Data analysis requires deeper exploration:
Data analytics is a powerful tool for decision-making, but managers must not blindly trust the surface results. They should investigate deeper insights and view surface data with professional skepticism. Emergence of new tools in finance:
New data analytics tools in the financial sector provide unique insights but also create challenges when applying skepticism. Bigger data availability leads to more information but also increases challenges in identifying and managing red flags. Concern of false positives:
False positives occur when data identifies anomalies that turn out to be normal variations upon investigation. The frequency of false positives increases as data size and complexity grow. Difficulty in identifying outliers:
Analysts often face problems in determining outliers while using data analytics tools. Importance of professional skepticism:
Professional skepticism is vital for practitioners, researchers, regulators, and standard setters. However, it may result in extra costs, such as: Strained client relationships. Budget issues. When to apply skepticism:
Finance and audit professionals need to identify when and where to apply skepticism. Balance is critical between costly skepticism and underutilizing data analytics to control costs.