Tableau Cloud Usage Swift by Playfair Data
The Tableau Cloud Usage Swift takes disparate data from Tableau Cloud for a visual analytics tool to help users understand their dashboard portfolio adoption at a glance. Developed with advanced filtering options and insight panels, it enables Tableau Cloud Administrators to evaluate the usage of their data visualization portfolio, assess active users within their Tableau Center of Excellence, identify areas for performance optimization, and measure the real-time impact of their dashboards.
Employing an intuitive star methodology to track dashboard popularity based on quantile tracking, the tool highlights top-performing dashboards with five stars, indicating 80-100% adoption rates, while those in the 60 - 79% adoption range receive four stars, and so on. This familiar visual representation aids in quickly identifying areas that may require attention and optimization and highlights dashboard adoption.
Not all dashboard views are equally important. In addition to the star methodology, the advanced filtering options found in this tool allow administrators to select "critical users" and assign a weight to their Tableau usage activity. This function allows users to focus on the most impactful stakeholders or contributors to their dashboard's success. Additionally, administrators have the flexibility to select different measures to highlight specific cards or views, tailoring their analyses to suit their unique goals.
In addition to workbook ranking by popularity, it analyzes dashboard performance across popularity, load time, and storage used, delivering dynamic, actionable recommendations linked directly to educational tutorials and resources. By providing clear steps to enhance the functionality of underperforming dashboards, the Tableau Cloud Usage Swift provides users with the means to make data-driven decisions, optimize performance, and maximize the impact of their dashboard portfolio.
CreditsStrategy: Ariana Cukier, Jason Penrod, Rafael Simancas, Ryan Sleeper | Visual Analytics Architecture (Design): Luke Lewis | Data Engineering: Nick Cassara | Decision Science Engineer: Maddie Dierkes | Visual Analytics Engineering: Dan Bunker | Quality Assurance: Alyssa Huff, Megan Spengler