Using which method can data analysis be made more dynamic?

Dynamic analysis refers to the ability to adjust the view, scope, or context of data in real-time, allowing users to explore data from different angles and derive insights without being limited to static reports. This method enhances the flexibility of data interpretation, enabling users to ask new questions and refine their analysis on the fly, which is crucial in a rapidly changing business environment.

Using dynamic analysis, users can modify parameters such as time periods, scenarios, or specific data sets interactively. This adaptability makes it easier to detect trends, patterns, and anomalies, thus improving decision-making and forecasting. In contrast, while pivoting, custom dimensions, and cube sheets can all aid in data representation and structure, they do not inherently offer the same level of flexibility and responsiveness that dynamic analysis provides. They often require more manual adjustments or pre-defined structures compared to the fluid nature of dynamic analysis.