Transcript GSBPM and GSIM in Statistics Norway Prepared by Rune Gløersen and Jenny Linnerud MSIS, Dublin 14-16 April 2014

GSBPM and GSIM in Statistics Norway
Prepared by Rune Gløersen and Jenny Linnerud
MSIS, Dublin
14-16 April 2014
The GSBPM
Why do we need the GSBPM?
• To define and describe statistical processes in a coherent
way
• To compare and benchmark processes within and between
organisations
• To make better decisions on development of production
systems
• To optimize organisation and allocation of resources
4
Statistics Norways
Business process model
5
Statistics Norways
Business process model
Specify
needs
Develop and
design
Build
Collect
Process
1
2
3
4
5
Determine need
for information
Outputs
1.1
Consult and
confirm need
Analyse
Disseminate
6
7
Prepare data for
dissemination
database
Build and
enhance process
components
Classify
and code
Acquire domain
intelligence
2.1
3.1
Establish frame
and registers,
select sample
4.1
5.1
6.1
Frame, register
and sample
methodology
Integrate production
system with
other systems
Set up
collection
Micro-edit
Produce
statistics
Produce
product
2.2
3.2
4.2
5.2
6.2
7.2
Establish output
objectives
Data collection
methodology
Test production
system
Run collection
Macro-control
Quality assure
statistics
Release and
promote product
1.3
2.3
4.3
5.3
6.3
7.3
Check data
availability
Process and
analysis
methodology
Finalise
collection
Impute for partial
non-response
Manage
customer queries
4.4
5.4
Interpret
and explain
statistics
1.2
1.4
2.4
Prepare
business case
Production
system
1.5
2.5
3.3
Finalise
production
system
3.4
Calculate weights
and derive
new variables
5.5
6.4
Prepare statistics
for dissemination
6.5
Finalise
content
6.6
7.1
7.4
Statistics Norways
Business process model
• Was mapped against GSBPM 4 in the CORA
project
• Slightly different on detailed level within Build
• Some processes on detailed level placed
differently within Process and Analyse
• Was different with regards to archive as an
overarching process, which has been better
aligned with GSBPM 5
Complete documentation on our Intranet
GSBPM in Statistics Norway
Streamlining Statistics Production
Categorising systems
Java
SAS
Oracle
Fame
SIV/SIL
Blaise
Altinn
(Idun,
Kostra)
SFU
ssb.no
FDM
ISEE
- statistic register
Norsamu
ISEE Driller
-Google analytics
(Trekkbas)
Verify
Telefinn
SMIE
SERES
Stat. Bank
SELEKT
X12-Arima
Tau-Argus
Mu-Argus
Presys
SAS Insight
Service Manager
(Helpdesk, OTRS)
Stat. population registers:
“Projectplanning”:
- Jira
Document centers:
Confluence (Trac, Wiki)
Windows-server
- National register
Produktregister
Metadataportals:
- The Central Coordinating
- Vardok
Register for Legal Entities
- Datadok
- GAB – Landed property,
- Stabas
- ssb.no (About statistics) Address, Dwelling (map)
LDA-app
MS Office
SmartDraw
ArcGIS
Websak
SPSS
Adobe
Summary
• planning new statistics
• prioritizing new projects (portfolio management)
• improve existing work processes in statistical production
• reducing portfolio of IT-systems
• reducing risk
• making a more complete business architecture
• easier training and integration of staff.
11
Introduction to GSIM
We need consistent information
• Modernisation of statistics requires:
– reuse and sharing of methods, components, processes
and data repositories
– definition of a shared “plug-and-play” modular
component architecture
• The Generic Statistical Business Process Model
(GSBPM) will help determine which components
are required.
• GSIM will help to specify the interfaces.
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GSIM and GSBPM
• GSIM describes the information objects and flows
within the statistical business process.
GSIM in Statistics Norway - Vision
META DATA
GSIM in Statistics Norway - Vision
GSIM should lead to:
• A foundation for standardised statistical metadata
use throughout systems
• A standardised framework for consistent and
coherent design of statistical production
• Increased sharing of system components
Remote Access Infrastructure
to Register Data (RAIRD)
Statistics Norway and the Norwegian Social Science Data
Services (NSD) aim to establish
• a national research infrastructure
• providing easy access to large amounts of registerbased
statistical data
• managing statistical confidentiality
• protecting the integrity of the data subjects.
The work is funded by the Research Council of Norway. See:
www.raird.no
RAIRD Information Model (RIM)
Based on GSIM v1.1
• Design principles
• Information objects
New information objects for users (producers, administrators
and researchers)
Less information objects for details of the official production of
statistics
RAIRD continues out 2017
Research production process
to be supported by RIM?
RIM Data Descriptions
GSIM information objects
• Input Unit Data and Metadata/Event History
Data Resource
• Analysis Data Sets
• Disclosure Control
• Final Product
• Themes and Subject Fields
• Classifications, Concepts, Variables, etc.
GSIM Glossary
blue – not in RIM, yellow – in RIM
Statistical
Support
Program
Business
A program which is not
related to the post-design
cyclic production of statistical
products, but is necessary to
support cyclical production.
This type of program will include such functions as
metadata management, data management,
methodological research, and design functions. These
programs correspond to the horizontal functions shown
in the GSBPM, as well as programs to create new or
change existing Statistical Programs.
Subject
Field
Concepts
One or more Concept
Systems used for the
grouping of Concepts and
Categories for the production
of statistics.
A Subject Field is a field of special knowledge under
which a set of Concepts and their Designations is used.
For example, labour market, environmental expenditure,
tourism, etc.
Unit
Concepts
The object of interest in a
Business Process
Here are 3 examples - 1. Individual US person (i.e.,
Arofan Gregory, Dan Gillman, Barack Obama, etc.) 2.
Individual US computer companies (i.e., Microsoft,
Apple, IBM, etc.) 3. Individual US universities (i.e., Johns
Hopkins, University of Maryland, Yale, etc.)
Unit Data
Point
Structures A placeholder (or cell) for the
value of an Instance Variable
with respect to a Unit.
This placeholder may point to multiple values
representing different versions of the data. Values are
only distinguished on the basis of quality, date/time of
measurement or calculation, status, etc. This is handled
through the mechanisms provided by the Datum
information object.
subject
area,
theme
cell