduction of GSBPM
• 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 1.2 Consult and confirm needs 1.3Establish

• utput
• bjectives

1.4 Identify concepts 2.1 Design outputs 2.2 Design variable descriptions 2.3 Design data collection methodology 2.4 Design frame and sample methodology 3.1 Build data collection instrument 3.2 Build or enhance process components 3.3 Configure workflows 3.4Test production system 4.1 Select sample 4.2 Set up collection 4.3 Run collection 4.4 Finalize collection 5.1 Integrate data 5.2 Classify and code 5.3 Review, validate and edit 5.4 Impute 6.1 Prepare draft

• utputs

6.2 Validate outputs 6.3 Scrutinize and explain 6.4 Apply disclosure control 7.1 Update output systems 7.2 Produce dissemination products 7.3 Manage release of dissemination products 7.4 Promote dissemination products 8.1 Define archive rules 8.2 Manage archive repository 8.3 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 1.5 Check data availability 1.6 Prepare business case 2.5Design statistical processing methodology 2.6 Design production systems and workflow 3.5Test statistical business process 3.6Finalize production system 5.5Derive new variables and statistical units 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data files 6.5 Finalize

• utputs

7.5 Manage user support

• 9 Mega phases and 47 sub-

processes

• 3. The Role of KSBPM
• 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

Automation

• Shorten the period of

statistic production and improve work efficiency

• Save expense by

Standardized Process-Driven Automation

High-quality Statistic Low-cost Production High-

Expectation

WHY KSBPM?

• Encourage re-usage of

data and statistic production

• Enhance the international

status of Statistics Korea by following International standard preventing development of duplicated system

• Promote co-operation by

automating data links among statistic produce agencies

efficiency Production

4

• 4. Statistical Environment(1)
• 4. Statistical Environment(1)

Features of Korean Statistical System

Centralized Centralized

C t li d d i

Decentralized Decentralized

E h t A i d Centralized producing agency eg) Canada, Germany, Sweden, Australia, Netherlands Each government Agencies produce 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)
• 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 integrated Difficulty in data comparison due to non-standardization Budget wasting due to non-integrated system development

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• 5. Usage Cases of KSBPM
• 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

• 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

• Provide systemic analysis process (i.e.Nara System) in

automation of statistic production through IT technology (for Data collection process analysis)

Usage of

KSBPM

automation

technology (for Data collection, process, analysis)

Reference of data and metadata standardization

• Reference for the management of metadata in

decentralized statistic production system

Development of KSBPM v2 Development of KSBPM v2 Development of KSBPM v2 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

f

• 5. Establishment of KSBPM v2.0

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• 1. Trends for International Standard
• 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

GSI M

(I nformation Concept)

Conceptual

Standard Concept of Analysis Object 1 2 Generic Statistical Busines Process Model (GSBPM) Generic Statistical Information Model (GSIM) MACRO/ MICRO Data

GSBPM

(Business Concept) Common Generic I ndustrial Statistics

Methods

(Statistical How To)

Used for realization Practical

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

3 MACRO/ MICRO Data Exchange (SDMX, DDI)

Technology

(Production How To)

• 2. Implications for developing KSBPM v2.0
• 2. Implications for developing KSBPM v2.0

Implications for developing KSBPM v2.0 based on assessment of current status KSBPM v2.0 Concept Enhance general reference model Rename standard terms

DDI

• Role of generic reference model in producing official

statistics should be strengthened.

• As a generic model, standard names for common use by
• rganization 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,

GSBPM GSIM

Analyze Trends in International Standards

SDMX

g , it does not require any changes to KSBPM v2.0.

Add quality assessment process

Examine Current State

• f 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.

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• 3. Steps Taken for Development of KSBPM v2.0
• 3. Steps Taken for Development of KSBPM v2.0

Guidelines

• f Official

Statistics Statistical Quality Assessment Handbook KSBPM v2.0 Government Manual for Statistics Task Force Team Meetings KSBPM v1.0 GSBPM v4.0

• 5. Process
• 4. Collect
• 3. Design &

Manage Sample

• 2. Design
• 1. Plan
• 5. Analyze Data

and Evaluate Quality

• 4. Enter &

Process Data

• 3. Collect
• 2. Design
• 1. Plan
• 5. Process
• 4. Collect
• 3. Build
• 2. Design
• 1. Plan
• 5. Process
• 4. Collect
• 3. Prepare

Collection

• 2. Design
• 1. Plan
• 5. Process
• 4. Collect
• 3. Build
• 2. Design
• 1. Plan
• 5. Process
• 4. Collect
• 3. Build
• 2. Design
• 1. Plan
• 5. Process
• 4. Collect
• 3. Build
• 2. Design
• 1. Specify

Needs

• 7. Disseminate
• 6. Process Non-

Responses and Analyze Data

• 7. Follow-up
• 6. Document &

Disseminate Quality

• 8. Archive

7. Disseminate 9.Evaluae

• 6. Analyze

7. Disseminate

• 6. Analyze
• 8. Archive
• 9. Evaluate
• 8. Archive

7. Disseminate

• 9. Evaluate
• 6. Analyze
• 8. Archive

7. Disseminate

• 9. Evaluate
• 6. Analyze
• 8. Archive

7. Disseminate

• 9. Evaluate
• 6. Analyze
• 4. Changes of Processes for KSBPM v2.0
• 4. Changes of Processes for KSBPM v2.0
• 1. Plan
• 2. Design
• 4. Collect
• 3. Build
• 6. Analyze

7. Disseminate

• 8. Archive

9. Evaluate

• 5. Process

9 mega processes renamed and 21 sub-processes revised

1.1 Determine statistical demand 1.2 Verify & Specify statistical demand 1.3 Establish output

• bjectives

1.4 Identify statistical concepts 1 5 2.1 Design output 2.2 Design variables 2.3 Design collection methodology 2.4 Design universe & sample 2.5 3.1 Build collection instrument 3.2 Build production system 3.3 Configure workflows 3.4 Test production system 3 5 4.1 Select sample 4.2 Prepare collection 4.3 Run collection 4.4 Finalize collection 5.1 Integrate data 5.2 Classify & code 5.3 Review, validate & edit 5.4 Impute 5.5 6.1 Prepare draft

• utputs

6.2 Validate outputs 6.3 Scrutinize & explain 6.4 Apply disclosure control 7.1 Prepare dissemination data 7.2Produce disseminate products 7.3 Manage release of dissemination products 7.4 Promote dissemination Products 7 5 8.1 Define archive rules 8.2 Manage archive repository 8.3 Preserve data & associated metadata 8.4 Dispose of data & associated metadata 9.1 Make evaluation plan 9.2 Conduct evaluation & produce reports 9.3 Derive improvement plans & make action plan 1.5 Check data availability 1.6 Make production plan 2.5 Design processing methodology 2.6 Design production system 3.5 Test business process 3.6 Finalize production system 5.5 Derive new variables & statistical units 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data processing 6.5 Finalize outputs 7.5 Manage user support

Processes revised from KSBPM v1.0

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• 5. Establishment of KSBPM v2.0
• 5. Establishment of KSBPM v2.0
• 1. Plan

1.1 Determine 2.1 3.1 Build collection 4.1 5.1 6.1 Prepare draft 7.1 Prepare dissemination 8.1 Define archive 9.1 Make evaluation

• 2. Design
• 4. Collect
• 3. Build
• 6. Analyze

7. Disseminate

• 8. Archive
• 9. Evaluate
• 5. Process

statistical demand 1.2 Verify & Specify statistical demand 1.3 Establish output

• bjectives

1.4 Identify statistical concepts 1.5 Check data availability Design output 2.2 Design variables 2.3 Design collection methodology 2.4 Design universe & sample 2.5 Design processing Build collection instrument 3.2 Build production system 3.3 Configure workflows 3.4 Test production system 3.5 Test business process Select sample 4.2 Prepare collection 4.3 Run collection 4.4 Finalize collection Integrate data 5.2 Classify & code 5.3 Review, validate & edit 5.4 Impute 5.5 Derive new variables & Prepare draft

• utputs

6.2 Validate outputs 6.3 Scrutinize & explain 6.4 Apply disclosure control 6.5 Finalize outputs dissemination data 7.2Produce disseminate products 7.3 Manage release of dissemination products 7.4 Promote dissemination Products 7.5 Manage user support Define archive rules 8.2 Manage archive repository 8.3 Preserve data & associated metadata 8.4 Dispose of data & associated metadata Make evaluation plan 9.2 Conduct evaluation & produce reports 9.3 Derive improvement plans & make action plan availability 1.6 Make production plan p g methodology 2.6 Design production system process 3.6 Finalize production system statistical units 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data processing p support

※ KSBPM : 9 phases and 47 processes

Introduction of Nara Syste Introduction of Nara Syste Introduction of Nara Syste Introduction of Nara Syste

• 1. Development of GSIS
• 2. Configuration of Nara Statistical System
• 3. Sub‐system’s Outline

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• 1. Development of GSIS
• 1. Development of GSIS
• Integrating and streamlining

statistical policy, production, and

Policy

Research

People

metadata mgmt. systems

• Common use system based on

standardized statistical business process

※ Application of Global Standard

Policy makers

Metadata

Service Policy Data M t

Common use System

Int’l Org.

(GSBPM)

• Interface with existing

systems(KOSIS, MDSS, etc)

Macrodata Microdata Standard Prcs.

Production Mgmt.

Agencies

• 2. Configuration of Nara Statistical System
• 2. Configuration of Nara Statistical System

User information User groups Statistical production agencies Standard DB Statistical demand Quality check Review DB Approva l DB Integration Integration Integration stical

• licy

Object system DB Statistical design Registration

• f surveys

Questionnaire Design Edit design Data collection Register management Assignment of enumerator business Population management

National statistics portal DB

Integrated national statistics DB (KOSIS) Establishment Administrativ e data DB Data processing & analysis system Raw data Microdata Data manage- ment system Di i ti General users Self & regular check Transfer Statistical production agencies KOSTAT Storage DB Demand information Data storage Approval Request for approval Transfer / storage

DW DB MDSS Production agencies Central government (36 agencies)

Local governments (260 agencies) P i t

stical uction stem Macrodata Statistical standards DB

Statistical policy DB Quality management DB Statistical review Statistical approval

Stati po

g Summary table design Survey methodology System architecture management enumerator business Data collection management Input edit Ending

• f data collection

GIS DB Treatment of missing values Tabulation Batch process editing Ending of data processing Tabulation and analysis edit Weighting Manage dissemination data Prepare dissemination data Disseminatio n data Policy makers Research institutes Policy makers Research institutes Metadata

• n statistics

Statistical terms metadata Metadata on statistical production

Population/ Establishment Private designated agencies (77 agencies)

Statis Produ sys Statistical metadata management system

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• 3. Sub‐system’s Outline
• 3. Sub‐system’s Outline
• Approval, Evaluation, Quality Management of Statistics

Policy M t

• 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

• Share of information among related works
• Provides framework for the share and reuse of statistics

Management Statistical Production Metadata

• Unification of metadata of existing information systems
• Single Sign On for policy management, statistical production, and

metadata management of the statistical agencies

Management Web-Portal

Policy Management Syste Policy Management Syste Policy Management Syste Policy Management Syste

• 1. Configuration of Statistical Policy Management System(1)
• 2. Configuration of Statistical Policy Management System(2)

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• 1. Configuration of Stat. Policy Mgmt. System
• 1. Configuration of Stat. Policy Mgmt. System

Statistical Policy Management System Statistical Policy Evaluation y

• Management Evidence

based policy making system

• Long/Medium term

development plan

• Management of national

statistical system

• Agency selection

Coordination

• Regular inspection

Quality Mgmt. KOSTAT Intranet system St ti ti l Policy Mgmt.

• fficer
• Agency selection
• Approval on the official

statistics (production, modification, cancelation, etc)

• Regular inspection
• Support for self-

inspection

Statistical Production system Quality Mgmt.

• fficer
• 2. Configuration of Stat. Policy Mgmt. System
• 2. Configuration of Stat. Policy Mgmt. System

stical Production System

Plan Design Collect Enter & Process Data Analyze Disseminate Follow-up

Plan Report Request for approval Request for change Statis Quality Assessment Quality Assessment Quality Assessment Quality Assessment Quality Assessment change Quality Assessment Quality Assessment Official Statistics Developments

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 history management Statistical development status Chief Statistics Officer status Quality Management Regular Assessment Regular quality assessment Areas for improvement based

• n regular assessment

Table of regular assessment results Statistical Approval Agency designation Revoke agency designation Designation of statistics Revoke designation

• f statistics

Approve compilation (consultation) Approve modification (consultation) Self Assessment Self-administered quality assessment Register laws I nfra management Regional statistical demand Regional statistical demand survey Check implementation Relevant agencies status Approve suspension (consultation) Revocation of approval Approve non-release Statistical results Consultation on dissemination after non-release Self Assessment Table of self assessment results Ad-hoc assessment Ad-hoc quality assessment Register policies Register statistical indicators Statistics producing agencies status Approve statistics status Subject evaluation Subject area evaluation

Statistical Policy System

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• Stat. Quality Management
• Stat. Quality Management
• Stat. Quality Management
• 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
• 1. Introduction of Quality Assessment

f Definition of Quality  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

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• 2. Procedure of Regular Quality Assessment
• 2. Procedure of Regular Quality Assessment

1

5 sector assessment

1 1. Basis/ Environment 2. Users’ satisfaction & needs 3. Process- review 4. Accuracy in data collection 5. Data Service

Put together Put together

• Identify problems
• Draw assignments for quality

improvement

• Feed assignments back to statistical

agencies Implementation

Statistics Agencies

• 3. Procedure of Regular Quality Assessment
• 3. Procedure of Regular Quality Assessment

Screen for regular assessment functions (pop- up window)

Table of quality management infrastructure Quality evaluation report for individual statistical procedure Error check table for dissemination data

List of statistics for regular assessment Select statistic Select function List of regular assessment functions up window)

Reference materials

s

• Information on

statistics for regular assessment

• Information
• n
• rganization

and user

Portal Quality-Policy

• Basic information
• Information on

human resources

• Information on

physical resources

• Interviews on

views on statistical t

Quality-Policy

• Information on user
• Response

information

• Supporting materials
• Information on

researchers

Quality-Policy

• Information on

statistics for regular assessment

Quality-Policy

management

• Information on

dissemination data

• Information on

responses for check table

• Information on

researchers

Quality-Policy

Reference materials

• Information on

Quality Evaluation Team

Quality-Policy

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• 4. Structure of Self Assessment Procedure
• 4. Structure of Self Assessment Procedure

1 Conduct ing Printing the Verificat ion of Determi nation Impleme ntation Self assess- Approval 1 g assess- ment assess- ment sheet derived assignm

• ent
• f

assignm

• ents
• f past

assignm

• ents

ment report

• 5. Procedure of Self‐administered assessment
• 5. Procedure of Self‐administered assessment

Upload Evaluation Report List of Statistics for S lf A t Select Statistics Submit for Review & Approval Screen for Chief Statistics Officer (Pop-up Evaluation Report Self-Assessment Statistics

• Information on
• rganization
• Information on

Portal

• Response information

in evaluation reports Q&A in evaluation reports

Policy-Quality

• Information on statistics

for self-assessment

• Information statistics

d ibilit

Policy-Quality

• Information on prior

evaluation reports

Policy-Quality for Review Window)

• Final approval by

Chief Statistics Officer

Policy-Quality

user reports

• Reviews on evaluation

reports under responsibility

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Future Works Future Works Future Works Future Works

• VI. Future Works
• VI. Future Works
• Reinforcing Quality Assessment Function

I f b Q li A i h

• 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
• Making Continuous Efforts to go with International

Standard Trends including GSIM

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Thank you for watching

Kobong Park

Deputy Director Informatics Planning Division Informatics Planning Division

Tel : +82.42.481.2351 Tel : +82.42.481.2351 Fax : +82.42.481.2474 Fax : +82.42.481.2474 E‐mail : kbpark@korea.kr mail : kbpark@korea.kr