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Adventureworks Bike Sales Analysis

Problem Description

İmagine a bike seller company called Adventureworks, which has decided to conduct data analysis to increase sales and ensure customer satisfaction. The company wants to use past sales data to understand which products are more popular, in which regions more sales are made, and to understand customer behavior. This analysis will help the company develop its marketing strategies, optimize inventory management, and strengthen customer relationships. Adventureworks wants to determine which products appeal to which customer segments and predict future sales using this data. Therefore, the data analysis team at Adventureworks will examine past sales data to find answers to these questions and gain insights that will support the company's strategic decisions. Our task is to analyze Adventureworks' past sales data to find answers to these questions and gain insights that will support the company's strategic decisions. This analysis will help us understand which products are more popular,

As a data analyst, we will use various data analysis techniques to explore the sales data, identify trends, and make informed recommendations to the company. We will look at sales by product category, region, and customer demographics to uncover patterns and insights that can drive business growth.

Our first step will be data ingestion, where we will load the sales data into our analysis environment. Next, we will perform data cleaning to ensure that the data is accurate and ready for analysis. After that, we will conduct exploratory data analysis (EDA) to identify trends and patterns in the sales data.

After complete SQL analysis, we will build executive Tableau dashboards to visualize our findings and make it easier for stakeholders to understand the insights we have uncovered. For Tableau dashboards, we will again use SQL to create data marts that will feed into our visualizations. Finally, we will present our findings and recommendations to the company's leadership team, providing them with actionable insights that can help drive sales and improve customer satisfaction.

In summary, our goal is to analyze Adventureworks' past sales data to identify trends, understand customer behavior, and provide actionable insights that can help the company increase sales and improve customer satisfaction. Through data analysis and visualization, we will support the company's strategic decisions and contribute to its growth and success.

Project Structure

  1. Data Ingestion: Backup the .bak file and restore it to the SQL Server. Then, connect to the database and load the sales data into our analysis environment.
  2. Data Cleaning: Perform data cleaning to ensure that the data is accurate and ready for analysis. This involve handling missing values, correcting data types, removing duplicates and unncessary columns, and ensuring data consistency.
  3. Exploratory Data Analysis (EDA): Conduct exploratory data analysis to identify trends and patterns in the sales data. This will involve analyzing sales by product category, region, and customer demographics to uncover insights that can drive business growth.
  4. SQL Analysis: Use SQL to perform in-depth advanced analysis of the sales data, creating queries to answer specific business questions and uncover insights that can inform strategic decisions.
  5. Tableau Dashboards: Build executive Tableau dashboards to visualize our findings and make it easier for stakeholders to understand the insights we have uncovered. This will involve creating data marts using SQL to feed into our visualizations, and designing dashboards that effectively communicate our findings.

Skills Used

  • SQL: For data ingestion, cleaning, and analysis.
  • Tableau: For creating executive dashboards to visualize our findings.
  • Data Analysis: For exploring the sales data, identifying trends, and making informed recommendations to the company.
  • Data Cleaning: For ensuring that the data is accurate and ready for analysis.
  • Data Visualization: For effectively communicating our findings through Tableau dashboards.
  • Business Intelligence: For understanding the company's needs and providing actionable insights that can drive business growth.
  • Quarto: For documenting our analysis process and findings in a clear and organized manner.
  • Communication: For presenting our findings and recommendations to the company's leadership team in a clear and compelling way using the dashboard and self-hosted quarto website through Github Pages.

Sources and Requirements

  • Adventureworks Sales Data: The primary source of data for this analysis will be the sales data from Adventureworks, which will be ingested from a .bak file and restored to the SQL Server.

  • Data Source: AdventureWorksDW2022.bak

  • DBeaver: SQL Queries will be executed in DBeaver.