Harry Goossens - ESSnet Coordinator ESSnet on microdata linking and data warehousing in statistical production The statistical data warehouse: a central datahub, integrating new datasources and statistical output Harry Goossens - ESSnet Coordinator Head Data Service Centre at Statistics Netherlands hct.goossens@cbs.nl UNECE - Seminar on New Frontiers for Data Collection Geneva, 31 October - 2 November 2012 ESS-net DWH

Explaining the statistical data warehouse (S-DWH) Content Background ESS-net Challenges Explaining the statistical data warehouse (S-DWH) Elements of the S-DWH Business architecture GSBPM mapping Meta data ESS-net DWH

ESSnet on microdata linking and data warehousing in statistical production ESS-net DWH

Statistics Netherlands (CBS) Co-partners: ESSnet Partnership ESS-net coordinator: Statistics Netherlands (CBS) Co-partners: Estonia, Italy, Lithuania, Portugal, Sweden, UK Starting date: 4 October 2010 SGA 1: first year, till 3 October 2011 SGA 2: last 2 years, till 3 October 2013 ESS-net DWH

General Objectives ESSnet DWH Provide assistance in: the development and implementation of a maximum efficient statistical process for business and trade statistics, independent of any (technical) specific architecture Results in daily statistical practice: increase the efficiency of data processing in statistical production systems maximize the reuse of already collected data a 'data warehouse' approach to statistics ESS-net DWH

Rapidly changing demand for information: The Challenges Decrease of costs & administrative burden versus increase of efficiency & flexibility Rapidly changing demand for information: growing need for more information on more topics decreasing lifecycle of policymakers, quicker delivery Disclosure of all new data sources coming from global use of modern technology Make optimal use of all available data sources (existing & new) ESS-net DWH

The Statistical Data Warehouse A central data hub to connect and integrate all available data sources, supporting statistical production AND data collection processes by providing: a detailed and correct overview/insight of all available data sources a framework for adequate data governance, including metadata management, confidentiality aspects and data authorisation flexible data storage and data exchange between processes access to registers sampling frames (BR, etc); A central ‘statistical data store’ for managing all available data of interest, regardles of its source, enabling the NSI to produce necessary information (= statistics !) and to (re)use available data to create new data / new outputs. ESS-net DWH

Data extracts Selected Dataset sample Data extracts Working data Rules for generating samples etc. Data extracts Selected sample Dataset Data extracts Selected sample Dataset Working data Aggregate Statistics Staging area Admin data source Dataset Aggregate Statistics Microdata Admin data source Backbones (BR eg.) BB snapshots Data extracts Rules for updating BB Input reference frame Input data Storage, combination Outputs ESS-net DWH

rules for updating the sources for the DWH Explaining the S-DWH A system or set of integrated systems, designed to handle the processing of statistical data in the production of statistics, comprimising: technical facilities for storing and processing data, receiving data in and producing outputs in a flexible way rules for updating the sources for the DWH definitions necessary to achieve those samples / sources The S-DWH is a concept that provides an architectural model of the statistical data flow, from data collection to statistical output ESS-net DWH

The S-DWH Business Architecture Conceptualisation of how to build up a S-DWH A common model for the total statistical process and data flow Provide optimal organisation of all structured data, enabling re-use, creation of new data etc. 4 Layers, covering all statistical activities Sources Integration Interpretation & Analysis Data Access / Output ESS-net DWH

The layered architecture of the S-DWH, with focus on the data sources used in each layer ESS-net DWH 10 10

Mapping the S-DWH on the GSBPM Use the GSBPM as common language to identify and locate the various phases on the 4 S-DWH layers ESS-net DWH

Metadata is vital in the governance, satisfying 2 essential needs: Managing the S-DWH The S-DWH is a logically coherent central data store, not necessarily one single physical unit. Metadata is vital in the governance, satisfying 2 essential needs: to guide statisticians in processing and controlling the statistical data to inform users by giving insight in the exact meaning of the statistical data The vertical metadata layer enables to search all (meta)data in the 4 layers and, if permitted, give access to the data. ESS-net DWH

Interpretation and Data Analysis Layer Meta data layer Metadata Layer Data Access Layer Interpretation and Data Analysis Layer Integration Layer Source Layer ESS-net DWH

14 Meta data - the DNA of the S-DWH Framework: General meta data definitions Meta data for the S-DWH Use of meta data models Meta data standards & norms Meta data quality & governance Categories & subsets Minimum requirements ESS-net DWH 14 14

Internal rules Guidelines S-DWH meta data requirements Subsets Standards & Norms More … Data models Authorization metadata Technical metadata Quality metadata Process metadata Statistical metadata ISO 11179 Internal rules Guidelines Mata data model S-DWH Gatekeeper ESS-net DWH 15 15

Organisational aspects Implementation of a S-DWH has huge organisational impact: It means: moving from single operations to integrated, generic processes It needs: a redesign of the statistical process It asks: new IT systems, tools, high investments It is: a new way of working Only changing systems will not do the trick, changing people is the key to success ESS-net DWH

ESSnet on data warehousing Thank you ! ESS-net DWH 17