We continue the series of materials on BI-systems and today we will have a look at the dashboard prepared in PowerBI using the SuperStore Sales dataset. We will cover how to connect the data to the system, set custom colors for visualizations and create new measures, implement switching between charts using bookmarks and we will discuss the challenges that we faced when building the dashboard.

This is the how the final dashboard looks like:

The most notable feature of the dashboard is data cards that show the company’s KPI. The cards compare the parameters to the same period in the previous year and show the previous year’s dynamics in the background.

Below we can see the chart that shows top-performing provinces. The bluer the rectangle the more profitable the province, the more orange the rectangle the more losses the province sustains. The size of the rectangle corresponds to the quantity of sales. We can click on rectangles to see more detailed information about profits and sales dynamics in the region on the graph on the left and their KPI at the top. On the graph, there are green and blue points that indicate the month of the current year and the previous year respectively. Hovering over these points, you can see a trend line.

The next part of the dashboard shows product and customer analysis. This part allows us to answer questions such as “which products were the most profitable or unprofitable” or “which customers contributed to most of the profits or most of the losses”.

Data collection

To connect the data we used an Excel file. PowerBI offers a number of sources to connect your data from such as Excel, csv, json files and various databases.

Configuring reports and visualizations

When building a dashboard in PowerBI we wanted to copy the color themes from Tableau. To do this, we have created a JSON file with the list of colors that we want to use. You can see the content of our file below. Then in the views tab, we clicked on the “browse for themes” button and uploaded our colors.

{
	"name":"Orange-Blue Diverging",
	"dataColors": [
		
		"#1c5998",
		"#1c73b1",
		"#3a87b7",
		"#67add4",
		"#7bc8e2",
		"#cacaca",
		"#fdab67",
		"#fd8938",
		"#f06511",
		"#d74401",
		"#a33202",
		"#7b3014",
		"#F07C28",
		"#2B5C8A",
		"#94C6E1",
		"#87d180",
	]
}

Then we have created a separate table called Calendar and populated it with all order dates. After that, we created a column with just a month and a year to create a filter based on it.

Creating necessary measures

When creating a dashboard with PowerBI we often need to create new measures. For the data cards, we created such measures as Total Profit, Total Sales, Total Orders, Total Clients and so on. The arrows that you can see in the data cards are also customized and a measure was created for each of them. To apply the color to arrows we formatted the color by rules and indicated red if the value is less than 0, green if the color is more than 0.

Adding bookmarks to switch between charts

To switch between charts, we added bookmarks for sales and profits. For the sales chart, the profits bookmark is hidden and vice versa. The button was downloaded from the internet and added to the respective bookmarks.

Interesting features and challenges we faced when building the dashboard

We have created custom data cards for KPI which are different from the default ones offered by PowerBI. The original features of cards include the background trend, the name and value while the arrows and changes are a custom feature. Another interesting feature that we used is cross filtration which allowed us to apply the filter to both the profits/sales chart and KPI cards.

One of the challenges that we have faced was the inability to build a bar chart with 2 categories. This feature was not implemented in PowerBI at the moment of writing this overview (maybe it is implemented now), so we had to create a table and add bar charts into it. Similarly, we inserted bar charts into the Top Customers table.

Conclusion

Our team has evaluated the dashboard and has given the following scores from 1-10 scale (10 being the highest) to this dashboard:

1. Meets the tasks – 9.8
2. Learning curve  – 3.0
3. Tool functionality – 9.5
4. Ease of use – 7.5
5. Compliance with the layout – 9.5
6. Visual evaluation – 8.8

Overall: 8.0 out of 10. Have a look at the final dashboard here.

This year I succeeded in becoming a Tableau Desktop Certified Associate. When I was thinking about how to prepare for the exam, I came across e-learning courses from Tableau that turned out to be free for 90 days.

I decided not to waste such an opportunity and complete all the 3 modules in Fundamentals at a fast pace. When I got certified, I was wondering which programs are offered by other producers of BI tools. First things first, I decided to study training materials on PowerBI. In this small article, I would like to compare Tableau and PowerBI training programs.

Disclaimer: in the end, I have formed an unfairly prejudiced and positive attitude towards Tableau, so PowerBI supporters may not like this article and find it biased (in all fairness, there are also words of praise for PowerBI).

After having studied the training materials, I can finally state the reasons why I am definitely in favor of Tableau as a tool for data analysis and visualization.

First of all, there is a huge gap in the approach to materials and the assessment of their understanding. Although Tableau training materials are more technical and pay less attention to design, by studying through their videos you can do excellent visualization. After completing all three steps of Tableau training, a strong desire to create new stunning reports with the use of LOD Expressions, Filter Actions, and make convenient interfaces arises. However, after watching all the materials on Power BI the only question that remains is why did I waste my time?

Emotions aside, there are several key points that turned out to be important after having studied the material.

This is a good dashboard according to Microsoft

The quality of content and training examples

If you consider the way training videos are presented in Tableau and the questions in a quiz format that are posed at the end of the covered material, you start understanding the idea of the software. But in the case of Power BI, you will be totally disappointed. Have a look for instance at the material for identifying outliers, here Microsoft suggests building a scatter plot and visually identifying all the outliers.

Design of reports and dashboards

There is some objective criticism towards Tableau training materials on the topic of graph design and control elements, but they are still neatly and beautifully made. Now have a look at the dreadful thing that Microsoft suggests as the result of the analyst’s work. And this is a well-built dashboard according to Microsoft.

Assessment of the knowledge gained during the training

During the training at Tableau, immediately after a small lecture, you learn by applying the part of the studied material in practice. You need to click certain buttons in the interface to solve a problem. Power BI offers “labs” that are supposed to be launched from a remote machine. I didn’t manage to start a single lab; I wrote to the support 3 times and the support couldn’t solve my problem so I didn’t manage to experiment over the PowerBI tasks.

The results of the analyst’s work according to Microsoft.

Other points are mostly related to the software rather than the training program.

Cross-platform support

I have been working with Tableau for a long time and 4 years ago I switched to Mac. After the transition from Windows, my experience of using Tableau did not change. In fact, Tableau was developing and I was developing with it, but the team did not change the key elements of the interface. I have been experimenting with building reports in PowerBI, but I was uncomfortable with different Microsoft archaisms like publications through some share-portal where you need to have an MS account and configure something through the administrator. All of this was a terrible headache.

However, what struck me so much was that I could not use PowerBI on a Mac. There is absolutely no way and this is a principled stance of Microsoft which is not expected to change in the future. From my point of view, such software belongs to a B2B segment in the field of analytics, assumes the connection to all kinds of DBMS, but denies the existence of an alternative operating system which could be used by a number of potential consultants that could use and promote PowerBI as an analytical tool.

Most certainly, there are some rational reasons why any software from Microsoft doesn’t work very well on Mac, but the simple truth is that for me the software remains inaccessible. Nevertheless, I wasn’t looking for an easy way out and installed PowerBI through Parallels in order to honestly consider the tools again taking into account the training materials.

Visualization options

Both Tableau and PowerBI offer stunning visualization options. In fact, in this regard, PowerBI offers a video with a little more information than usual. So, on this matter, the tools are presented equally well.

Functionality

Here I want to give credits to the functionality of PowerBI. In fact, the variety of tools is extremely wide even without connecting third party libraries. For example, automatic clustering, decomposition tree, data profiler and setting filters on a graph.

Internal language syntax

To work with PowerBI you need to learn DAX. It is not a programming language, but a functional language. You won’t be able to write your own code, however, you won’t even need it – all the functions are already implemented, so you should only learn how to use them. Microsoft tells about DAX quite well in the manual. Definition of a new measure in DAX looks like this:

Revenue YoY % =
DIVIDE(
	[Revenue]
		- CALCULATE(
			[Revenue],
			SAMEPERIODLASTYEAR('Date'[Date])
	),
	CALCULATE(
		[Revenue],
		SAMEPERIODLASTYEAR('Date'[Date])
	)
)

Preparing data for the analysis

Inside PowerBI there is a Unpivot feature that allows bringing the data in columns with time periods into the form that is convenient to use in pivot tables.

However, an ETL tool for data cleaning and wrangling in Tableau Prep also has this feature implemented.

Conclusions:

1) The training programs are built in completely different ways, the methodology of immersion into Tableau tools is more elaborate and efficient. There is an opportunity to get practical experience of solving problems and get feedback (albeit automatic).
2) Reports and dashboards design in training materials from Microsoft hardly look professional while Tableau’s implementation is much better.
3) Knowledge assessment at Microsoft is implemented at the abysmal level (absolutely perfunctory tests like in a bad school) while at Tableau it’s much better implemented, you dive into the problem, think about the answer and solve it.
4) Cross-platform support is not PowerBI’s strongest point, however in the case of Tableau it’s an excellent competitive advantage.
5) The functionality and capabilities of the tools are certainly at the highest level, and in some points, PowerBI wins.

Have a look at our dashboard reviews in Tableau and other BI tools.

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?

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