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

ESS-net DWH 1  Background ESS-net  Challenges  Explaining the statistical data warehouse (S-DWH)  Elements of the S-DWH - Business architecture - GSBPM mapping  Meta data  Organisational aspects Content

ESS-net DWH 2 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 ESSnet Partnership

ESS-net DWH 3 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 General Objectives ESSnet DWH

ESS-net DWH 4 Conclusions  Data Warehousing in statistics is ‘hot’  Metadata is found important….. but also often neglected !  S-DWH is very difficult to compare with common commercial DWH  Visiting NSIs has proven very effective for gathering information AND for sharing knowledge and expertise  Great need for knowledge & expertise Start SGA2

ESS-net DWH 5  Decrease of costs & administrative burdenversus 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 kinds of new data sources  Need for integrated production systems  Make optimal use of all available data sources (existing & new) The Challenges

ESS-net DWH 6 The Statistical Data Warehouse 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. 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);

ESS-net DWH 7 Aggregate Statistics Aggregate Statistics Microdata Data extracts Data extracts Data extracts Dataset Backbones (BR eg.) Selected sample Selected sample Admin data source Admin data source BB snapshots Storage, combination OutputsInput dataInput reference frame Staging area Working data Rules for generating samples etc. Rules for updating BB

ESS-net DWH 8 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 Explaining the S-DWH

ESS-net DWH 9 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 10 The layered architectureof the S-DWH, with focus on the data sources used in each layer Specific for S-DWH

ESS-net DWH 11 Use the GSBPM as common language to identify and locate the various phases on the 4 S-DWH layers Mapping the S-DWH on the GSBPM

ESS-net DWH 12 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. Managing the S-DWH

ESS-net DWH 13 Meta data layer Source Layer Integration Layer Interpretation and Data Analysis Layer Data Access Layer Metadata Layer

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

ESS-net DWH 15 S-DWH meta data requirements SubsetsStandards & Norms ISO Internal rules Guidelines Mata data modelS-DWH Gatekeeper

ESS-net DWH 16 Defining and implementing business modell:  Organisational aspects - Experts from partners and other ESS members - Research on actual topics - Seminar / workshop  Financial aspects covered  Roll out for more fields of expertise Centre of knowledge & expertise

ESS-net DWH 17 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 Organisational aspects

ESS-net DWH Thank you ! ESSnet on data warehousing