What is one of the types of data analysis models mentioned?

The top-down data analysis model is a method that begins with a broad overview of a problem or system and then breaks it down into smaller, more manageable components. This approach allows analysts to establish a framework and context for the data being examined. It is particularly useful in safety contexts, such as firefighting and emergency response, where a comprehensive understanding of overarching strategies is essential before diving into specific details or data sets.

Understanding the top-down model is crucial because it facilitates efficient decision-making by guiding the analysis from a general perspective to specific details, ensuring that all factors are considered within the larger framework. This contrasts with other types of analysis that may either apply a more granular approach first or rely on existing data without establishing an overarching context.

The other types of models mentioned—cost-effective, empirical, and data-driven—discuss different focuses or methods in data analysis that do not specifically emphasize the hierarchical approach that the top-down model embodies. Each of these concepts plays a role in data analysis, but they do not encompass the same systematic methodology of starting with a high-level view and refining the information progressively.