Remember to consider the limitations and pitfalls of causal analysis: correlation does not equal causation.
Always consider the context of your data: read the technical documentation, think critically about what the dataset misses or excludes, and what biases may be present. For instance, who did the counting and what might they have been blind to?
Limitation: All data represent researchers' attempts to quantify and categorize human behavior. They are inherently limited by the implicit and explicit biases of those researchers and the era in which they were collected. Users must consider the limitations, gaps, and faults of any data source.
Limitation: It is very difficult, for both novice and experienced researchers, to understand complex issues using just raw data and analytical methods. Researchers must consider the limitations and pitfalls of causal analysis.
See these resources for further information:
The University of Minnesota Libraries guide, Conducting research through an anti-racism lens, provides a useful rubric for building awareness of a variety of forms of privilege often inherent in research and offers strategies designed to de-center whiteness in research.