Reference no: EM133746846
Business Intelligence
Assessment Item 1: Case Study analysis: Investigation of business intelligence, decision making and decision support systems
Introduction
This assignment necessitates the analysis of a dataset, the interpretation of findings, and the presentation of conclusions through a written report. It is imperative that you complete this assignment on an individual basis and submit it electronically via the Learning Management System (LMS) before the specified due date. Ensure that you follow the LMS instructions to verify the correct submission of your work. Please note that we do not accept hard copies or assignments submitted via email. The assignment relies on the dataset found in the file Assignment1_RetailStore_Dataset.xlsx, which can be downloaded from LMS.
Case Study: Retail Store Data Set:
Supermarkets are on the rise in densely populated urban areas, leading to heightened market competition. This data set represents historical sales data from a supermarket company with records from three different branches over a three-month period. Utilizing predictive data analytics techniques with this dataset is highly accessible and straightforward.
Data Description:
The "Data Description" sheet describes all the variables used in the "Retail Store Dataset" and is copied below for your convenience. Invoice id: Computer generated sales slip invoice identification number
Branch: Branch of supercenter (3 branches are available identified by X, Y and Z). City: Location of supercenters
Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card. Gender: Gender type of customer
Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports, and travel
Unit price: Price of each product in $
Task:
The task of designing a comprehensive Decision Support System (DSS) for a retail business based on the retail score dataset is a multifaceted assignment that requires students to apply their knowledge and skills in the domain of business intelligence and data analysis.
Let's elaborate on this assignment:
Designing a Comprehensive DSS:
Understanding the Retail Score Dataset: To begin with, students should thoroughly understand the given retail score dataset. This entails examining the dataset's structure, variables, and the kind of information it contains. They should also consider the specific objectives and needs of the retail business in question.
Defining DSS Components: Next, students need to design the components of the Decision Support System. A DSS typically includes various elements, such as a database, user interface, analytical tools, and reporting capabilities. Students should explain how each of these components will be integrated into the system.
Data Integration and Transformation: The retail score dataset might not be in the ideal format for decision support. Students should describe how they will integrate the dataset into the DSS and what preprocessing steps, like data cleansing and transformation, will be necessary to make the data suitable for analysis.
Analytical Tools and Algorithms: The heart of the DSS lies in its analytical capabilities. Students should select and justify the specific analytical tools, algorithms, and
models they will use to extract insights from the data. For example, they might opt for clustering algorithms to segment customers or time series forecasting to predict sales trends.
User-Friendly Interface: Designing a user-friendly interface is critical. Students should discuss how they plan to present the data and insights to end-users, which may include retail managers and executives. This interface should be intuitive and facilitate data exploration and decision-making.
Aiding in Strategic Decision-Making:
Identifying Key Business Objectives: Students should define the strategic objectives of the retail business. These objectives could include enhancing customer experience or increasing sales. They need to explain how the DSS will align with and contribute to achieving these goals.
Data-Driven Insights: The core function of the DSS is to provide data-driven insights that support decision-making. Students should illustrate how the DSS will generate actionable insights from the retail score dataset. This could involve identifying customer preferences, forecasting demand, or detecting sales trends.
Scenarios and "What-If" Analysis: A robust DSS allows for scenario analysis. Students should describe how their system will enable users to conduct "what-if" analyses, helping decision-makers explore the potential impact of different strategies or market conditions.
Visualization and Reporting: Effective communication of insights is crucial. Students should outline how the DSS will present findings through visualization tools, dashboards, and reports. Visualizations can make complex data more understandable and actionable.
Monitoring and Adaptation: A good DSS should not be static. Students should discuss how the system will monitor the retail environment, collect real-time data, and adapt its recommendations based on changing conditions.
Overall, this assignment challenges students to think holistically about designing a DSS that leverages the retail score dataset to aid in strategic decision-making. It also highlights the importance of aligning the DSS with the specific needs and objectives of the retail business.
The report's length should be approximately 1500 words (excluding references). Utilize 1.5 line spacing and a 12-point Times New Roman font. Employ both numerical and graphical statistical summaries, as sometimes insights can be gained from one that are not apparent in the other.
Once you have drafted your report, it can be valuable to set it aside for a day and then revisit it with fresh eyes. Read it as if you were unfamiliar with the analysis. Does it flow smoothly? Is it comprehensible? Can someone without prior knowledge understand your conclusions from the written material? This review process often reveals opportunities to edit the report for greater clarity and directness.
Introduction
In this independent assessment, you will leverage the case study presented in Assessment Item 1 as a foundation for your tasks.
Develop the architecture for a business intelligence system and formulate a data warehouse framework.
Employ visual analytics to convey your discoveries. Your work will be presented in the format of a report.
Data Description:
The "Data Description" sheet describes all the variables used in the "Retail Store Dataset" and is copied below for your convenience. Invoice id: Computer generated sales slip invoice identification number
Branch: Branch of supercenter (3 branches are available identified by X, Y and Z). City: Location of supercenters
Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card. Gender: Gender type of customer
Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel
Tasks:
Let's break down the key components of this assessment:
you have access to a dataset that contains information related to a retail store. This dataset likely includes data on sales, customer information, inventory, and other relevant aspects of the retail business.
Designing Business Intelligence (BI) System and Data Warehouse Framework:
Your first task is to design the architecture of a Business Intelligence (BI) system and a data warehouse framework.
Business Intelligence System: A BI system is a set of tools and technologies that help in gathering, processing, storing, and analyzing data to provide valuable insights to support business decision-making. Your role in this assessment is to plan and design the structure and components of this system. You'll need to decide how data will be collected, processed, and presented to the end-users.
Data Warehouse Framework: A data warehouse is a central repository of data that is specifically designed for querying and reporting. You'll need to define how data from the retail store dataset will be stored in the data warehouse. This involves decisions regarding data modeling, ETL (Extract, Transform, Load) processes, data storage technologies, and overall architecture.
B. Utilizing Visual Analytics: Visual analytics is a process of analyzing data through interactive and visual methods such as charts, graphs, and dashboards. In this assessment, you are expected to use visual analytics techniques to analyze the retail store dataset. This means you'll be creating visual representations of data to uncover insights, trends, and patterns. Your findings should help us to understand the retail business better.
Finally, you are required to present your work in the form of a report. This report should document the following:
Your design of the BI system and data warehouse framework, explaining the rationale behind your choices.
Visualizations and insights obtained from the retail store dataset using visual analytics techniques.
Any recommendations or conclusions drawn from your analysis.
The report should be well-structured, clearly written, and include visual aids like charts or graphs to support your findings.
Following the successful completion of these tasks using the appropriate tools, produce an analytical report that leverages visual analytics to convey the insights uncovered to the Retail Store Directors.
The report should span roughly 2000 words (excluding references), adhere to 1.5 line spacing, and employ a 12-point Times New Roman font. Make use of both numerical and graphical statistical summaries, as certain insights may become apparent through one form of representation that might not be evident in the other.
Assessment: Design, implementation, and evaluation of a business intelligence solution
Case Study: Loan Prediction Dataset:
Data Description:
Loan_ID: This is a unique identifier or reference number for each loan application. It is used to distinguish one loan application from another.
Gender: This column likely records the gender of the loan applicant, indicating whether they are male or female. Married: This column may indicate the marital status of the applicant, specifying whether the applicant is married or not.
Dependents: This column typically records the number of dependents or family members financially reliant on the applicant. Education: This column indicates the educational background of the applicant, specifying whether they are educated or not. Self Employed: This column may show whether the applicant is self-employed or works for someone else.
Monthly Applicant Income ($): This column likely records the monthly income of the primary applicant in dollars.
Monthly Coapplicant Income ($): This column probably records the monthly income of any coapplicants, like a spouse or partner, in dollars. Loan Amount ($): This column typically indicates the amount of the loan applied for, usually in dollars.
Loan Amount Term: This column is likely used to specify the term or duration of the loan, such as the number of months for repayment. Credit History: This column may contain information about the credit history of the applicant, often indicating whether it is good or bad. Property Area: This column likely represents the geographical area or location of the property for which the loan is sought.
Loan Status: This column usually indicates the status or outcome of the loan application, such as whether it was approved or denied.
Task:
Tasks - PART A
The major assessment task is a comprehensive project involving predictive and prescriptive analytics on loan prediction datasets, which will ultimately result in the design and implementation of a business intelligence solution.
Let's break down the task and elaborate on each component:
Examination of Techniques for Predictive and Prescriptive Analytics:
In this phase, your group will explore and analyze various data analytics techniques and methods used for loan prediction. This typically involves studying statistical, machine learning, and data mining techniques that can be applied to historical loan data to make predictions about future loans. Predictive analytics aims to
forecast future events, while prescriptive analytics goes a step further to provide recommendations on what actions to take based on the predictions. Your group will need to research and understand these techniques, including the data preprocessing steps, model selection, and evaluation metrics.
Design and Implementation of a Business Intelligence Solution:
After gaining a deep understanding of the techniques, your group will be tasked with designing a business intelligence (BI) solution. A BI solution involves creating a system or platform that integrates and analyzes data to provide valuable insights for decision-making. In this context, it means creating a system that can handle loan data and provide insights into whether a loan applicant is likely to be approved or denied. The design phase involves planning how the system will be structured, what data sources will be used, and how the analytics will be applied.
The implementation phase is about actually building the BI solution. This may involve developing software applications, setting up databases, and integrating various tools and technologies. You'll also need to implement the predictive and prescriptive analytics models that were examined in the first phase. This might include using programming languages like Python or R, and machine learning libraries such as Scikit-Learn or TensorFlow.
Development of Elements of the Proposed Solution:
This component refers to the practical work of creating different components of the BI solution. This could include data collection and cleaning, model training and testing, integration with visualization tools, and the creation of a user interface if necessary. It's the hands-on work that transforms your design into a functional system.
Report and Presentation:
Once the design and implementation phases are complete, your group will need to compile a report that documents the entire process. The report should detail the techniques examined, the design of the BI solution, the steps taken in the development phase, and the results obtained. It should also include insights gained from the analytics, any challenges faced, and recommendations for improving the solution or addressing potential issues.
The presentation component involves summarizing the report's key findings and presenting them to an audience, such as your peers or instructors. This is an opportunity to showcase your work, explain your methodology, and share the insights your solution has generated. Effective communication and visualization of your results are crucial during this phase.
In summary, this assessment task encompasses a full cycle of data analytics and business intelligence development, from research and analysis to the practical implementation and reporting. It's a comprehensive project that allows your group to apply theoretical knowledge to a real-world problem, demonstrating your ability to harness data for decision-making in the context of loan prediction.
Tasks - PART B
Each member of the group will deliver a concise 5-minute oral presentation on the submitted business report and the accompanied visual dashboard.