What is a suitable method for addressing the need to analyze data at a granular level?

Analyzing data at a granular level requires access to detailed information that can provide insights into individual transactions or events. Row-level transaction details are indeed the most suitable method for this purpose, as they allow analysts to see each transaction or data point in its entirety. This level of detail enables the identification of patterns, trends, or anomalies that may not be apparent in aggregated data.

When working with row-level data, analysts can perform deeper investigations into specific records, making it possible to answer detailed questions and conduct thorough analyses. This can be crucial for tasks such as identifying root causes of issues, performing quality control checks, or understanding customer behavior on an individual basis.

In contrast, aggregate summary reports, visualization comparisons, and broad overviews provide higher-level insights and trends but lack the depth necessary for granular analysis. While these methods are valuable for understanding overall performance and broad patterns, they do not offer the level of detail needed to analyze data effectively at a granular level.