Online Analytics-Con-301 Questions Video

For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:

Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.

Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices. Fixed sizes prevent components from resizing in ways that could disrupt the dashboard’s readability or functionality.

Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.

References

This approach leverages Tableau’s device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau’s official guides on creating and managing device-specific dashboards.

Comprehensive and Detailed Explanation From Exact Extract:

The client wants to always show the most recent day present in the data, not today’s date. The dataset is updated intermittently, meaning some days may have no new rows. Tableau documentation states that:

Using TODAY() recalculates on every query and prevents effective caching because Tableau must compute the current date for each refresh.

Using Relative Date Filters like “Last 1 day” also prevents caching because Tableau evaluates relative conditions each time the workbook loads.

Quick Filters also break caching and decrease performance because they require interactive evaluation on each render.

A FIXED LOD calculation allows Tableau to compute the maximum date inside the extract, which preserves caching because it is data-driven, not time-driven.For example:{ FIXED : MAX([Date]) }Then filtering where [Date] = { FIXED : MAX([Date]) } ensures only the most recent date in the dataset is shown.

Tableau’s documentation on performance emphasizes that caching is maximized when calculations depend only on the data itself and not on functions like TODAY() or relative filters.

A FIXED LOD provides the best caching performance and correctly returns the most recent date based on the dataset rather than the current system date.

Tableau extract caching behavior describing how data-dependent filters cache better than time-dependent filters.

LOD Expressions guidance recommending FIXED for identifying values like “latest date in the dataset.”

Tableau performance guidelines discouraging TODAY() and relative date filters when caching is important.

For optimal performance, it is recommended to limit the data to what is necessary for analysis, which in this case would be the current and previous years. Moving the calculation of averages to the data layer and aggregating the dates to a monthly level will reduce the granularity of the data, thereby improving the performance of the dashboard. This approach aligns with best practices for optimizing workbook performance in Tableau, which suggest simplifying the data model and reducing the number of records processed12.

To improve performance by moving calculations to the data layer and materializing them in the extract, the consultant should choose calculations that benefit from pre-computation and significantly reduce the load during query time:

Aggregation-Level Calculation: The formula SUM([Profit])/SUM([Sales]) calculates a ratio at an aggregate level, which is ideal for pre-computation. Materializing this calculation in the extract means that the complex division operation is done once and stored, rather than being recalculated every time the report is accessed.

Performance Improvement: By pre-computing this aggregate ratio, Tableau can utilize the pre-calculated fields directly in visualizations, which speeds up report loading and interaction times as the heavy lifting of data processing is done during the data preparation stage.