More data is not necessarily better data, whatever the source.
Claims based on lots of data can be misleading. Sometimes this is called “big data” (data from large databases) or “real world data” (routinely collected data). Unfortunately, routinely collected data often does not include data about “confounders”.
Confounders are factors other than the interventions being compared that can affect the outcomes. For example, a study might compare the effects of grazing on grassland biodiversity by comparing fields that are grazed at different intensities. If the fields were also grazed at different times of year or by different types of grazers, these would be potential confounding factors.
When using routinely collected data, it is only possible to control for confounders that were already known and were measured when the data was collected. So, we cannot be sure that an association between an intervention and an outcome means that the intervention caused the outcome, rather than confounders.
BEWARE of claims that an intervention has an effect based solely on the existence of large amounts of data or an association found using “big data” or “real world data”.
REMEMBER: All data are not equally reliable. Think about whether you can be sure that there aren’t other reasons for the association.