Logic Logistics Report
The stakeholders of a logistics company called logic logistics wishes to visualize it data asset and understand:
- the total number of cars in its collection
- the number of cars belonging to a particular type
- the minimum present price for each car belonging to a particular type
- the maximum present price for each car belonging to a particular type
- the total present price for cars belonging to a particular type
- the total present price for the total number of cars in the collection
- the minimum selling price for each car belonging to a particular type
- the maximum selling price for each car belonging to a particular type
- the total selling price for cars belonging to a particular type
- the total selling price for the total number of cars in the collection
- the most expensive car by present price
- the least expensive car by present price
- the most expensive car by selling price
- the least expensive car by selling price
- the mileage in kilometers covered by each car belonging to a particular type
- the profit/loss incurred overtime
- how the present price and selling price progressed overtime.
The data asset is on-premise and stored as csv file. The data was collected and loaded directly into Power Query for transformation using Microsoft Power BI.
The data set being denormalised, refactoring into dimensions and fact tables was imminent. The permanent data representing the business entities was split into dimension tables:
- [Cars]
- [Seller Type]
The continuous activity of the business was split into fact tables:
- [Sales]
- [Profit_and_Loss]
In the [Profit_and_Loss] table, DAX was used to create a calculated column named 'Profit/Loss'
Each column in the tables were validated to have the correct data type in order to ensure data accuracy.
From findings, the business has been underperforming and incurred a loss of $70M dollars overtime.
The selling price of the cars should be raised above the present price to bring about profit to the business.