Speaker
Description
A modernised statistical production will in nature be multi sourced. Administrative registers and other digital data sources will be combined with traditional or complementary sample surveys to create qualitative statistical output while minimising response burden. Furthermore, many new data sources will be useful for several statistical outputs. This situation calls for an architecture that gives the statistical office control over its data and data quality, and how data flows both inside the office and to and from other agents in various data eco systems. Standards like The Generic Statistical Business Process Model (GSBPM), Generic Statistical Information Model (GSIM), Data Documentation Initiative (DDI), Statistical Data and Metadata eXchange (SDMX) and Linked Open Data (LOD) are essential in establishing such an architecture.
Over the last few years, Statistics Sweden has started implementing such an architecture. This paper describes the fundamental concepts in that architecture, and how we have used various standards in building it. The paper describes our data architecture using “steady states” of data throughout the production process, how data management, data access and data sharing is set up to allow for maximum re-use and combination of data sources while preserving security in data management, and how metadata and quality indicators can facilitate both re-design of statistics to make use of new emerging data sources and make data “AI ready”.