Table 3 Fallacies of empiricism according to [9]
From: Data science: developing theoretical contributions in information systems via text analytics
Claim | Counterargument |
|---|---|
Big data (as a key force behind data science) can capture the full resolution of a given domain or phenomenon | No matter how exhaustive the data is, it is still a representation and a sample, and is bound by space and time in a continuously changing world |
There is no need for a priori knowledge in the form of theory, models or hypotheses | Systems that capture and generate data are designed for very specific purposes, and analytical algorithms are designed through scientific reasoning (drawing on established theories) |
Data can speak for themselves free from human bias | Patterns extracted from data require human interpretation and theorization to avoid “ecological fallacies” when taking action based on random correlations. Making sense of data is always framed through our knowledge and experiences |
Meaning transcends context or domain-specific knowledge | Domain-specific expertise and wealth of knowledge is needed to assess and articulate problems and interpret results in order to avoid reductionism |