The term quality of statistical data, developed and used in official statistics and international organizations such as the IMF and the 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, NCSES and Statistics Canada created dimensions for evaluating the quality of a survey for obtaining ‘accurate survey data’.

The concept of Information Quality, or InfoQ, (Kenett and Shmueli, 2014), provides a general framework applicable to data analysis in a broader sense than summary statistics: InfoQ is defined as “the potential of a dataset to achieve a specific goal 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 eight dimensions used to deconstruct InfoQ and thereby provide an approach for assessing it.

We compare and contrast the InfoQ framework and dimensions with those typically used by statistical agencies. We discuss how the InfoQ approach can support using official statistics not only by government for policy decision making, but also by other stakeholders such as industry by integrating official and organizational data.