4 posts tagged


Tableau Dashboard Overview

Estimated read time – 7 min

In the previous article, we focused on the problem statement, designed a layout, shared our goal to build a Tableau Dashboard for Superstore dataset. The dashboard should provide insights on most profitable regions, products, customer segments and estimate key performance indicators (KPIs) over the past time.

The data in SuperStore Sales reflect sales and profit of the retail chain in Canada. It includes information about customer orders, refunds, sales and geodata. But we’re mostly interested in sales data, as our main goal is to create an executive dashboard to understand company’s operating margin, find most and least lucrative product categories, and customer segments.

So here’s how the dashboard looks like:

All dashboard elements are placed into containers, we can easily resize or change their hierarchy, this enables to optimize the dashboard and make it more mobile/tablet friendly. We can also filter the data by time periods and choose a specific month and year in the top right corner, and all the charts will be redrawn automatically.

The next field shows key factoids on the company performance: profit, sales, orders count, average discount, customers and sales per customer. Each of the indicators displays YOY, a statistical measure to evaluate a company’s financial progress over time. If the indicator shows positive change, a green arrow will be added, if negative – red.

Below are two core charts, displaying regions (colored based on profit) and profit dynamics. We can click on a specific one to view its stats in-depth.

The green dot on the right chart represents data for a selected month this year, while the blue dot displays the same month last year. When hovering these points you can see a trend line, that facilitates assessing how the company’s doing today.

Let’s move to the second part, here we placed company’s products and customers onto 3 charts. The first one, starting from the left, called bar in bar chart, where you can easily explore product efficiency. For instance, Tables is one of the most inefficient categories, with Breford CR4500 that resulted in significant losses.

Bar in bar chart implementation

Then goes the chart with company’s customers, by default they are sorted in descending order by profitability. The chart is linked with Top Performing Provinces, so if we want to discover best or worst customers for the selected province, the data will be redrawn automatically.

Dashboard Evaluation

We evaluated this dashboard according to the criteria below. On a scale of 1 – 10, 10 being the highest, it gets the following scores from our team :

  1. Meets the tasks – 10,0

  2. Learning curve  – 5,5

  3. Tool functionality – 9,0

  4. Ease of use – 8,5

  5. Compliance of the result – 10,0

  6. Visual evaluation – 9,7

This Tableau Dashboard scored 8.8 out of 10 from the team! In our perspective, the dashboard fully meets the requirements and facilitates understanding of business performance over a reporting period. We can assess profit dynamics in general or for the selected region, and effectively leverage products and customers data in measuring monetary results. The final version is available through this link.

Please let us know your thoughts in the comments down below, how would you rate this dashboard?

 No comments    168   5 mon   BI   BI-tools   guide   tableau

Animating sports data in Tableau

Estimated read time – 4 min

Previously we shared how to visualize your sports data from the SwingVision app in Tableau , using custom background and shapes. This time we are going to animate our dashboard to watch how landing locations of tennis shots changed over the match. Such an animation can be exported into a video file for later use. That’s what our result looked like in Tableau earlier:

The chart shows landing coordinates of tennis shots on the court. Forehand shots are marked in red, backhands are in orange, the x marks for shots went into the net. We can also use filtering and get expanded tooltip info on hover.
Tableau enables us to create pages to flip through members of a field, changing and animating the analysis. In this case, all we need to is simply drag-and-drop the Shots table to the Pages shelf and click on the Play button.

Let’s switch to the dashboard and try adding the Pages shelf, just click on Worksheet -> Show cards and apply to the current page.

Next, create a new vertical container, drag the panel and minimize the view:

Now after clicking on the Play button, the first part is done:

If you’re a macOS user, it won’t be a problem to make a video from this animation by pressing ⌘ + Shift + 5 and choosing a specific part of your screen. In other cases, you may need to download third-party software for screen recording.

 No comments    61   5 mon   animation   BI-tools   dashboard   tableau

Custom visualization of sports data in Tableau

Estimated read time – 8 min

Being a tennis fan, I recently discovered a new app created to help players to assess their game skills – SwingVision. The app can recognize tennis shots in real-time and display its landing coordinates. The author of this app is Swupnil Sahai, currently he is a Lecturer at UC Berkley.

My tennis stats, shown by the app

SwingVision also allows you to view your “rallies” and specific shots, assess the average shot speed and error rate. Moreover one can easily export its stats as an Excel Table.

Example of exported table

In today’s material, we are going to create a custom Tableau chart that would reproduce stats from SwingVision and display the landing location of my shots on the court. First, we need to find a suitable tennis court image (top view), like this one.

Next, we need to import the data stored as an Excel Table into Tableau, set values for both coordinates using the Shot Placement (x), and Shot Placement (y) columns, and remove the aggregation of measures to get something like this:

After filtering shots by player the chart somewhat resembles the upside-down version of the actual image:

To reverse the image, we need to change the values of current x and y from positive to negative by creating new measures, add some color and everything will start to line up:

The X marks on the chart represent all shots that hit the net, we can hide them from view and set a constant value for Y =- 11,89, which corresponds to the length of a half-court.
Then when we try adding the background image, however, this will cause a warning, because the image is not scaled properly:

This means that we need to calculate the ratio of our image to the real-size court. In our case, for instance, the image is 913px in width, while the court itself is 10.97 meters wide, so by calculating 913 over 10.97, the ratio for x will be 83.227.

The middle of the court will be considered as the origin (0, 0), and will divide the court vertically into halves of 456.5px.
Remember that the image itself has margins, both to the right and left that are equal to 143.3px each. Just create new measures for x and y, substituting with the following values:

After these steps, our image should be as follows:

As finishing touches, we set a custom icon for each point on the chart and add filtering options.

To sum up, the dashboard displays everything we need: landing location of shots, their speed, types of strokes and expanded tooltip info on hover:

 No comments    77   5 mon   BI-tools   dashboard   tableau

Guide to modern Business Intelligence Tools

Estimated read time – 2 min

In our new series, we will try to give a detailed representation of  several BI tools using the SuperStore Sales dataset. The data in SuperStore Sales reflect sales and profit of the retail chain in US dollars.

In the upcoming blog post, we will discuss a real problem statement that could arise when creating a dashboard based on the SuperStore Sales data and design a functional layout to provide clear answers. Throughout this task, we’ll stick with a predefined set of colors to make the comparison more unbiased.

Next, we’re going to create a dashboard that would assist in data-based decision-making with each of the BI tools. We also plan to involve industry experts to learn from their experience.

A complete list of BI systems and tools to be tested in our experiment is provided below. I want to welcome everyone who is willing to help us in solving this challenge to message me on Telegram  – @valiotti. I will be glad to hear from you. Although it’s a non-profit project, it’ll be really useful for the open-source community.

We plan to cover the following list of tools:

Free Open Source:

  • Metabase
  • Redash
  • Apache Superset
  • Dash / Plotly

Free Cloud-Based:

  • Google Studio
  • Yandex Datalens

Paid Cloud-Based:

  • Mode
  • Cluvio
  • Holistic
  • Chartio
  • Periscope
  • DeltaDNA
  • Klipfolio
  • Count.co


  • PowerBI
  • Tableau
  • Looker
  • Excel
  • Alteryx
  • Qlik Sense
  • Qlik View

The final goal is to evaluate the BI tools against the following criteria:

  • learning curve of BI tool (1 — too hard to learn, 10 — easy)
  • tool functionality (1 — very poor functionality, 10 — multifunctional)
  • ease of use (1 — very inconvenient, 10 — super convenient)
  • compliance of the result (1 — far from the designed layout, 10 — too close to the designed layout and objective)
  • visual evaluation (1 — poor appearance, 10 — great visual appearance)

An integral weighted score for each tool will be calculated based on the internal estimates.

The results will be posted to our Telegram channel @leftjoin_en and followers will also be able to share their thoughts on the experiment.
By the end, each tool will be represented as a point in the plane, which will be divided into 4 parts.

This article will be updated with links and ratings as we new posts come out.

 No comments    90   6 mon   BI-tools   excel   looker   powerbi   redash   tableau