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June 20, 2024

How to Simultaneously Highlight & Deselect with a Parameter Action in Tableau

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In this video, we’ll dive into an advanced technique to enhance your Tableau visualizations: simultaneously highlighting and deselecting marks using parameter actions. This is a great skill for making your dashboards more interactive and user-friendly. 

What You Will Learn:
- Highlight & Deselect Marks: Learn how to use parameter actions to highlight selected marks while deselecting others, improving the clarity and focus of your data stories.
- Dynamic Labeling: Discover how to label ONLY the selected bar, making it easier to convey specific insights without cluttering your visualizations.

Tutorial Highlights:
1. Introduction to Parameter Actions: A brief overview of what parameter actions are and why they are powerful tools in Tableau.
2. Step-by-Step Guide: Follow along as I demonstrate how to set up parameter actions to achieve the simultaneous highlight and deselect effect.
3. Labeling Techniques: Learn the trick to dynamically label only the selected mark, enhancing the readability and professionalism of your dashboard.

By the end of this tutorial, you will have the skills to create more interactive and visually appealing Tableau dashboards that can captivate and inform your audience effectively.

June 4, 2024

Find Red Flags in Your Data in ONLY 5 MINUTES with Control Charts

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If you like this video, you'll love learning from me 4 hours per week in Next-Level Tableau. Join here.

Control charts are one of the best charts you can use for identifying outliers in a series of measurement. So what are they?

Control charts are used to monitor whether a process is performing consistently over time. It's basically a line graph that tracks data points collected at specific intervals, but with three key additions:

1. 𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗹𝗶𝗻𝗲: This horizontal line represents the average performance of the process based on historical data.
2. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗹𝗶𝗺𝗶𝘁𝘀: These are two additional horizontal lines, one above and one below the center line. They are typically 2-3 standard deviations from the average, but can be whatever number of standard deviations work for your situation. These limits reflect your "stable" range.
3. 𝗢𝘂𝘁𝗹𝗶𝗲𝗿 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Color-coding the outlier marks helps you more easily identify the problems.

The line chart you create will help you see if measurements fall within the control limits. 

- If they do, it suggests the process is in control. 
- If they fall outside the control limits, it indicates a potential problem that needs additional analysis.

Control charts are widely used for quality control purposes (especially in manufacturing), but they can be applied to any process where you want to track performance over time. 

Download the workbook here.