Ever added a reference line to your Tableau visualization, only to see it appear in the grand total row, even when it doesn’t make sense?

If your grand total uses a different aggregation than your main data (e.g., SUM vs. AVG), the reference line can be misleading. 

Unfortunately, Tableau doesn’t offer a simple way to exclude it—but there’s a trick to removing it while keeping your totals visible.

In the video below, I’ll walk you through the step-by-step solution to remove reference lines from grand totals.

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If you’re serious about learning Tableau, you must understand the level of detail of the view. Another way to think about it is the level of granularity.

This is essential for ensuring you know the aggregations that Tableau will perform as this will impact your analysis.

Here's a guide to help you:

Have you ever wondered how to compare sales performance between two sets of weekdays? 

In this video, I'll show you how to calculate and compare sales for the last 10 weekdays against the prior 10 weekdays—all while excluding weekends. 

What You Will Learn:

1. Setting up your data source for comparison
2. Creating calculated fields for two sets of weekdays
3. Configuring table calculations to get only the days you need
4. Filtering out weekends from both data sets
5. Implementing advanced date functions for comparison
6. Visualizing and comparing sales for both sets of weekdays

Background Context:

Tableau enables you to conduct intricate data analyses, but comparing specific sets of weekdays can be tricky. This tutorial simplifies that process, focusing on comparing the last 10 weekdays' sales with the 10 weekdays preceding them.

Assumptions:

1. You have a foundational knowledge of Tableau.
2. Your sales data contains date information.
3. You've previously worked with calculated fields in Tableau.

Related Videos:

1. Tableau for Beginners: Link
2. Everything you need to know about table calcs: Link

Resources:

In this tip, I going to show you how to build 3 different KPI cards for measuring day over day change. In just a few steps, I’ll show you how to calculate:

1. Sales for the most recent day
2. Sales for the previous day
3. Difference in sales between those two dates
4. Percent difference in sales between those two dates

I'll then show you how to create these 3 different methods for displaying the KPIs so that you can create those that are most useful for your audience.

RESOURCES

• Data - https://data.world/vizwiz/sample-car-sales
• Month over Month Change - https://www.youtube.com/watch?v=ViD3vclgNv8
• Year over Year Change - https://www.youtube.com/watch?v=YuxYr4_l2ww

Aggregations can be difficult to understand. In Tableau, I find the ATTR (attribute) aggregation the hardest to get my head around. 

Marginal histograms are a great choice when you want to display your data at different levels of aggregation in a single view.  Download the data here.

If you don't participate in Workout Wednesday, you're missing a great opportunity to learn. For week 24 2018, Ann Jackson challenged us to also use Tableau Prep. The toughest part of the data prep was getting the second purchase date for a customer. First is easy. The trickiest part is that you can't sort data in Prep, so you have to do some workarounds to get what you need.

In this video, I should you how I approached returning the first and second purchase dates for a customer, include some summary measures, then bring them both back together into a single table for visualizing in Tableau.

Aggregated extracts are an undervalued and underused feature in Tableau. 

Week 6 of #MakeoverMonday allowed us to work with 105M Chicago taxi trips from an Exasol data source. This is fantastic, until you need to publish the viz to Tableau Public which has a 15M record limit. The way to work around the 15M limit is by creating an aggregated extract, for which Tableau has created a great quick start guide here. In the video below, I show you how I created an aggregated extract with the Chicago taxi data.

Keep in mind, though, that your data has to be aggregatable. For example, if you do cohort analysis, you likely won't be able to aggregate the data and maintain the cohorts.

Why aggregated extracts?

1. Smaller extracts
2. Better performance
3. Only contains the necessary dimensions
4. Makes extract refreshes faster
5. Reduces resource burden on your Tableau Server

1. Hide unused dimensions
2. Add extract filters (optional)
3. Aggregate the data for the visible dimensions
4. Create the extract