Business architecture ESTP course on Structural Business Statistics 17 October 2012 – Luxembourg Wim Kloek Unit B1 Quality, methodology and research Eurostat

Vision for the next decade Meet new user needs and improve quality with less resources and with less burden Reference: COM(2009) 404

Two questions on the vision Your opinion Suggestions for implementation

Some ideas Integrate processes Use administrative sources Data warehouse Use of new information technologies Share standard tools Collaborative methodological development

Advantages of integration Less double work More profitable to share IT-development and to invest in human resources Higher level of harmonisation between countries Higher coherence over domains (new output)

Examples of integration The European Enterprise group register European sampling Coordination of sampling (to spread the response burden or to match with variables in an other domain) Standardisation of data and metadata description (GSIM, SDMX) Standardisation of process description (GSBPM) Establishment and spread of best practices

Disadvantages of integration Difficult to manage; bounded rationality Not necessarily in line with subsidiarity (e.g. use of administrative sources) Less flexibility Not all official statistics via NSI 百花運動

Some observations on the Generic Statistical Business Process Model (GSBPM) Only core business processes are covered, not policy, management or other supporting processes GSBPM has a lot of detail, but still not sufficient for integration or common development No strict order of steps GSBPM assists to describe processes in a standardised way, but gives no direction to development

Example: validation Current process: validation by Member State, validation by Eurostat, feedback Double work, time consuming feedback mechanisms Sharing of validation rules Goal: global coherence of data Requires: common revision strategy, but also common understanding of the integrated process (tasks, responsibilities)

Administrative sources advantages No response burden Rich in detail (e.g. region, micro data)

Administrative sources how to use Direct replacement of survey observations Model (use correlation) Integrate in the statistical business register (better stratification of samples) Circumstantial validation and interpretation

Administrative sources disadvantages Continuity of the source Control over definitions (risk of data driven definitions) Incentives for over/under reporting Control over the executive practices Statistical independence from policy National in character: comparability

Administrative sources: metadata/output How to assess data quality for statistical data based on a mix of survey data and administrative data How to explain to users - the way the data was produced - the quality of the data

Data warehouse: requirements Why not put all information in one database? Links between the units Metadata, standardisation Replication requires traceability of changes Revision strategy How do we validate imputations, model estimations? Linking national warehouses?

European Statistical System ESS Member States + Eurostat Establishing statistical legislation Steer the implementation of the vision Some resources (money) for collaborative work System?

Conclusions Ambitious vision Implementation Challenges Standardisation and integration of processes (GSIM, GSBPM) Use of administrative sources Sharing Challenges Managing complexity Managing change Financial and legal restrictions