The 'classicmodels' schema is a database structure that covers various operational aspects of a company, including product sales, customer management, and internal company details. This schema consists of various tables such as 'customers', 'employees', 'offices', 'orderdetails', 'orders', 'payments', 'productlines', and 'products' that are interconnected. While this schema seems to be well-designed, there are some potential issues that may arise. For example, if there is no proper handling of missing or inconsistent values within the table, this can lead to errors or bias in data analysis. Also, if there is a lot of redundant or unnecessary data, this can affect the efficiency and performance of the database. Therefore, it is important to perform thorough data cleaning and pre-processing before performing data analysis or visualization.

In this project, I tried to gain insights into Classic Models' sales data by creating a comprehensive visualization using PowerBI. The business understanding stage involves identifying key metrics critical to assessing company performance, such as sales value, cost of sales, and order quantity. So I tried to extract data from the classicmodels database schema, focusing on relevant columns such as orderDate, orderNumber, productName, and others.

During the data understanding stage, I try to analyze the extracted data to identify trends, patterns and anomalies. This helps in preparing the data for the next stage. In the data preparation stage, I try to clean and transform the raw data to make it suitable for analysis. This involves handling missing values and ensuring consistency in the dataset.

The culmination of this process is in the data visualization stage where I use PowerBI to create interactive dashboards that display various visual representations of our cleaned data. These dashboards include a bar chart showing the sales overview by product type (7 product types); a dot plot showing the comparison between sales proceeds by cost of sales; a piechart showing total sales proceeds transactions by country of office; and a bar chart showing sales proceeds by customer country.

On the second page of the visualization, there is a "Sales Overview" table that presents a monthly breakdown of sales, including Sales Value, Sales Volume, and Sales Value YTD. This table provides a clear picture of the company's sales performance over time, allowing stakeholders to track sales trends and patterns. In addition, there is also a "Customer List" that displays details about customer names, products purchased, as well as transaction values. This information is crucial for understanding customer preferences and can be used to design more effective marketing strategies. Thus, these visualizations not only provide valuable insights into sales data, but also help in informed business decision-making.

This comprehensive visualization allows stakeholders to easily interpret complex datasets thus aiding in the informed decision-making process. It highlights trends in sales value across various parameters such as productLines or geographical locations which can be an important instrument for strategic planning.