This document summarizes a Power BI portfolio analyzing business intelligence data. It contains visualizations of population data from several countries over time, revenue by country for a sporting goods company, and monthly revenue trends. Additional analysis includes a time series of order data, a correlation between revenue and price, and a dashboard showing highest sales by employee. The portfolio demonstrates Power BI's capabilities for data analysis, visualization and dashboarding using open and sample datasets.

1. BI Analyst
Power BI Portfolio by Aniekan Okono
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Contents
Contents
1. Power BI
1.1. About Power BI
1.2. About PorĔolio
2. Data Analysis & Business Intelligence Analysis
2.1. Overview
2.1.1. Aggregated Visualizaĕon
2.2. Business Intelligence
2.2.1. Revenue by Country
2.2.2. Aggregated Data Visualizaĕon
2.2.2. Revenue by month
3. Time Series, Correlaĕon Analysis and KPIs
3.1. Overview
3.1.1. About this data
3.1.2. Transformaĕon of data
3.1.3. Time Series Analysis
3.1.4. Correlaĕon Analysis
3.1.4. Revenue by Country
3.1.5. Dashboards
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1. Power BI
1.1. About Power BI
Power BI is a suite of business analytics tools used to analyze data and share insights. Monitor business performance and get
answers quickly with rich dashboards available on every device.
1.2. About Portfolio
This portfolio uses open data sources to showcase the use of Power BI. The data sets used contain business metrics from an
unspecified company and population data from the World Bank. Power BI is used to analyse and visualise the data. Since the
free version of Power BI is used, it has certain limitations and restrictions compared to pro accounts.
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2. Data Analysis & Business Intelligence Analysis
2.1. Overview
The first data visualization is carried out with the World Bank’s Country Population data. The data is a subset of a dataset
called "World Data Bank's Population, total". This is the link to the data file.
2.1.1. Aggregated Visualization
Diagram 1.0 below is a visualization of the populations of USA, Mexico, France, Canada and Germany from 1999 to 2014.
I have compared the population of these countries over the said period. The graph represents the comparisons over time.
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Diagram 1.0
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2.2. Business Intelligence
VanArsdel is a company that manufactures and sells sporting goods. The company has offices in the United States and
several other countries. Its sales comprise of US and International sales.
VanArsdel’s sales come from its owned manufactured products, as well as other manufacturers’ products.
Here is the link to the dataset in excel format. For business intelligence purposes, date, product identification number,
revenue, country and units have been extracted. The dataset has 841 150 rows and therefore fits the big data description.
The following columns have been analysed:
● ProductID
● Date
● Units
● Revenue
● Country
As part of the data management process, I have created a new column and calculated the price from the revenue and units
data.
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2.2.1. Revenue by Country
Diagram 1.1 below showcases revenue by country. France has the highest revenue at $314.075, Germany the second highest
at $234.007, Mexico the 3rd highest at $217.001,and Canada the lowest revenue at $75.032.
Diagram 1.1
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2.2.2. Aggregated Data Visualization
Diagram 1.2 aggregated various graphs to be used in visualizing different aspects of the data like location, revenue by
countries, units sold and prices.
Diagram 1.2
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2.2.2. Revenue by month
Diagram 1.3 below indicates the revenue generated throughout the year. It can be seen that May was the most profitable
month with $115.008 while January was the least profitable with $30.016.
Diagram 1.3
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3. Time Series, Correlation Analysis and KPIs
3.1. Overview
The below analyses are from an Odata source, created for visualization purposes only. The data can be found at
http://services.odata.org/V3/Northwind/Northwind.svc/
3.1.1. About this data
The company details of the data are not revealed. The data contains information on the international orders of a company.
The dataset is a typical example of what product or service companies require. At typical order database contains the
following information: Orders (Customer, Date, ShipAddress), Order_Details (OrderID, Product, Quantity, UnitPrice,
Discount)
3.1.2. Transformation of data
Excess columns are stripped away from the data to enable the analysis of the most important business related insights. The
date of the order, location, employee (sales owner), quantity, year and month of order are all key details extracted from
the data to use in providing key insights.
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3.1.3. Time Series Analysis
Power BI provides a powerful time series analysis. Diagram 1.4 is used to visualize an analysis of the orders over time.
Diagram 1.4
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3.1.4. Correlation Analysis
Diagram 1.5 below shows a correlation between the revenue generated (price multiplied by quantity) and price per unit.
Higher prices brought more revenue whereas items with lower prices had more orders.
Diagram 1.5
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3.1.4. Revenue by Country
Diagram 1.6 below shows that the USA brought in more sales revenue compared to other countries. Germany also crossed
the $200.000 revenue mark. The revenues are labeled on the x‐axis while the countries on the y‐axis.
Diagram 1.6
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3.1.5. Dashboards
Diagram 1.7 below shows that an employee by the name of Margaret brought in the highest sales revenue. The dashboard
can be used to visualize various aspects of the data. Reports can be generated and shared via the Power BI platform.
Diagram 1.7
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