Abstract
The term quality of statistical data, developed and used in official statistics and international organizations such as the International Monetary Fund (IMF) and the Organisation for Economic Co-operation and Development (OECD), refers to the usefulness of summary statistics generated by producers of official statistics. Similarly, in the context of survey quality, official agencies such as Eurostat, National Center for Science and Engineering Statistics (NCSES), and Statistics Canada have created dimensions for evaluating the quality of a survey and its ability to report ‘accurate survey data’.
The concept of Information Quality, or InfoQ provides a general framework applicable to data analysis in a broader sense than summary statistics: InfoQ is defined as “the potential of a data set to achieve a specific (scientific or practical) goal by using a given empirical analysis method.” It relies on identifying and examining the relationships between four components: the analysis goal, the data, the data analysis, and the utility. The InfoQ framework relies on deconstructing the InfoQ concept into eight dimensions used for InfoQ assessment.
In this article, we compare and contrast the InfoQ framework and dimensions with those typically used by statistical agencies. We discuss how the InfoQ approach can support the use of official statistics not only by governments for policy decision making, but also by other stakeholders, such as industry, by integrating official and organizational data.