Understand the step-by-step workflow of Tableau, from connecting data to publishing visualizations, and learn which tasks require the most attention. Gain clarity on available chart types and the distinction between dimensions and measures to build effective data visualizations.
Key Insights
- The Tableau workflow follows a linear process: connect to data, define relationships, perform basic formatting, build and edit worksheets and visualizations (the most time-intensive step), and finally create dashboards and publish results.
- Tableau offers native support for common chart types like bar, line, pie, and scatter plots, but more complex visuals like donut or gauge charts require manual workarounds and are not built-in by default.
- Dimensions in Tableau represent qualitative data (such as names or dates), while measures represent quantitative data (such as sales), and the platform uses color coding to visually distinguish between the two.
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Here is the Tableau workflow, the linear step-by-step process. Connect to your data sources, create relationships between your datasets, if you have multiple datasets, perform basic formatting of data, create worksheets, which is where you'll spend most of your time, format and edit your visualizations, which is where you'll spend most of your time, create dashboards and stories, less of your time, publish your visualizations, less of your time. So this area here, create worksheets and format and edit your visualizations, this is going to take up most of our time.
Let's see, perform basic formatting of data, yeah, probably these three. This will take like, I don't know, 10,20 minutes. This, maybe like about an hour or so.
I'm not talking about today, but like, depending on what you want to do, these take less time than what we do in the middle. Tableau chart types, these are the charts that are available natively in Tableau. So since it's a data visualization application, you want to know the types of charts you can create.
Bar chart, line chart, pie chart, maps, density maps, scatter plot, Gantt chart, bubble chart, pre-map, area charts. These are all default. Notice anything missing? Well, someone might say, how about a donut chart? Doesn't exist.
Wait, okay, I'm going back to Power BI. You're kidding. How does anyone even pay for this software? They're charging all this money.
You can create a donut chart, but it's not natively available. You're going to have to do something to make it look like a donut chart. What's a donut chart in this case? A donut chart is a pie chart with a white circle in the middle.
You mean that circle is not like through the pie? No, it's a white circle they put on top of a pie chart. And then they typed on top of the circle? Yeah, but it looks like a donut pie. It looks like it's hollow.
No, it's not hollow. It's just a white circle. So you'll have to go through certain steps to make it look like that.
How about a gauge chart? I'd like to do that too. That's going to take even more work. That'll take you, I don't know, 35 to 40 steps to create something like, but you can do it.
It's just, you'll probably find some YouTube video where someone outlines all the steps you have to go through. We're just letting you know that. We're going to work with charts tomorrow.
Dimensions versus measures. Again, data analysis stuff that you should know. Dimensions are categories like products.
They correspond to qualitative data. Measures refer to quantitative data like sales, numerical values. So I have the breakdown here.
Dimensions are qualitative data such as name or date. Any field with text or date values. They appear as column headers for rows of data such as customer name, product name, city names, order date.
It defines the level of granularity that shows in the view. Measures are quantitative. They're numerical data.
By default, Tableau treats any field containing this kind of data as a measure. Data that is classified as a measure can be aggregated based on a given dimension. For example, total sales by region.
Sales would be measure. Region would be the dimension. That's how you're categorizing the total sales.
Remember dimensions and measures broadly correspond to qualitative and quantitative variables. Dimensions are fields containing text or dates. While measures always contain numbers.
Not 100% of the time, but for the most part. Discrete fields are in a blue background and appear as a blue pill in the shelf. So in Tableau, you'll be able to tell the difference between dimensions and measures because they will color those fields based on measures or dimensions.
To make it very easy for you to know what's numerical and what's categorical.