Scaffolding is a way to blend data. In quite a few cases, it gets the job done quite well. Thus there seems no more need to join or union data at the record level.

In recent articles, I described "Lossless Data Blending via Scaffold" and "Blending Dates via Scaffold". Both are in their simplest form: One dimensional scaffold to blend two data sources of different dimension components. The scaffolding dimension must be the superset of those same dimensions in the secondary data sources.

Again, I would emphasize that the difference between regular blending and scaffold-based blending is:
  • Blending: Loss of data in the secondary sources.
  • Scaffolding: No loss of data if we wish. Or we can choose to keep only those data of interest. The scaffold acts as the primary. All actual data sources are equally secondary.
Now, the scaffolding can help us blend the data together and show us a rather cool chart. The new question is: how do we filter it by certain dimensions?

The short answer is, we need to build the filtering dimensions into the scaffolding first. Then we can create the chart and filter the result afterwards.

There comes the multi-dimensional scaffolding. And the detailed answer follows.

Let's take the same example as in "Taking Stock with Start and End Dates". Assume we need to filter the result by Product Category and Customer Segment.

In that example, we created a single date dimension scaffolding. Now we need to add two more dimensions. The steps are as follows.

1.Create one column per dimension per sheet in Excel
So we have these 3 sheets friendly named: Date, Product Category and Customer Segment. But they could be using the default names like Sheet1, Sheet2 and Sheet3. Each sheet has a single column with header and dimension elements.
2.Cross join all the dimensions using custom SQL
A SQL one-liner suffices to generate the multi dimensional scaffolding

Select * from [Date$],[Product Category$],[Customer Segment$]

There are 2 elements in Date: Start date and End date. There are 3 elements in Product Category and 4 elements in Customer Segments. Cross joining them will generate 2x3x4=24 combinations thus 24 rows in the scaffolding.

The size of the scaffolding equals to the multiplication of the sizes of each dimension.
The next step is to make sure all the secondary data sources are blending with the primary on all 3 dimensions.

Last, by creating the same measure "Outstanding Orders" and dragging Customer Segment and Product Category to the filter shelf, we now can filter the measure and associated chart by the two dimensions.

The resulting interactive workbook can be downloaded here.

Dimension Reduction
We see that the scaffolding is created using 3 dimensions. The size of the scaffold or the number of rows are obtained by multiplying the sizes of each dimension. This number can become huge if a few of them are big. Sometimes, such a huge and bulky scaffolding is unnecessary because it takes up space and decreases performance. So we need to do some dimension reduction.

For example, in our superstore data set (depending on versions), there are 3 product categories and 17 sub-categories. If we want to filter by these two dimensions, according to the above, we seem to need 3x17=51 rows of scaffolding. This is assuming the two dimensions are orthogonal. In reality, they are not. Each category is just a label on the 17 sub-categories. And each sub-category belongs to one category only. So these two dimensions can be put in one sheet. Thus the size of the scaffolding is reduced from 51 to 17. If necessary, this sheet can be cross-joined with other dimensions.
This is how multi-dimensional scaffolding works! It can help us blend multiple data sources and build dimension filters in a very flexible way. This actually creates alternatives to union or join at the record level.
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Jake and I collaborated on a dashboard. He told me that he learnt a way to create an in-place help page in Tableau. He first saw it at a conference somewhere and couldn't recall who the speaker was. So I am blogging here about it but the credit goes to somebody else. If anyone knows who the original creator is, leave a comment below.

The key idea is to float a semi transparent worksheet on top of the dashboard, where a help text box is strategically placed on top of each chart. This way, we can explain how to view each chart and what data points are important, etc. This worksheet is collapsible by a show/hide button.

(Addendum: Jonathan Drummey has a much better Tableau-only solution that I missed from his presentation. I only caught later part of the presentation. You might ask him about it if you know him.)

In a recent presentation, Tableau visionary HOF Jonathan Drummey talked about a solution for a variable row heights in a text table. The question apparently came from a perfectionist tableau designer. Tableau is not really made for text processing.

[Forward: I asked ChatGPT o1-mini who then wrote this. Hope it helps. All the credit and the blame go to ChatGPT.

I went over the plan and it looked decent. Whether it can be done in 30 days or not, it depends on the person and the time he spends on it. By the way, ChatGPT can be a really good study buddy. Ask it questions whenever you have any.]

This comprehensive 30-day plan is designed to take you from a Tableau beginner to an advanced user.

Mundane charts are those basic ones that all data visualization beginners can create, possibly with Show Me in Tableau. They are the boring ones at times because many people tend to create fancier ones just to show off. 

I actually like the mundane ones a lot because they are not only easy to create but also easy to be read by the stakeholders.

Pareto chart is a very powerful tool, providing great insights into the data set and into the business at stake.

A while ago, Sharon came to me asking a question regarding Pareto Chart Multiples. That is, per each category, there is a Pareto chart. And we need to create Pareto charts for all the categories. This chart allows us to quickly view the few most important factors that matter to the majority of output in each category. 

Vilfredo Pareto (1848-1923) is the father of the 80/20 rule: 80% of output are produced by 20% of input. It works magically well through all the years.

[Update: The product manager Wilson Po alerted me that the Viz Extension is still a work in progress. It will not be part of the incoming version 2024.1. Instead, it will be released later in 2024. Just be patient]

Tableau 2024.1 is coming. I got a chance to test drive it. As I wrote a bunch of posts on Sankey chart tutorials in the past, I am most excited by the new Sankey chart type. Here I would like to share what I learnt. This is a quick preview. Your comments are welcome.

Buzzfeed recently asked Midjourney to draw images of people in 50 US states.  So the AI drawing tool created 50 images of couples that represent its perception of the people in each state.

I just put the images into a tiled map in Tableau. Each image is added as a background in each tile.

And also I added Viz-in-tooltips to enlarge an image to look at more details.

Feel free to download the workbook and explore it.
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The folks at Business Expert had a brilliant idea. They asked AI's perception on UK banks as a dog. I am inspired to do the same on US banks.

ChatGPT is asked to confess its perceptions on top US banks as a dog. Then Midjourney is tasked to generate the images. Check out what dog is matched to your favorite bank.

All are put together into a single-sheet Tableau dashboard. Feel free to check it out.

Through my previous post on the new Sankey chart type, I got in touch with Wilson, the product manager leading the development of this new chart type. I made some comments on creating multi-level Sankey via cascading of single Sankey's. He told me it can be done already by dropping more dimensions into the Level card.

As an enthusiastic user of Sankey charts, I am excited to learn that a Sankey chart type is being piloted in Tableau Public (Web Edit only). I wrote about Sankey chart design in multiple posts. Sankey chart may appear in different forms depending on applications. 

I played a little with it just to evaluate it. Here are my initial findings and comments.

1. The basic Sankey

I can quickly create a Sankey with 2 dimensions and 1 measure.
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