Fiscal Year 2022 Revenue Dashboard Built on Microsoft Power BI
Are you the kind of person who sets resolutions at the start of the new year? Or do you have your own milestone timeline? Well, I’m the kind of person that falls in the second category and currently, my goal is to build a strong data analysis portfolio that lands me great opportunities.
Talking about the new year reminds me of businesses around the globe that compile their “Turn Around”, “Revenue”, “Burn Rate” and other business parameters, so I have built a dashboard (The fiscal year 2022 Report) of a service-offering company from Saudi Arabia.
Note: for this project, I have taken inspiration from Other levels and the data has been taken from this website
The data is comprised of three tables namely “location”, “Request Status”, and “Service Type” all of which have a 1-to-1 relation with each other.
For this project I have used the following tools and Techniques:
Excel
Microsoft Power BI Desktop
Dax
DAX Formatter by SQLBI
Microsoft Power BI Service
Canva
Loading Data:
So, the first step is to load data in Microsoft Power bi, we can import data in multiple ways and the easiest will be to go for “import data from excel” at the center of the Power BI interface.
When done with loading all three datasets, I checked the data and the data is already cleaned and ready to be used for visualization. Here one more thing to keep a check on is the relation of tables that I stated above has a 1–1 relation, so if it’s not you have to make it one to one relation by connecting attributes.
Here’s what it would look like:
Dashboard Development
Now starting with building the dashboard,
So the first thing is to design the look and feel of the dashboard, I have used 3 shapes for this dashboard first layer is blue which you can see in the background then the upper darker shade of blue, and the third layer with a black shade. For color choices I have used color space, it’s quite handy in deciding on complementing colors.
The first visual that I built was the line chart showing to the number of requests per month for this I have taken “Request Number” as the X-axis and “Date” by month as the Y-axis both from the locations table.
And to make it more illustrative I have added a measure “Avg of Daily Request” on the top of it to show the average number of requests per respective date.
Here’s the DAX code for the measure:
The next illustration on the dashboard is the request table that has fields as “Request Number” and “Request Status”.
The table shows the number of completed, canceled, and pending requests.
After that, I felt the need to present the total count of requests so have added another measure “Total Daily Request”,
And here’s the DAX code:
Have used the filter function to calculate values of matched dates by day.
And the graphics over which the measure is shown has been designed in Canva.
Then I used another line chart to show the paid amount against months and for this, I used the sum of paid amount attributed to request status and the date by months from locations.
Next, I have added clustered column chart to draw a comparative analysis between “Paid amount” and “Quotation amount”, quotation amount is a fixed amount that is asked by the contractor or supervisor that can’t be changed in the future. So, in this context, it means that the amount will be given in the near future subject to the completion of the project.
Then I have added a slicer that helps us filter out the data according to month, here’s how it impacts the overall data:
Afterward, I added two rectangular shapes to tally down “Service Type Count” and “Service Type Amount”.
On both sides I have used the Donut chart,
for the Service Type Count:
Have used count by service type which shows that 53% of services are of advertising type and 47% are of financial type.
Underneath the donut chart, I have shown the total count of both request types.
for Service Type Amount:
I have used count by service amount and here again, advertising is contributing denominatively by 58%, and financial is contributing by 42%.
This tells that advertising service type is not just popular but also profitable.
Next, I used a map that has latitude and longitude from the locations table and provinces as fields.
One thing to note here is that here provinces are taken in context as states so the table shown over the map has shown the revenue generated respective to the states.
In table, you have to simply add the paid amount and change the settings to percentages and again add paid amount with the presentation type as a sum.
And this is all I have used other than text boxes that you can use by yourself.
Let me know how you find this dashboard and what improvement you see that can be done in this dashboard, to see and know more about these kinds of dashboards follow me on maven analytics, I regularly update my projects here: