Better data quality through global data and metadata sharing

Agne Bikauskaite and Håkan Linden

Eurostat

European Conference on Quality in Official Statistics (Q2014)Vienna, 3-5 June 2014

Outline

1. Context

2. A data sharing model

3. The necessary preconditions

4. Implementing Eurostat's data sharing strategy

5. Conclusions and outlook

Context

General objectives

•Reduce reporting burden on NSIs•More efficient use of resources in International Organisation (IO)•Ensure high quality and consistency of data of official statistics •Improve global data exchange and dissemination

A data sharing model

European statistics: From national to Eurostat

EU Member state

EU Member state

EU Member state

EU Member state

EU Member state

EU Member state

EU Member state

EU Member state

Data Validation Data Validation

EurostatEurostat

A data sharing model

EU countriesEU countriesOECD countries

(non-EU countries only)

OECD countries(non-EU countries

only)

Other countries (non-OECD countries

only)

Other countries (non-OECD countries

only)

Eurostat - ECB

Eurostat - ECB

OECDOECD

IMF, UN, WB, ILO, BIS, other IOs

IMF, UN, WB, ILO, BIS, other IOsU

SERS

USERS

Eurostat as international hub for European statistics

The necessary pre-conditions

• Internationally agreed technical and statistical standards

• Internationally agreed data structures

• Maintenance agreements

• Internationally agreed data validation

• Streamlined data exchange processes

It consists of technical and statistical standards, guidelines, an IT service infrastructure and IT tools.

SDMX provides •technical/statistical standards•new exchange modes (hubs) •clear rules and responsibilities

SDMX

ISO IS 17369

Statistical Data and Metadata Exchange(SDMX)

Organisation scheme

Concepts

Code lists

Concept Schemes

Provision Agreement

SDMX describes the data and metadata exchange

DSDs

maintainer SDMX Registry

Describing the data exchange

Who?

What?

When? Who?

Where?How?

What?

Cross-domain concepts and code listsCross-domain concepts and code lists

Statistical subject-matter domainsStatistical subject-matter domains

Metadata common vocabularyMetadata common vocabulary

Recommendations to harmonise implementations

Organisation 1 Organisation 2 Organisation 3

interoperability

Content-Oriented guidelines

• Code lists describe dimensions in data tables, giving a meaning to the data.

• Code lists are based on:

• official statistical classifications such as NACE, NUTS, ISCO, etc.

• The ESS and SDMX Content Oriented Guidelines

• domain specific codifications

• A standard code list is a code list already harmonised

• Standard code lists should be used all along the statistical business process: data design, collection, aggregation, dissemination, exchange, archiving.

Implementing Eurostat's data sharing strategyStandardisation of structural metadata

Implementing Eurostat's data sharing strategyRecommendations for the SCL creation

RECOMMENDED RULES ESS SDMX COMMENTS

Input: Official information ⱴ ⱴ  

Coding A-Z + 0-9 + - + _ A-Z + 0-9 + _ In SDMX “–“ (dash) is not allowed (to avoid confusion with operator "minus")

Codes starting with letter ⱴ ⱴ With some exceptions

Meaningful codingⱴ ⱴ Less homogeneity in coding in SDMX (due to

involvement of several different partners)

Aggregates are possible ⱴ ⱴ  

To be used all along the statistical business process

ⱴ ⱴ  

May be referenced by several statistical concepts

ⱴ ⱴ  

Based on clear guidelines ⱴ ⱴ  

Maintenance agency ⱴ ⱴ ESS: Eurostat Unit B5SDMX: Statistical Working Group (SWG)

Versioning system ⱴ ⱴ In future registries

Generic conceptⱴ ⱴ In SDMX is special CL for generic codes

In ESS generic codes are implemented in each SCL when it is needed

Implementing Eurostat's data sharing strategySDMX standards into ESS structural metadata

In purpose to improve quality of the data comparability and clarity is needed:

• To use identical SCLs in the ESS and in the SDMX• To transpose the SDMX guidelines into the ESS code lists• To adapt the ESS standard codes into the SDMX DSDs

Implementing Eurostat's data sharing strategyOverview of the ESS SCLs

• 504 ESS CLs • 194 ESS SCLs released in Ramon

•12 fully SDMX compliant•110 SDMX compliant (except Generic codes)

Implementing Eurostat's data sharing strategyStandardisation of Reference Metadata

WASTE (end of life vehicles, packaging, electronic waste)

WINE

FARM STRUCTURE

MIP STATISTICS

HICP/ Compliance monitoring EHIS (Education, health and social protection) R&D (CIS 2012) Annual crops

PRAG ESAW AES (Education, Science and Culture) LCI (Labour Cost Index) INFOSOC (Information Society) BUSINESS REGISTER

HICP LFS-Q, LFS-A EU-SILC FATS STS (Short Term Statistics) WASTE AEI (Pesticides) EDUCAT JVC (Job Vacancy Stats) PRODCOM EXTERNAL TRADE (3rd countries) COSAEA URBANREG R&D TOURISM PERMANENT CROPS CENSUS HOUSING PRICES HPS

Over 30 Eurostat domains are in various phases of ESS Reference metadata standardisation.

This concerns about 35% of all eligible Eurostat processes.

Implementing Eurostat's Reference metadata sharing strategy

Implementing Eurostat's data sharing strategy The Eurostat established methodology

Implementing Eurostat's data sharing strategyin ESS

GNI INT TRADE * CENSUS AVIATION(Gross National Income)

WATER R&D* FISHERIES *

JVS / LCI MARITIME ORCHARDS / PEST.

EGR IS TICPESTICIDES USE (Eurogroup register) (Trade in currency)

ESSPROS TEC WASTE

NATIONAL A/cs *STS(Short-term Statistics) BOP *

EDUCATION * FDI *

Over 20 Eurostat domains are in various phases of SDMX implementation.This concerns about 25% of all eligible Eurostat processes.

Job Vacancy Statistics )Labour Cost Index

(Trade by Enterprise Characteristic)

(social protection statistics)

Implementing Eurostat's data sharing strategyDevelopment of the technical infrastructure

Key components:

• SDMX Registries• The Euro-SDMX Registry• The Global SDMX Registry

• SDMX Reference Infrastructure (SDMX-RI)

Implementing Eurostat's data sharing strategyWhat is the EuroSDMX Registry(SER)?

• Eurostat's implementation of the SDMX Registry specifications as published by the SDMX initiative sdmx.org.

• Based on SDMX 2.1 (as published on April 2011) Also capable of importing and exporting SDMX 2.0 artefacts.

• Allows browsing, searching, editing and subscribing to artefacts.

• Advanced access control mechanism for distributed maintenance of artefacts controlling also their visibility.

Home pageHome page

Most recent itemsMost recent items

Access to the content of the

Registry by type

Access to the content of the

Registry by type

Access to the content

of the Registry text

search

Access to the content

of the Registry text

search

Access to the content of the Registry advanced search

Access to the content of the Registry advanced search

Conclusions

• International data co-operation improves the production of accurate, comparable and coherent statistics;

• SDMX promotes an incremental movement toward the data and metadata sharing model;

• The increasing use of SDMX based statistical standards improves the quality of the underlying statistical processes;

• The SDMX technical standards pave the ways for simplified exchange and dissemination processes helping to improve also timeliness and accessibility;

• Statistical integration needs to go hand-in-hand with technical integration and standardisation.

Outlook

• Much more global data and metadata sharing in the years to come;

• Common data validation and processing procedures are required (from structural validation to content information validation);

• Better metadata driven statistics production systems: the use of standards throughout the processes in combination with common metadata registries ;

• Better harmonised international reference metadata frameworks and templates;

• Broadening the scope of SDMX (versioning of codes, disabling of dimensions, other formats like CSV, flat files etc.);

• Interoperability between information models (GSIM, SDMX, DDI etc.).