When identifying outliers, what are two primary factors to consider?

When identifying outliers in sustainability accounting, considering significant risks or opportunities and normalization of data is essential. Significant risks or opportunities refer to factors that could have a profound impact on an organization’s performance or sustainability efforts. These elements help highlight outliers, as they can signify anomalies that might indicate either a critical issue needing attention or an opportunity for improvement.

Normalization of data is crucial for effective outlier detection, as it allows for adjustments to be made for any discrepancies in data collection or measurement that could skew results. Normalizing data ensures that the analysis reflects true variations rather than those caused by differences in scale or other factors unrelated to inherent performance characteristics. This dual focus allows analysts to gain a clearer understanding of the underlying trends and discrepancies in the data.

The other options, while relevant in different contexts, do not directly address the multifaceted nature of outlier identification. Data integrity and historical performance focus more on the reliability and past behaviors of data rather than isolating outliers. Comparative analysis and geographical factors might provide context for understanding data points, but they do not specifically tackle the identification process. Financial ratios and market trends emphasize quantitative analysis but miss the qualitative aspects of risk and the necessity for data normalization, crucial for accurate outlier detection.