Few textbooks on research methods offer more than a few words of advice
on how to devise scientific hypotheses. Big data is conceived as a
hypothesis-generator procedure, a disruptive analytical innovation that
is reconfiguring ecological research. My theses are (a) the hypotheses
that big-data can originate “stricto sensu” are empirical
generalizations that do not provide ecological understanding, (b)
empirical generalizations may encourage instrumentalist research, but
cannot supply ecological explanation, and (c) generalizations emerging
from data-driven research can serve as a problem-generating procedure if
they are reflected in the context of the theoretical framework
surrounding the research. Discovery (e.g., novel patterns shown by
big-data analysis) and invention (e.g., hypotheses on mechanisms and
processes conjectured by the human mind) are complementary tools in
ecological research because they play different epistemological roles.
Data-driven research provides a useful analytical tool, but it does not
justify any epistemological or methodological paradigm shift.