Abstract: In a world of ever increasing data availability and user expectations,
National Statistical Offices face mounting challenges to produce relevant
and timely statistics. They need to transform their business practice to
take advantage of big data - especially administrative data - by
integrating non-traditional and survey data sources to maximise value, and
utilising new technology to enable enhanced analysis. An example of a
response to these challenges is the prototype GLIDE (Graphically Linked
Information Discovery Environment) the Australian Bureau of Statistics (ABS)
is currently developing using semantic web technology. This environment
includes as a test case a prototype semantic linked employer-employee
database (LEED) which integrates administrative tax data and ABS business
register data to enable detailed microeconomic analysis. However, as data
structures become more complex and multi-dimensional, data integration and
exploration encounters challenges within traditional relational databases,
prompting the exploration of alternatives. Semantic web technology allows
for a flexible data structure, machine reasoning and inference on the
dataset, a shared understanding of the data's meaning, reusable
classifications and standards, easy exploration of many dimensions, and
network analysis. The possible advantages of such an approach for official
statistics are demonstrated through two practical examples, showing how the
prototype GLIDE supports effective data exploration and visualisation, and
enables network analysis, to solve real business problems.
Keywords: Semantic web, linked data, official statistics, data visualisation, data analytics
