Our last overview on BI instruments was dedicated to Redash, but today we will deal with the dashboard in QlikSense using the SuperStore Sales dataset. The dashboard was built for us by Alexey Grinenko, a head of BI section, Business Solutions Development Department at Softline.

Data Sources

To load data in QlikSense we need to use load scripts. This is how the script looks:

Basically, the script lists the columns that we need to import and the link to a file. [Calendar_Order] script makes a custom calendar as all our data is before 2012 and the default calendar creates measures such as *current month* or *previous month* which we don’t need.

Dashboard Structure

As you can see, the dashboard has the same structure as in Tableau with some peculiarities of QlikSense.

At the top, there is a line with KPI cards. The cards compare KPI values in the chosen year with the values in the previous one and show the change in percentage. If the change is positive, the percentage is displayed in green, if the change is negative, values are red.

Then you can see a treemap which shows provinces by profit. The color shows profit distribution in the provinces. As all charts in QlikSense, the treemap is clickable and we can choose several provinces and see only the data related to them. On the right of the treemap, you can see area graphs that show profits by month in 4 different years.

At the top right corner of the dashboard, we have a drop down menu where we can choose a month and a year. As QlikSense doesn’t have this feature as default, we had to customly create such a calendar in a load script. First, we loaded the Month and the Year columns and created a third column where we concatenated Month and Year with a dash. This measure will later help us in switching the periods.

In the next part of the dashboard, we can see the analysis of products and customers. On the right, there is a bar chart that shows profit and sales by categories. On the left, the bar chart displays the Top 15 products by profit. In the settings, we can change this number and display fewer products if we wish.

Last but not least, we have a bubble chart which shows the distribution of customers by profit and sales. Each bubble represents a customer and the size of the bubble shows the number of orders. The color indicates the size of the discount.

Conclusions

Together with Alexey (his scores are in brackets) we have evaluated the dashboard on 10 points scale (10 being the highest score) and received the following results:

1. Meets the tasks – 10
2. Learning curve  – 6.9 (8)
3. Tool functionality – 9.0 (7)
4. Ease of use – 7.3 (8)
5. Compliance with the layout – 9.8 (9)
6. Visual evaluation – 7.5 (10)

Overall: 8.4 out of 10.

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.

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:

In our previous post, we announced the beginning of a new series about modern Business intelligence (BI) tools. As the adage goes, “problem first, solution second” – today we’ll start by defining our problem. Let’s consider a fairly common scenario for a large company, one that almost every company, where I happened to work encountered with. Suppose that a top management team holds monthly meetings to review the results of the past month. Their key goal is to maximize the company’s dividends and profits.
Hence the team needs a tool that would display the historical profit trend with some other key indicators for the reporting period. The tool is needed to understand where and how profit is formed, and what are the main drivers for profit growth. We suggest using an analytical dashboard as such a tool.

Problem Statement

Our goal is to design and create a Dashboard using the Superstore Sales data (which is really close to reality) to provide answers to the following questions:

1. What are the performance indicators values for the past month? It’s necessary for stocktaking and comparing it against the same period last year.
2. What key factors do affect profit growth?
3. What categories, subcategories, products and clients generate more profits, and what ones that bring losses?

Reviewing Data

The data contains information about customer purchases (Orders list) and returns (Returns list). The purchasing data includes all available information on orders: record ids, order dates, order-processing priority, number of items, sales and profit margins, discounts, shipping options and prices, customer data, and other useful information. But are only interested in the Orders list.

Snippet of the Orders list

Designing a Layout

We’ll position the header with a brief description on top of the page. Then, goes the time-based filter on par with the header. And the subheading “KPI” on the next line.

First of all, we want to generalize key changes according to the factoids:

• Profit and YoY growth
• Sales and YoY growth
• Orders count and YoY growth
• Avg Discount and YoY growth
• Number of customers and YoY growth
• Sales per Customer and YoY growth

Below will be a graph presented as a tree-like map (or equivalent) with top regions by sales count. It will be comprised of different rectangles, the size will correspond to sales volume while the color to profits made. This brings more clarity and helps understand which regions are most effective. It would be great if the reviewed BI tool would provide expanded information upon clicking on a region so that we could see the difference between regions.

More to the right will be a graph with a historical profit trend, displaying how profits change over time. We will try to dot the reviewed month and the same month last year to trace a trend.

Next is products and customer segments. The horizontal bar chart on the left side will be displayed sales volume and profits arranged by categories and subcategories. And try adding a filter for top product names by profit if the BI tool functionality allows so.

Learn more about how to build an interactive waterfall chart

On the right is a horizontal bar chart with top products sorted by profit
volume.

On the bottom of the page, there will be a horizontal bar chart displaying most lucrative clients. It’s very similar to the previous one, but instead of product names will be shown names of customers grouped by their segment and amount of generated profits.

To sum it up, our dashboard layout will look something like this:

Dashboard draft layout