Sales Report For Adventure Works Company

Background

As a data analyst, I was assigned to create a report using PowerBI for Adventure Works, a company that provided a dataset consisting of staff, order items, calendar date, customer, products, and orders. The aim was to extract insights from the data and present them in a visual format that is easy to understand.

Goals

The primary goal of this project was to analyze the data provided and extract insights that could help Adventure Works make informed decisions. Specifically, the project aimed to identify the trends in revenue and the correlation between revenue and SalesOrderDetailID. Additionally, the project aimed to examine the distribution of revenue by country and explore the relationship between SafetyStockLevel and revenue.

Methodology

To achieve the project's goals, the first step was to transform the raw data into a data model using PowerBI. Next, I created visualizations that would help me identify patterns and trends in the data using PowerBI

Overall, the methodology involved transforming the raw data into meaningful insights using visualization tools in PowerBI. The insights obtained from the analysis could help Adventure Works make data-driven decisions that could positively impact its revenue and business operations. It is important to state that Time intelligence was added to the data model.

Time Intelligence in Power BI refers to a set of features and functions that enable users to perform calculations and analysis on data based on time periods and trends. Time Intelligence features allow users to create measures and visualizations that compare data across various time periods such as days, weeks, months, quarters, and years.

Some examples of Time Intelligence functions in Power BI include Year-to-Date (YTD), Quarter-to-Date (QTD), Month-to-Date (MTD), Last Year (LY), and others. These functions enable users to perform calculations on data that are relative to a specific time period or trend.

Time Intelligence in Power BI is useful for analyzing trends over time, identifying seasonal patterns, and comparing performance against benchmarks or targets. It is commonly used in business intelligence, financial analysis, and other data-driven applications where time-based analysis is critical.

For a detailed analysis, you may want to reach out to me via my LinkedIn as seen on my profile.