Streamlining statistical production PGSC meeting 18-19 October 2012 Tirana

New long term orientations of the ESS New production method of statistics – the VISION 2017 European Statistics Code of Practice 2005 and 2011 New European Statistical Law 2009 and 2012 Quality Assurance Framework Communication 211 “Towards robust quality management for European Statistics” in 2011 (preventive quality management, revision of the Statistical Law) Commitments on Confidence in Statistics

The VISION and the CoP Institutional environment (who?) Statistical output (what?) Statistical processes (how?) How? What?

Current situation – “Stovepipe approach”

Weaknesses Current situation Weakness User needs are defined in an isolated manner As a result, data collections are isolated as well Regulations are made separately by statistical domain Separate data transmissions from NSIs to Eurostat Variety of tools for data validation and analysis Weakness no cross-checks for synergies; Inconsistencies; Single-purpose use of data Differences in concepts, breakdowns, reference periods… Different channels and formats, difficult follow-up; inconsistencies in metadata; Inefficiency, lack of interoperability, difficult quality control

Future business model (1)

Future business model (2) Situation in the future IT side

Principles of ESS joint strategy User needs are at the heart – increase availability of statistics (globalisation and multi-dimensional) Use separate strategic approaches for “WHAT” (products, services, priorities) and “HOW” (the vision) Reduce costs while maintaining data quality Develop close partnership between all the 28 NSIs (MS, Estat) through appropriate dialogue and networks Reuse of statistics from other sources (web, administrative sources) Integration and standardisation of methods and tools Legislation needs to focus on large domains and be output oriented Develop a strategic human resource policy (staff skills, common training)

Major aspects of implementation Horizontal integration: production of data according to the responding unit (e.g. household, enterprise), not by domain – no domain is specific! Vertical integration: joint structures, networks (ESSnets) Standardisation: common tools for each step of the data production process (e.g. common classifications, common definition of variables, common validation rules) Use and combination of different data sources (survey data, administrative sources) Move from sending data (push mode) to retrieving data from data warehouses (pull mode)

Enterprise architecture ESS Vision Information Network Information store Process Modular Production Optimal cooperation

Information Process Network Information store Modular production Optimal cooperation 2. EGR 5. Reference Production Architecture 6. ESS validation 1. Web infrastructure 7. Admin sources 3. SIMstat 4. ESS data warehouses

ESS VIPs related to strategic directions

Examples of projects supporting the VISION ESS vision infrastructure projects – by project Use of administrative data (ADMIN) National accounts production system - services(NAPS-S) ESS data warehouses (price and transport statistics) (PRIX/TRANS) European System of business registers (ESBR) Single Market Statistics (SIMSTAT) Information Society – web infrastructure (ICT) Common data validation policy (VIPV) Census hub Framework legislation for business statistics Remote access to individual data

Examples of projects supporting the VISION Cross-cutting issues - technical Information models (defining information standards) Networks (improve network infrastructure) Data warehouses (develop a solution for storing, processing and redistributing shared data) Shared services (put in place a service oriented architecture for integrating and industrialising processes) Validation (design and maintain validation architecture) Cross-cutting issues - general Communciation Governance Human resources Financial resources Legal framework Implementation strategy Change management

ESS VIPs versus cross-cutting projects

Common data validation rules and tools Develop standard documentation for validation Develop standard formats for data Develop standard rules for data validation Describe the standard rules Validation at the most appropriate levels (closest to the data, the sooner the better) Develop generic IT tools

Census hub Use of SDMX as a standard Pull mode of data transmission Development of standard tools

Questions What measures can the NSI take / have the NSIs taken to streamline their production? What are the most promising projects (electronic reporting, standard tools, harmonised concepts, generic IT tools)? What are the obstacles to implement them? How can the obstacles be overcome? What kind of success stories do the NSIs can report about? What can Eurostat do to help?