The most critical first step in troubleshooting any performance issue is to diagnose the root cause through data analysis. In Tableau Cloud, this involves using administrative tools like Admin Insights to examine user activity, content usage, and background job performance. By analyzing these patterns and resource utilization metrics, an administrator can identify specific workbooks, data sources, or user actions that are creating bottlenecks during peak times. This data-driven approach ensures that any corrective actions are targeted and effective, rather than implementing costly or disruptive changes based on assumptions.

A. Increasing resources without analysis is a costly guess. The root cause might be an inefficient workbook, which scaling would not fix effectively.

C. This prematurely assumes a network bottleneck. The performance issue is more likely related to data source queries, calculations, or rendering within the Tableau environment.

D. This negatively impacts users and is a reactive measure. Analysis should first confirm which, if any, dashboards are problematic before considering their removal.

1. Tableau Cloud Help

"Use Admin Insights to Monitor Tableau Cloud": This official documentation details how to use pre-built dashboards to analyze site activity. It states

"Admin Insights provides a curated data source and pre-built workbooks to help you understand your site's usage and activity... to monitor things like content usage and user adoption." This directly supports the analysis of user patterns and resource utilization.

2. Tableau Cloud Help

"Create a Performance Recording": This document outlines the primary tool for diagnosing workbook-level performance issues. It explains

"Performance recording in Tableau gathers data about events that occur in a workbook... You can then view the performance workbook to analyze the events that are slowing it down

" which is a core component of identifying bottlenecks.

3. Pardillo

J.

& Mazón

J. N. (2012). A Temporal Data-Aware Framework for the Performance Management of Business Intelligence Systems. In Conceptual Modeling - ER 2012 (pp. 478–485). Springer. This academic paper discusses the importance of monitoring and analyzing workloads in Business Intelligence systems. Section 3

"Performance Management Framework

" emphasizes the initial phase of "Monitoring and Analysis" to capture workload characteristics and identify performance problems

aligning with the principle of analyzing before acting. (DOI: https://doi.org/10.1007/978-3-642-34002-438)