The need for 'skeptical' accountants in the era of Big Data

- Accounting education needs to adapt to Big Data.
- Big Data analysts should be informed skeptics able to question analytical assumptions.
- Questions for analytical assumptions are organized into seven topics.

Big Data is now readily available for analysis, and analysts trained to conduct effective analysis in this area are in high demand. However, there is a dearth of discussion in the literature related to identifying the important cognitive skills required for accountants to conduct effective Big Data analysis. Here we argue that accountants need to approach Big Data analysis as informed skeptics, being ever ready to challenge the analysis by asking good questions in appropriate topical areas. These areas include understanding the limits of measurement and representation, the subjectiveness of insight, the challenges of statistics and integrating data sets, and the effects of underdetermination and inductive reasoning. Accordingly, we develop a framework and an illustrative example to facilitate the training of accounting students to become informed skeptics in the era of Big Data by explaining the conceptual relevance of each of the topical areas to Big Data analysis. In addition, example questions are identified that accountants conducting Big Data analysis should be asking regarding each topic. Further, for each topic, references to additional resources are provided that students can access to learn more about effectively conducting Big Data analysis.