A complete beginners guide on Power BI

#powerbi #datavisualisation #dataanalytics #beginnersguide #inueron

Prasanna Oct 22 2020 · 3 min read
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Hey there ! I am excited to share my first ever blog on power BI and It is not far for you to write your own blog either. Power BI is a powerful data visualization tool that is widely used in the analytics field. The most exciting part is that it is absolutely free to have a desktop version. So why wait? download it and lets dive further !

Power BI let's you load data from Excel, Text/CSV, XML, JSON, PDF, mutiple databases, Power Platforms, Azure, online sources and multiple other sources.

In this article, let's load a transport data set stored in my local folder to power BI, do a basic data transformation using power query and visualize it.

Load Data:

Power BI home -- Get Data -- Excel -- This let's you browse your local folder, select the right excel file and click OK.

Select the appropriate Work Sheet -- Click Transform Data -- This loads the data to power query and let's you make data transformations.

Transform Data:

"Now the data is loaded? Is it appropriate to start visualizing right away? A big NO !"

To get accurate visualizations, data exploration and basic data transformations are vital before they are visualized. Power query lets you impute missing values, change appropriate data structures, add new columns based on available columns and multiple other useful functions. 

Another beautiful feature of power query is that,  it captures all the changes made by the user in the 'Applied Steps' and  users can revert any changes made by a simple click.

Our loaded data looked clean and not much of transformations were required. Let's jump in to visualization.

Visualize the Data:

Okay ! the wait is over. Lets visualize !

A good report is not based on how fancy it looks, it is based on how simple and informative it can be!

The transport data set for the company 'Sky Transport' included multiple columns with information about their routine business. Our focus is on the key columns like their top customers, revenue, total miles covered and top serviced states/cities.

(i) Top level Overview:

The below includes type of visualization used and the obtained results

A 'table' with only columns like customer, trip type, total miles, revenue provides complete information to a top level stake holder to understand their top customers. We notice customer 'Quotefix' is the top revenue contributor, followed by Lazap & Zonecone.

A visualization 'card' clearly shows the total miles covered and total revenue earned. Don't you think this is something every stake holder would love to see at first sight?

A 'piechart' showing revenue & miles based on different trip types, clearly indicates the customers of Sky Transport prefer domestic trips followed by international.

A 'Slicer' is my personal favorite option, that let's users to see reports of individual customers and their preferences. 

(ii) Detailed Analysis:

Here's the second report with detailed analysis for Sky Transport.

'A line and clustered column chart' shows the average revenue trend is static over the period. This is a strong indicator for Sky Transport to figure out the cause and take necessary measures to increase revenue over the periods.

A 'map' helps to visualize the top locations best than any other visualization tools. Here we see, the major business for Sky Transport happens in Illinois, followed by Michigan & Wisconsin.

A 'clustered chart' is always a simple and effective tool. Here we see the shipping city with top revenue and miles covered. Elgin being the top shipping city for Sky Transport.

KPI's are powerful visualization tool that can show the actual vs target values and monitor them regularly. Here the KPI's only show the actual's, as the target values are not provided.

The reason 'slicer' being my favorite is because, just selecting a random customer 'acequote', gives me every detail about the customer.

Woohoo ! you just learnt how to extract, transform, load a data set & create basic visuals using power BI. 

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