Which visualization technique is best suited for identifying outliers in sales data?

The heat map is an ideal visualization technique for identifying outliers in sales data because it effectively represents data variability through color gradation. In a heat map, each value is represented by a color, allowing users to quickly spot anomalies when certain values stand out against the overall color scheme. For sales data, where variations in performance can be crucial, heat maps can easily highlight unexpected spikes or drops in sales, thus revealing outliers.

Other visualization methods may not be as effective in highlighting these discrepancies. For instance, a time series chart is great for analyzing trends over time but may not distinctly indicate outliers unless specifically focused on anomalies in a temporal context. Stacked bar charts present comparative data well but can obscure individual data points, making it harder to identify outliers. Similarly, pie charts, while useful in showing proportions of a whole, do not provide enough detail to effectively pinpoint individual outliers in sales figures.

In contrast, the heat map's emphasis on color variation makes it a powerful tool for quickly spotting these anomalies in large datasets, which is fundamental for data analysts looking to make informed decisions based on sales performance.