A Global Landscape of Unicorns Around the World

After completing the Power BI modules from Udemy and participating in the first data analytics challenge hosted by Maven Analytics, this became one of the initial datasets I worked on.

Objective

To analyze the following KPI metrics:

• Identify unicorn companies with the highest return on investment

• Determine the timeline for transforming an entity into a unicorn

• Map the concentration of unicorns across the globe

• Identify key investors who have funded the maximum number of unicorns

• Analyze growth patterns

• Evaluate unicorn company valuations and funding patterns

Here is the breakdown of my thought process

Data Extraction Process:

In this project, Power BI's Power Query ETL (Extract, Transform, Load) feature was utilized to import data in CSV format.

Data Cleaning & Validation Process:

The initial steps involved verifying the correct data types of all columns and ensuring the promotion of headers within the Power Query editor. Subsequently, the table was renamed "Unicorn Companies" for identification purposes. Data type adjustments were made, specific column values were replaced, and an ID column was introduced for data modelling and identification. A duplicate table was created, denoted as the "Investors Table," wherein all columns except the ID, Company Name, and Investors columns were removed, and further modifications were applied.

Data Modelling Process:

Following the data modifications, both tables were imported into the Power BI canvas for data modelling. Leveraging the ID column in both tables, a one-to-many relationship featuring a one-way filter was established by linking the ID column of the Unicorn Companies table to that of the Investors Table.

Data Analysis and Visualization Process:

Calculated columns and measures were also generated to facilitate the analysis of the requisite Key Performance Indicators (KPIs). The visualization of these KPIs entailed the creation of a dashboard comprising diverse cards, charts, and graphs, complemented by the incorporation of filters to manage data flow in alignment with analytical requirements.

The static image of the resulting dashboard is revealed below. (Click on the image to view it.)

Your feedback on this project is much appreciated.