United Nations Economic Commission for Europe Statistical Division Standards-based Modernisation Steven Vale UNECE [email protected] Contents The data deluge Standards Projects Big Data.
Download ReportTranscript United Nations Economic Commission for Europe Statistical Division Standards-based Modernisation Steven Vale UNECE [email protected] Contents The data deluge Standards Projects Big Data.
United Nations Economic Commission for Europe Statistical Division Standards-based Modernisation Steven Vale UNECE [email protected] Contents The data deluge Standards Projects Big Data Why is modernisation important? In the last 2 years more information was created than in the whole of the rest of human history! The Challenges New competitors & changing expectations Rapid changes in the environment Increasing cost & difficulty of acquiring data Reducing budget Riding the big data wave Competition for skilled resources These challenges are too big for statistical organisations to tackle on their own We need to work together Using common standards, statistics can be produced more efficiently No domain is special! Do new methods and tools support this vision, or do they reinforce a stove-pipe mentality? The answer ... Standards-based Modernisaton The GSBPM Why do we need the GSBPM? To define and describe statistical processes in a coherent way To compare and benchmark processes within and between organisations To make better decisions on production systems and organisation of resources The GSBPM is used by more than 50 statistical organisations worldwide KSBPM – Republic of Korea Beyond statistics: Data archives Generic Longitudinal Business Process Model Framework for combining standards? Something missing? We need a layer between GSBPM and the data transfer standards! A global project Overseen by the HLG GSIM and GSBPM GSIM describes the information objects and flows within the statistical business process. So what is GSIM? A reference framework of information objects: • • • Definitions Attributes Relationships GSIM aligns with relevant standards such as DDI and SDMX Version 1.0 released in December 2012 GSIM gives us standard terminology GSIM documentation Different layers of detail for different audiences! HLG Projects for 2013 Aim: “to support the enhancement and implementation of the standards needed for the modernisation of statistical production and services” GSIM Implementation Group Providing support for a community of GSIM “early adopters” A forum for exchanging ideas and experiences 12 organisations represented Feedback on how to improve GSIM Mapping GSIM to DDI and SDMX Detailed mapping between GSIM and the information models of DDI and SDMX. Evaluate coherence / differences GSIM / SDMX v2.1 GSIM / DDI v3.2 Reviewing GSBPM and GSIM Gathering feedback (until 30 September!) Expert groups reviewing and refining New versions of GSBPM and GSIM by the end of 2013 But ... • Continuity is important • Major change is unlikely Historically, statistical organisations have produced specialised business processes, methods and IT systems for each survey / output Applying Enterprise Architecture Disseminate ... but if each statistical organisation works by themselves ... ... we get this ... .. which makes it hard to share and reuse! … but if statistical organisations work together to define a common statistical production architecture ... ... sharing is easier! Layers of Architecture = Business Layer = Information Layer = Implementation Layer Proof of Concept Currently being developed to: • • • Demonstrate the process of working together and the advantages in cooperation Demonstrate business viability to senior management Prove the value of the architecture - Here is something that we could not do before Big Data: A new project for 2014? HLG and Big Data Paper: “What does Big Data mean for official statistics?” Project proposal from global task team: • Work package 1: Strategy and methodology • Work package 2: Shared computing environment (“sandbox”), practical application of methods and tools • Work package 3: Training and dissemination Collecting Big Data? Is a completely new approach needed? • Big Data, but small processes • Why move the data? • Fundamental paradigm shift: Process data at source (or in the cloud) rather than in house? • Just transfer aggregates back to statistical organisations? More research needed! Informal Workshop Friday afternoon All welcome Get involved! Anyone is welcome to contribute! More Information • HLG Wiki: http://www1.unece.org/stat/platform/display/hlgbas • LinkedIn group “Business architecture in statistics”