1

1

Improving interoperability in StatisticsImproving interoperability in StatisticsSome considerations on the impact of SDMXSome considerations on the impact of SDMX

59th Plenary of the CES

Geneva, 14 June 2011

Rune GløersenIT DirectorStatistics Norway

2

Contents

• The characteristics of processes and data at NSIs

• Applicable standards for various business processes

• The preconditions for increased interoperability

• A top-down approach to further standardisation

• SDMX as part of the industrialisation of statistics

3

3

GSBPM – leaving stove pipes

4

4

Data archiving

5

Specifyneeds

Design Build Evaluate

Quality Management/Metadata Management

Process stages and data archiving

Data archiving spans the 4 main business processes,and comprises 4 steady states of the data life cycle

6

Dissemination of aggregated statistics using SDMX

SDMXConversion

SDMX Common Architecture

Can (somewhat) easily be streamlined

7

Dissemination of any statistical data using SDMX

SDMXConv

SDMX Common Architecture

Requires a paramount strategy

8

Specifyneeds

Design Build Evaluate

Quality Management/Metadata Management

Adopting standards

DDISDMX

?

9

The diversity of users, needs and data flows

Public

Domain specific

Research

Questionnaires

Data transfersRegisters

Common high level models, vocabulary etc

10

Challenges

• The high-level decision to use SDMX for the exchange of statistical data; how should this be envisaged?

– The role of the standardisation experts, the IT experts, the subject domain experts and the top management

• SDMX implementation is strategic, but is regarded as technical– The importance and impact of the Information Model and the Metadata

Common Vocabulary

• Choosing standards; DDI, SDMX, DSPL etc.– No standard is likely to fit all purposes.– Will a common high-level information model contribute to easier

implementation of standards?– Can a high-level information model bridge different standards?

• Provide well defined interfaces, or develop software to hide the challenges?

– Common requirements for the quality of software

11

Improved interoperability Some trends

Organisationalinteroperability

Technologicalinteroperability

Semanticalinteroperability

12

Maturity growth in e-Government

OrganisationalInteroperability

SemanticalInteroperabilitySource: www.semicolon.no

Analytical Framework for e-Government Interoperability

SharingKnowledge

Aligning WorkProcesses

Joining ValueCreation

AligningStrategies

Bilateral data exchange, semi automated,Technical specifications and standards

Share best practises, metadata specifications,Set up standards for technical systems and dataexchange

Common information models, process models and service catalogues, shared development costs

Legislation,Whatever

13

Common GenericIndustrial Statistics

GSBPM GSIM

Methods Technology

Statistical Concepts Information Concepts

Statistical HowTo Production HowTo

conc

eptu

alpr

actic

alIndustrializing Statistics

De-coupling content and technical standardisation

14

Conclusions

• Standardisation is not a goal in itself; any standardisation effort must be based on well defined business cases. Success requires a top-down, management driven approach.

• The adoption of SDMX must be aligned with the on-going process oriented developments among NSI’s.

• Utilize the benefits of SDMX for the exchange of aggregated data, improve the international harmonisation of requirements, and simplify implementation whenever possible.

• Agreeing on common high-level models, creates an opportunity for flexible, targeted and effective solutions on the detailed level, still harmonised within a standardised framework

• The statistical community should act as an industry, not only as individuals, in order to increase commercial attention to the industry of statistics

15

Actions ?

• (Continue to) set up a common reference framework comprising the objectives of harmonisation/standardisation

– Appreciate clustered initiatives, but require precise description on the contributions to the overall objectives

– Better prioritisation among projects; it is unlikely that we can achieve all goals at once

– Improve governance and coordination– Let the drivers drive– Evaluate

• Decide where to provide for best practises, architectures, standards and tools/shared software components

• Improve the strategy on how to coordinate process developments with subject matter/domain specific developments

• Provide for innovation