Identifying outliers or anomalies within a dataset is a critical step in data analysis. By detecting these unusual data points, businesses and organizations can gain insights into potential errors, data quality issues, or significant events. To showcase these anomalies over time, it is important to track trends and changes within the dataset.
By analyzing the trends and changes associated with the anomalies, businesses and organizations can gain insights into potential causes and take proactive measures to prevent similar events in the future. This approach can also help to identify potential opportunities or risks associated with the outliers, allowing for more informed decision-making.