What is typically the range for the number of labels in an analytics taxonomy?

The range of 50 - 150 labels in an analytics taxonomy is generally considered effective for creating a structured and useful framework for categorizing and analyzing data. This range strikes an optimal balance, allowing for sufficient granularity to capture the complexities and nuances of the data while also remaining manageable and understandable for stakeholders.

Having too few labels can lead to oversimplification, causing critical information to be overlooked, while an excessive number of labels can create confusion and ambiguity, making it harder for users to navigate and gain insights from the taxonomy. Thus, the 50 - 150 label range promotes clarity and efficiency in data categorization, making it a best practice in data analytics. This option allows organizations to effectively segment their data without overcrowding the taxonomy, enabling better decision-making based on analytic insights.