Big Data is not the New Oil: Common Misconceptions about Population Data
This work identifies critical pitfalls for researchers and practitioners handling population data, though it is incremental as it synthesizes existing issues rather than introducing new methods.
The paper addresses common misconceptions in using population data for research and decision-making, highlighting that large size does not guarantee validity and that social factors often lead to overlooked technical issues in data processing and linkage.
Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably many of these misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many of these misconceptions are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.