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The Adoption of METIS GSBPMin 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
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
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
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
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
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
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!
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)
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
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
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
Example: Control flow level
Trigger
Phases
Input
• Regulations
• Data
• etc.
Output
• Intermediate
• Final
Time
Example: Functional level
Who does what
Start condition
End condition
Note that…
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
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
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
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
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)
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
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
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