+ All Categories
Home > Documents > The Adoption of METIS GSBPM in Statistics Denmark

The Adoption of METIS GSBPM in Statistics Denmark

Date post: 05-Jan-2016
Category:
Upload: march
View: 26 times
Download: 0 times
Share this document with a friend
Description:
The Adoption of METIS GSBPM in Statistics Denmark. Agenda. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned. Agenda. Background and context Working with business processes - PowerPoint PPT Presentation
22
The Adoption of METIS GSBPM in Statistics Denmark
Transcript
Page 1: The Adoption of METIS GSBPM in Statistics Denmark

The Adoption of METIS GSBPMin Statistics Denmark

Page 2: The Adoption of METIS GSBPM in Statistics Denmark

Agenda

1. Background and context

2. Working with business processes

3. An example of documentation

4. Results of process analysis

5. Metadata coverage

6. Lessons learned

Page 3: The Adoption of METIS GSBPM in Statistics Denmark

Agenda

1. Background and context

2. Working with business processes

3. An example of documentation

4. Results of process analysis

5. Metadata coverage

6. Lessons learned

Page 4: The Adoption of METIS GSBPM in Statistics Denmark

Working group on standardisation

1. Multi-annual corporate strategy as basis (”Strategy 2015”)

2. Working group, that refers to Board of Directors

3. METIS GSBPM adopted as common frame

4. Dual focus• Process analysis and documentation• Coverage of metadata systems

Page 5: The Adoption of METIS GSBPM in Statistics Denmark

2Design

1SpecifyNeeds

3Build

4Collect

5Process

6Analyse

7Disseminate

5.1Integrate data

5.4Impute

5.5Derive new variables & stat. units

5.2Classify &

code

5.3Validate &

edit

1.1Determine need for

information

1.4Identify

concepts & variables

1.5Check data availability

1.2Consult &

confirm need

1.3Establish

output objectives

2.1Design outputs

2.5Design stat. processing

methodology

2.6Design prod.

systems / workflows

2.3Design data collection

methodology

2.4 Design Frame & sample

methodology

3.1Build data collection

instrument

3.4Test

production systems

3.2Build or enhance

process comp.

3.3Configure workflows

4.1Select sample

4.4Finalize

collection

4.2Set up

collection

4.3Run collection

6.1Prepare draft

outputs

6.4Apply

disclosure control

6.5Finalize outputs

6.2Validate outputs

6.3Scrutinize &

explain

7.1Update output

systems

7.4Promote

dissemination products

7.2Produce

dissemination products

7.3Manage

release of dissem. prod.

7.5Manage user

support

8Archive

9Evaluate

8.1Define archive

rules

8.4Dispose of data

& assoc. metadata

8.2Manage archive

repository

8.3Preserve data & associated

metadata

9.1Gather

evaluation inputs

9.2Conduct

evaluation

9.3Agree action

plan

1.6Prepare

business case

3.5Test statistical

business process

3.6Finalize

production system

5.6Calculate weights

5.7Calculate

aggregates

5.8Finalize data

files

Quality management / Metadata Management

2.2Design

variable descriptions

Page 6: The Adoption of METIS GSBPM in Statistics Denmark

Reference document – ”SD’s METIS”

– METIS: confirmed standard for official statistical production

– Adopted by some of our peers

– Translation of document

– Approach for SD version

– Testing the extent to which the model apply to SD

– An ”SD METIS” would be a milestone for business process- and architectural maturity

– Necessary to move ahead according to our corporate objective of increasing standardisation

– Initial focus on phases 4-7

Page 7: The Adoption of METIS GSBPM in Statistics Denmark

Agenda

1. Background and context

2. Working with business processes

3. An example of documentation

4. Results of process analysis

5. Metadata coverage

6. Lessons learned

Page 8: The Adoption of METIS GSBPM in Statistics Denmark

Model/template for statistical business processes

– METIS level (“which phases do we open”?)

– Control-flow level (phases, input, output, time)

– Functional level (”who does what, and in what order?”)

– ”AS-IS” and/or ”TO-BE”

– BPMN: Standardized notation

– Collect ideas and convert them into action (standardisation, efficiency and quality)

– Form

• Workshop

• Facilitated by working group

• Ownership of results to the statistical team

• Needs a mandate!

Page 9: The Adoption of METIS GSBPM in Statistics Denmark

Selection of pilot cases• Social Statistics:

– Population register– Student register (register updates)

• Business Statistics– General account statistics (SBS)– Employment in construction industries– Retail Trade Index– Industrial commodity statistic– Farm Structure Survey– Car register and associated statistics– Use of ICT in enterprises

• Economic Statistics– Consumer price index– Foreign trade in services

• Sales and Marketing– Interview task: Yearly survey on safety– Key figures in housing (standardized product from SDs Customer Services Centre)

• User Services– Data collection-processes/-systems (XIS, CEMOS)

Page 10: The Adoption of METIS GSBPM in Statistics Denmark

Selection of cases in Business StatisticsDimension Values Cases

Frequency - Short term vs. - Structural statistics

- ECS - SBS

Standardised system (if any)

- Statistics in standardised systems vs. - Statistics in stand-alone systems

- ECS- SBS

Complexity - Simple vs. - Complex

- RTI - SBS

Type of Statistical Unit

- Statistics based on SBR vs. - Statistics with other units

- SBS - C-Reg

Method for error detection

- Micro-based error detection vs. - Macro-based error detection

- SBS- ECS

Coverage - Sample vs. - Cut-off vs. - Population

- ECS- ICS - FSS

Confidentiality scheme

- Positive confidentiality vs. - Negative confidentiality

- SBS- ICS

Cost - Statistics with high cost vs.- Statistics with low cost

- SBS- RTI

Stability - Few changes by each iteration vs.- Many changes by each iteration

- ECS- UIE

Maturity - Well established statistic in SD - New statistic in SD

- SBS- (RII)

”Type” - Primary statistic vs. - Derived statistic

- ICS - C-Reg

Page 11: The Adoption of METIS GSBPM in Statistics Denmark

Agenda

1. Background and context

2. Working with business processes

3. An example of documentation

4. Results of process analysis

5. Metadata coverage

6. Lessons learned

Page 12: The Adoption of METIS GSBPM in Statistics Denmark

Example: METIS level2

Design1

Behov3

Udvikl4

Indsaml5

Behandl6

Analysér7

Formidl

5.1Integrer

data

5.4Imputér

manglende data

5.5Afled nye

stat. enheder og variable

5.2Kod data

5.3Gennemgå,

fejlsøg og ret data

1.1Identificér

brugerbehov

1.4IdentificérBegreber

1.5Undersøg datakilder

1.2Konsultér og

bekræft behov

1.3Skitsér

output/tabeller

2.1Design output

2.5Design

databehand-lingsmetode

2.6Design prod. system; krav-specifikation

2.3Design data-indsamlings-

metode

2.4 Designudtræksramme og stikprøve-

metode

3.1Udvikl data-indsamlings-instrument

3.4Test

systemet

3.2Udvikl

produktions-system

3.3Definér

workflows

4.1Udvælg

stikprøve

4.4Afslut data-indsamling

4.2Forbered data-

indsamling

4.3Gennemfør

data-indsamling

6.1Forbered statistik-produkt

6.4Applicér statistisk

fortrolighed

6.5Afslut

analyse

6.2Kvalitetssikr

Statistik-produkt

6.3Gransk og

forklar

7.1Opdatér data i

formidlings-systemer

7.4Markedsfør

statistik-produkt

7.2Udarbejd statistik-produkt

7.3Håndtér

udgivelsen

7.5Håndtér bruger-support

8Arkivér

9Evaluér

8.1Definér

Arkiverings-regler

8.4Aflevér data og metadata

8.2Opsaml / gem

rådata

8.3Gem fejlsøgte

data og metadata

9.1Indsaml data /

input til evalueringen

9.2Gennemfør evaluering

9.3Beslut

handlingsplan

1.6Start

projekt

3.5Gennemfør

pilot-test

3.6Sæt system

i drift

5.6Beregnvægte

5.7Beregn

aggregater

5.8Færdiggør

aggregerede datasæt

Kvalitetsstyring / Håndtering af metadata

2.2Beskriv variable

Page 13: The Adoption of METIS GSBPM in Statistics Denmark

Example: Control flow level

Trigger

Phases

Input

• Regulations

• Data

• etc.

Output

• Intermediate

• Final

Time

Page 14: The Adoption of METIS GSBPM in Statistics Denmark

Example: Functional level

Who does what

Start condition

End condition

Note that…

Page 15: The Adoption of METIS GSBPM in Statistics Denmark

Agenda

1. Background and context

2. Working with business processes

3. An example of documentation

4. Results of process analysis

5. Metadata coverage

6. Lessons learned

Page 16: The Adoption of METIS GSBPM in Statistics Denmark

Results of process analysis (an overview)• Focus on processes is useful and has immediate effect in

some cases• Improvements for statistical teams

– Quality (documentation, new quality measures, etc.)– Standardisation (Use of standardised systems)– Efficiency (Eliminate manual processes)

• Improvements in communication– Many project managers regarding digitalisation– Coordinator function

• Improvements in efficiency for data collection– Focus on areas of responsibility

• Huge difference in degree of standardisation– Dissemination– Data collection– Data processing

Page 17: The Adoption of METIS GSBPM in Statistics Denmark

Agenda

1. Background and context

2. Working with business processes

3. An example of documentation

4. Results of process analysis

5. Metadata coverage

6. Lessons learned

Page 18: The Adoption of METIS GSBPM in Statistics Denmark

Metadata coverage

5. Process4. Collect 6. Analyse 7. Disseminate

FTP

Business-to-Business

Web-services

Virk.dk

Papir forms

Classify and code

Review, validate and

edit

Integrate Data

Impute

Calulate weigths and aggregate

Statistical database 1

Statistical database n

DataWare-houseIDV

Statistical database

Dst.dk

Statistics bank

PUK

PX Publ

CRM

Metadata og documentation

CEMOS

XIS2

IBS

Scan

Begrebsdatabase

Klassifikationer

Varedeklarationer

???

Højkvalitet

TimesTimes

Page 19: The Adoption of METIS GSBPM in Statistics Denmark

Metadata coverage

• Dissemination phase is very well covered

• Although dissemination phase is covered by four different applications the overlap is very limited

• The vision for the future is to create a single metadata system

• The data model should be based on three data stages (raw data, micro data, macro data)

Page 20: The Adoption of METIS GSBPM in Statistics Denmark

Metadata coverage

5. Process4. Collect 6. Analyse 7. Disseminate

FTP

Bussiness-to-business

Web-services

Virk.dk

Paper forms

Datacollection system

Classify and code (std)

Review, validate and

edit (std)

Integrate Data (std)

Impute (std)

Calulate weigths and aggregate

(std)

Statistical database 1

Statistical database n

DataWare-houseIDV

Statistical database

Dst.dk

Statistics bank

Statistics Denmark Metadatasystem

PUK

PX Publ

CRM

Metadata og documentation

Inputdataarchive

Page 21: The Adoption of METIS GSBPM in Statistics Denmark

Agenda

1. Background and context

2. Working with business processes

3. An example of documentation

4. Results of process analysis

5. Metadata coverage

6. Lessons learned

Page 22: The Adoption of METIS GSBPM in Statistics Denmark

Lessons learned

• Planning a strategy for further development is better using GSBPM

• Identify areas of interest for improvement initiatives.

• Major challenges regarding steps where data is processed

• Further standardization of methods is necessary

• A clearer view of the different need for metadata and documentation

• A better overview of the strong and the weak areas of our metadata applications


Recommended