GSBPM and GSIM in Statistics Norway

Prepared by Rune Gløersen and Jenny Linnerud

MSIS, Dublin14-16 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 NorwaysBusiness process model

5

Specify needs

1

Develop and design

2

Build

3

Collect

4

Process

5

Analyse

6

Disseminate

7

Consult and confirm need

1.2

Check dataavailability

1.4

Establish output objectives

1.3

Prepare business case

1.5

Prepare data for dissemination

database7.1

Produce product

7.2

Release and promote product

7.3

Classify and code

5.1

Micro-edit

5.2

Macro-control

5.3

Impute for partial non-response

5.4

Interpret and explain

statistics6.4

Establish frame and registers, select sample

4.1

Set up collection

4.2

Run collection

4.3

Finalise collection

4.4

Outputs

2.1

Data collection methodology

2.3

Process and analysis

methodology 2.4

Production system

2.5

Integrate production system with

other systems3.2

Test production system

3.3

Finalise production

system 3.4

Acquire domain intelligence

6.1

Produce statistics

6.2

Prepare statistics for dissemination

6.5

Finalise content

6.6

Frame, register and sample

methodology 2.2

Determine need for information

1.1

Manage customer queries

7.4

Calculate weights and derive

new variables5.5

Quality assure statistics

6.3

Build and enhance process

components3.1

Statistics NorwaysBusiness 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

Statistics NorwaysBusiness process model

Complete documentation on our Intranet

GSBPM in Statistics NorwayStreamlining Statistics Production

“Projectplanning”:- Jira

Document centers:Confluence (Trac, Wiki)

Windows-server

SIV/SIL

Blaise

Altinn

(Idun,

Kostra)

SFU

FDM

Norsamu

(Trekkbas)

Telefinn

SMIE

SERES

Presys

ISEE

ISEE Driller

Verify

SELEKT

ssb.no- statistic register

-Google analytics

Stat. Bank

ProduktregisterMetadataportals:

- Vardok- Datadok- Stabas

- ssb.no (About statistics)

Stat. population registers:

- National register

- The Central Coordinating Register for Legal Entities

- GAB – Landed property, Address, Dwelling (map)

Java

SAS

Oracle

Fame

Service Manager

(Helpdesk, OTRS)

LDA-app

MS OfficeSmartDraw

ArcGISWebsakSPSSAdobe

X12-Arima

Tau-Argus

Mu-Argus

SAS Insight

Categorising systems

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 GSIMIntroduction 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.

12

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 Infrastructureto 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 processto be supported by RIM?

RIM Data DescriptionsGSIM 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 Glossaryblue – 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.

subject area, theme

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.

cell