liewjunyen-dataanalyst/business-intelligence-bi-giant-bicycles-organization-academic

This is an academic Business Intelligence (BI) project related to the Giant Bicycle Organization, published by Liew Jun Yen - Data Analyst

Introduction

The Business Intelligence (BI) project is assigned by Asia Pacific University Malaysia to Giant Bicycles Organization. In this academic project, I will act as Business Intelligence Consultants and work in a simulation to assist Giant Bicycles Corporation with the implementation of BI systems, which include SQL Server Management Studio (SSMS), Microsoft Visual Studio, SQL Server Analysis Services (SSAS), and Microsoft Power BI. The project involves using a simulated data warehouse backup file from Giant Bicycles Organization, known as "Trek Bikes DataWarehouse.bak", provided by Asia Pacific University Malaysia to complete the project.

Aim & Objectives

Aim: To perform the role of a Business Intelligence Consultant, providing a better understanding of the overall online sales performance to the Senior Leadership of Giant Bicycles Corporation, as well as offering new insights and recommendations to the company based on the findings.

Objectives:

• To extract and restore the complete dataset from Giant Bicycles Corporation’s data warehouse into SQL Server Management Studio (SSMS).
• To design an online analytical processing (OLAP) cube with key sales performance dimensions, deploy it using Microsoft Visual Studio with SQL Server Analysis Services (SSAS), and establish a connection to Microsoft Power BI.
• To design and launch an interactive Power BI dashboard with at least 3 different visual components, culminating in a strategic recommendations report by the twelfth week.

Analytic Tools:

• Microsoft Power BI
• Microsoft Visual Studio
• SQL Server Analysis Services (SSAS)
• SQL Server Management Studio (SSMS)

Data Source

• Data Source: Trek Bikes DataWarehouse.bak
• Description: Giant Bicycles Organization's data warehouse consists of dimension tables and fact tables. It contains data from the period 2010 to 2014.