DELUXE UK RETAIL SALES DASHBOARD

Introduction

Data cleaning is a very fundamental aspect of data analysis. It involves detecting inaccuracies in data for good result. Data cleaning serves as the bedrock for a good analysis and visualization.

About the data

This data consists of 1534 rows and 10 columns with rows consisting of information about sales of product by a retail UK company(Deluxe) across various countries. The data was gotten from kaggle named as “sales_export 2019_2020.csv” which consists of Dates, Order_value_EUR, Cost, Category, Country, Customer_name, Device_type, Sales_manager, Sales_representative, Order_id.

Goals

The goal of this project is to learn, improve in data cleaning, preparation, exploration skills. Its also geared at building a worthy project.

My Data Cleaning and Preparation Procedure

The imported data was cleaned basically using the power query editor in PowerBI. To ensure the data is in good shape devoid of special characters or unnecessary spaces that could hinder the smooth analysis of the data, i “cleaned and trimmed” the text columns. Also important is checking the data quality and making sure its devoid of duplicates and blank spaces i used the “view” column in the power query editor, and the anomaly in that regard was also fixed.

In also making sure the data information(headers and what have you) is devoid of any ambiguity, i renamed the headers to truly project its connotation, For example the column “Order_value_EUR” was renamed to “Selling Price”. Also in exploring the data, i created a new column name “Profit” which is gotten from the deduction of the “Selling Price” and “Cost Price” as i found it important to the analysis.

Deductions

(1) From the chart titled “Top 5 profit by sales rep” it was deduced that “Corene Shirer” generated the highest profit for the company with a revenue in profit totalling E1,187481.43 euros.

(2) It was also determined that Portugal yielded the highest sales profit among all the countries.

(3) Also it was deduced that “clothing” as a product category garnered the highest profit by sales.

(4) It is also seen that profit climaxed around june for both years.

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