GSIS based on KSBPM

GSIS : Generic Statistical Information System

Establishment of KSBPM

System Development

Plans

Ⅱ

Ⅲ

Ⅳ

OverviewⅠ

3

Ⅰ Overview

1. Current Status2. Problems

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 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)

Current status1

Statistical Personnel

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 refer to officials whose statistical work occupies more than 50 percent of the their responsibilities.

Statistical personnel recorded 4,530 persons in 2010,which rose by 115 persons from 2008. Out of them,enumerators occupied 56.7 percent.

Current status1

Information Systems

The majority of statistical agencies produce statistics through outsourcing due to the absences of the statistical production and management system.

ClassificationCentral

government agencies

Local government

agenciesDesignated 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

Current status1

ProblemsProblems2

Central ALocal AOther A

PlanningSurvey Design

DataCollection Prep.

DataCollection

DataProcessing

Analysis

Release

Archive

Meta DataMgmt

Quality Control

7.0

8

Ⅱ Establishment of the KSBPM

1. Backgrounds2. Derivation of production process pool 3. Establishment of the KSBPM4. Major characteristics of the KSBPM

9

Internal and external conditions

Necessary to standardize the production and dissemination processes of national statistics

Necessary to establish governance over national

statistics

Social and economic loss owing to the production of inaccurate statisticsPublic confusion due to similar or redundant statisticsMore demand for the systemization of statistical production and dissemination

A waste of resources due to individual production and management of statistics

Necessary to establish the efficient management system of national statistics under the decentralized statistical systemNecessary to switch post quality management into ‘pre- and post-management’

Poor statistical quality caused by lack of statistical production systems

Necessary to standardize different production processes of individual surveysNecessary to integrate and share statistical information that is managed by each statistical agency

Poor infrastructure for production and management of national statistics due to non-standardized processes

Background1

Necessary to establish the standardized produc-tion and management processes of national sta-

tistics

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Derivation of a process pool (1/3)2Analyze 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

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Derivation of a process pool (2/3)2

Survey guidelines

7. Dissemination

6. Imputation and analysis

5. Processing

4. Collection

3. Sample design & management

2. Questionnaire design

1. Survey planning

Quality manage-ment

handbook

7. Follow-up

6. Documentation and dissemination

5. Analysis and quality evaluation

4. Input and processing

3. Collection

2. Design

1. Planning

GSBPM

8. Archive

7. Disseminate

9. Evaluate

6. Analyze

5. Process

4. Collect

3. Build

2. Design

1. Specify needs

Final draft

8. Archive

7. Disseminate

9. Evaluate

6. Analyze

5. Process

4. Collect

3. Build

2. Design

1. Plan & specify needs

KOSTAT business manuals

+ survey results

7. Dissemination

6. Analysis

5. Processing

4. Collection

3. Preparation for data collection

2. Design

1. Survey planning

8. Archiving

9. Evaluation

Reorganize the KSBPM after analyzing, linking and supplementing model candidates

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Derivation of a process pool (3/3)2Phases and sub-processes of the KSBPM

1. Plan & specify needs

2. De-sign 3. Build 4. Col-

lect 5. Process 6. Ana-lyze

7. Dissem-inate 8. Archive 9. Evaluate

1.1 Specify Needs

1.2 Consult & Review needs1.3 EstablishStatistical con-cepts 1.4 EstablishOutput objec-tives 1.5 Draw up bud-get

1.6 Make produc-tion plan

2.1 Designoutputs

2.2 Design variables de-scriptions

2.3 Design a frame

2.4 Design collection methodology

2.5 Design a sample methodology

2.6 Design Processing methodology2.7 Design work-flow

3.1 Build/ supplement data collection tools3.2 Config-ure system functions

3.3 Check/supplement the system

3.4 Test the sys-tem

3.5 Finalize the production system

4.1Select a sam-ple

4.2Prepare for col-lection

4.3Collect data

4.4Finalize collec-tion

5.1Integrate data

5.2Classify & code

5.3Validate & supplement

5.4Impute

5.5 Derive new variables & statistical units5.6Calculate weights

5.7 Tabulate

5.8 Finalize data files

6.1 Prepare output draft

6.2Validate out-puts

6.3Scrutinize & explain

6.4Apply disclo-sure control

6.5Finalize out-puts

7.1 Load/ check tabulation data

7.2 Produce dissemination data

7.3Disseminate

7.4Promote dis-semination

7.5Support users

8.1 Define archiv-ing rules

8.2Archive

8.3 Archive asso-ciated data

8.4 Dispose of as-sociated data

9.1Decide a checklist

9.2Evaluate

9.3 Derive challenges and make action plans

Chech data availability

Configure workflow

Removed sub-process from GSBPM

Added sub-process from GSBPM

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Establishment of the KSBPM3Derivation of the KSBPM

GovernanceStatistics-based

policy man-agement

Policy management

Quality management

Statistical coordination

Planning Collection Dissemination

Design Processing Archiving

Implementation Analysis Evaluation

Quality support by production phaseQuality check by

phaseProduction status

management

Popula-tion

Informa-tion

support

Sample design support

ED and map

support

Production support

Production process pool

Statistical business

knowledge sharing

Meta-data use & refer-

ence

Help desk

Statistical in-formation sharing

Specify the definitions and roles of business processes by phaseMetadata use and reference for the entire statistical businessQuality management at all times

Improvement

Composition of the KSBPM

Governance1

Production management2

Production support3

Statistical metadata4

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Establishment of the KSBPM3

[G] Statistical Policy Management [G4] Policy Support by Statistics

G4.1Preliminary evaluation

G4.2Practical evaluation

G4.3Tabulation of evaluation results and Reporting

[G1] Statistical Demand Management G1.1

Demand Management

G1.2Development and improvement of national statistics

G1.3Human resources management

[G3] Statistical Quality Control

G3.1Regular quality evaluation

G3.2Self quality evaluation

G3.3Occasional quality evaluation

G3.4Quality managementconsulting

[G2] Statistical Coordination G2.1Designate agencies

G2.2Cancel designated agencies

G2.3Designate statistics

G2.4Change designatedstatistics

G2.5Cancel the designation of designated statisticsG2.6Approve the production of statistics

G2.7Approve the change in the production of statisticsG2.8Approval the stop of statistical production 협의 )

G2.9Cancel the approval of productionG2.10Demand the improvementof statistical work

G2.11Prevent the redundancyand repetitionG2.12Coordinate survey items

[G5] Statistical Records ManagementG5.2Classify records that should be managed

G5.3Share records information

[G6] Statistical Production Process MonitoringG6.1Monitoring and policy-related consulting

G6.2Notify and check results

G5.1Receive records that should be managed

[Q] Statistical Production Quality Assessment Support[Q1] Self Assessment by Statistical Production Process

Q1.1Refer to production guideline

Q1.2Refer to the quality requirements

Q1.3Check the quality components step by step

Q1.4Check the quality after the completion of production

[S] Statistical Production Data Support

[S2] Sampling Data SupplyS2.1Ask for sample design supportS2.2Ask for sampling supportS2.3Investigate the support

S2.4Provide design andsampling

[K] Shared Info. Service

[P] Statistical Production Process Pool

S2.5Manage user feedback

[P1] Plan & Specify NeedsP1.1Specify needs

P1.3Establish statistical concepts

P1.5Draw up budget

P1.2Consult & confirm needs

P1.4Establish output objectives

P1.6Make production plan

[P2] DesignP2.1Design outputsP2.2Design variable descriptions

P2.3Design frameP2.4Design data collectionmethodology

P2.5Design sample methodologyP2.6Design statistical processing methodology

P2.7Design workflow

[P3] BuildP3.1Build data collec-tion instrument

P3.2Configure work-flows

P3.3Test production system

P3.4Test statistical business process

P3.5Finalize production system

[P4] CollectP4.1Select sampleP4.2Set up collection

P4.3Run collectionP4.4Finalize collection

[P5] ProcessP5.1Integrate data

P5.2Classify & codeP5.3Validate & supplementP5.4Impute

P5.5Derive new vari-ables & statistical unitsP5.6Calculate weightsP5.7Calculate aggregatesP5.8Finalized data files

[P6] AnalyzeP6.1Prepare draft out-put

P6.2Validate outputs

P6.4Apply disclosure controlP6.5Finalize outputs

P6.3Scrutinize & explain

[P7] DisseminateP7.1Update output system

P7.2Produce dissemination products

P7.3Manage release of dissemination productsP7.4Promote dissemination productsP7.5Manage user support

[P8] ArchiveP8.1Define archive rulesP8.2Manage archive repository

P8.3Preserve data and associated meta-dataP8.4Dispose of data & associated meta-data

[P9] EvaluateP9.1Decide checklist

P9.2Conduct evaluation

P9.3Derive challenges and make action plan

[K1] Statistical Knowledge Mgn’tK1.1Query & use knowledge K1.2Register, modify & delete knowledgeK1.3Investigate the registration, modificationand deletion of knowledgeK1.4Manage knowledge maps

[K2] Metadata ReferenceK2.1Statistical metadata reference

[K3] Help desk

K3.4Deal with requestsK3.5Ask for additional handlingK3.6Feedback

K3.1Query & use existing informationK3.2Receive new entriesK3.3Investigate reception details

[S1] Population Data SupplyS1.1Ask for populationinformationS1.2Investigate the supportof information

S1.3Support population informationS1.4Manage user feedback

[S3] Enumeration Districts Data SupplyS3.1Ask for support

S3.2Investigate the support

S3.3Provide information

S3.4Manageuser feedback

KSBPM Framework

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Characteristics of the KSBPM4Major characteristics of the KSBPMDerivation 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 qualityDerivation 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

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

Expectation effects of the KSBPMOrganic 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

Strengthen the sharing of associated knowledge and information

• Strengthen the sharing of associated knowledge and information to positively reflect opinions of statistical users

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

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Ⅲ GSIS

1. Purpose2. System Architecture

Purpose of GSIS1

Direction

Objective

System

A single window of Statistical business(Collaboration)

Reasonable statisticaladministration

(Governance)

Improving the reliability of national

statistics using metadata (Trust)

Standard process-based

Production with lowcost and high

efficiency (Quality)

Collaboration among producers, and customized servicesCommunication and knowledge transfer between the KOSTAT and production agenciesConsolidated account for different type of users

Link for the efficiency of approval managementSystem-based quality managementIntegrated history management to reduce workload of production agencies

Standardization of terms and processesManage statistical outputs step by stepProvision of statistical production standards by using metadata

Standardization of processesIntegrated system for the maximization of business efficiencyAutomatic business from questionnaire design to data transfer

Collaboration Portal Governance Integrated Metadata Management

GenericStatistical Production

Generic Statistical Information System ArchitectureUsers Generic Statistical Information System

Governance system

Common service-based system

Statistical collaboration

KOSTAT systems

Survey system(CAPI, CATI, ICR)

International organizations

Production agencies

Outside systems

Integrated login

Knowledge management

Communication

Support for production

Help desk

DB linkage

RMI

Integrated Metadata Management System

Link with the classification system of national statistics

Statisticians

Contract-based production agencies

Enumerators/

Survey managers

Academia/Research institutes

The general public

Mobile applicatio

n

Demand managementCoordination management

Quality management

Inspection management

Policy consulting

Statistical metadata Business reference metadata Standardization metadata

Mobile channels

Microdata archive

PDA UMPC

Survey design

Data collection

Data processing

Data dissemination

and management

Support for statistical

quality Evaluation

Integrated information link system

SecurityBackup

KPI management

History management

Support for common services

Generic Statistical Production SystemKOSIS

MDSS

Integrated Administrative Data Management System

Population System(establishments/

enterprises)

e-National Indicators

Statistical Metadata System

Statistical DW System

Linking system

Web services

System Architecture2

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Ⅳ Plans

1. Plans by Year2. Expectation Effects

2011 2012 2013

Integrate statistical policies (Demand, approval and quality)

Build the model statistical sys-tem (30 agencies)- Statistics Korea (1), Ministry of Public Ad-

ministration 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 sys-temExpand the statistical system(Other statistical agencies)Expand the functions of quality managementBuild a system for data sharing and linkage among agenciesSupport a specialized function of respective agencies

※ Information Strategy Planning (ISP) (2010)

Phase 1 Phase 2 Phase 3

Expand the generic statistical information system

Establish the infrastructure for the generic statistical information system

Strengthen the generic statis-tical information system

Action Plans by Year Plans by Year1

Qualitative effect

Quantitative effect

Efficient statistical activities via the standardized processes (Survey planning, dissemination and data management)

Budget reduction and common use of the statistical production system

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)

Expectation Effects2

Chanil Seo Director Informatics Planning Division Phone: 82.42.481.2377 Fax : 82.42.481.2474 E-mail: charlie88@korea.kr