This is the fourth in a five-part series about my go-to elements of Tableau dashboards. For future updates, subscribe to my mailing list. I often see corporate Tableau “dashboards” built with nothing more than a large text table and a dozen or more filters along the top or side of the crosstab. Sadly, I’d even go as far to say that this is what the majority of dashboards look like when I enter a new engagement. While there is some value in using Tableau purely as a querying tool - most notably the way this type of view helps validate raw data – it misses the point of how data visualization can help us analyze data. So how do we get users to evolve past the three-decade old, Excel-like views? If I have stakeholders that simply can’t let go of filters seven and above, I like to build in a dashboard element I call the global filters tab. This feature helps prioritize filters, but also provides the flexibility to access lesser-used filters. This post will show you how to create a global filters tab in Tableau so you can keep your dashboards clean while keeping your stakeholders happy.
This is the third in a five-part series about my go-to elements of Tableau dashboards. For future updates, subscribe to my mailing list. As discussed in the triple crown framework, the practice of data visualization is very much a psychological exercise. If you can get in the head of your audience and understand their needs and what will resonate with them, you will maximize the chance of your visualization causing action. I also mentioned in my who is the audience post that two of the four audience types rely on establishing trust with the data and/or the analyst themselves before taking action. For this reason, I often like to close my dashboards with a ‘signature line’ that usually includes (1) the name of the author and how to contact them, (2) a list of data sources the dashboard is created with, and (3) a notification that tells the user whether the data is up-to-date. This post will share an example of a signature line on a Tableau dashboard and show you how to create a data status alert so your stakeholders will always know if the data source is current.
This is the second in a five-part series about my go-to elements of Tableau dashboards. For future updates, subscribe to my mailing list. In the last post, I showed you how to build a current performance versus comparison performance index callout; one of my favorite descriptive tactics for communicating performance. One of my favorite prescriptive tactics is to provide a scatter plot that the user can build themselves - even if they don’t know how to use Tableau! The “parameterized scatter plot” is considered prescriptive because it helps us understand why something happened in the business, and ideally, prescribes something to do about it. Scatter plots have several advantages including (1) they’re able to show many data points at once, (2) they help illustrate correlations, and (3) they create a natural four-quadrant segmentation. This post will show you how to make scatter plots even better by allowing your end user to choose the measure displayed on the y-axis, measure displayed on the x-axis, and dimensional breakdown of the marks on the view.
This is the first in a five-part series about my go-to elements of Tableau dashboards. For future updates, subscribe to my mailing list. One of the primary elements you’ll find on almost every high-level corporate dashboard that I ever create is a callout that conveys the current performance of the KPI by some comparison point (i.e. goal, prior period). I’ve shown you before how to make “performance indicator titles” in Tableau, but I usually take these a step further by displaying the KPI’s performance and color it based on a 100-point index or percent change. I like this this element for several reasons: (1) it provides the exact numbers for my stakeholders that are accustomed to viewing reports as raw data, (2) it forces them to benefit from the pre-attentive attribute of color, and (3) it is descriptive and helps the user decide if they should invest further time for investigation. This post will show you two design options for creating a current versus comparison index callout in Tableau.
Comparisons and viewing trends across dates are two effective ways to turn raw date into insights, but dates can be tricky to work with in Tableau. I illustrated in my last post how to find hidden patterns in line graphs by adding a slope graph toggle, but what if the dates are not lined up on the same axis? For example, if you were to make a sales by continuous Order Date line graph with Tableau’s Sample – Superstore dataset colored by Year, you would get four colored lines that do no not line up on top of each other. Tableau does not have a Month + Day date part, which can make it challenging to compare year over year performance. Due to this, I often go through the relatively elaborate process of setting up two sets of date comparison filters and equalize the dates so the lines are right on top of each other. This post will show you a simpler way to normalize months and days so they share the same axis when colored by year. When the marks line up, it is much easier to evaluate year over year performance.