The purpose of this exercise is to help you gain an understanding of a few accounting ratios,

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Overview

The learning objective of this module is to introduce you to data analytics which employers consistently tell us are important to new accountants. We will be using an EY-developed case, Implementing the DuPont Method, which I have edited for AIS.

 

A key purpose of financial statements is to provide useful information to decision makers, including investors. Investors can use the information contained in financial statements to better understand company performance so they can make better investment decisions. One introductory framework that has been especially useful in understanding company performance is the DuPont Method (sometimes called DuPont Analysis, DuPont Model or DuPont Framework).

 

The project consists of several tasks to implement the analytics mindset in an audit context to learn to:

 

• Ask the right business questions

• Extract, transform and load relevant data (i.e., the ETL process)

• Apply appropriate data analytics techniques

• Interpret and share the results with stakeholders

 

In this case, you will implement an analytics mindset by comparing and contrasting different companies within different industries using the accounting ratios from the DuPont method. Your overall goal is to develop an investment recommendation for an investor.

 

You will need to read and understand the Tableau Case Background document to complete the Tableau Module. It describes the DuPont Method, the history of the model, an overview of the case and provides descriptions of the data file used in the case. You will need information from the case background to complete both Part 1 and Part 2.

 

The data for this case was extracted from company financial statements posted online from credible sources and has been loaded into the Excel file. You can assume that the web scrapper accurately and completely extracted the information and loaded it into Excel. In this part of the case, you will be verifying your data loaded successfully into Tableau, verifying the data loaded correctly, and creating preliminary analytics.

 

Realize for many situations, extracting, transforming, and loading (ETL) the data can account for over 80 percent of the time in the entire data analysis process. This case simplifies this process so you can focus on developing other aspects of an analytics mindset.

 

Part 1 is an introduction to Tableau which you will complete and submit individually. It consists of two tasks to introduce you to the software and the analytics mindset.

 

• Task 1 – Practice asking the right questions by completing the Task 1 Exercise on Blackboard.

• Task 2 – Load the data into Tableau, apply data analytics techniques and develop a dashboard highlighting your analysis.

Analytics mindset case studies – DuPont 1

© 2016 Ernst & Young Foundation (US). All Rights Reserved

 

Required

Task 1: Ask the right questions

 

This task focuses on developing an analytics mindset — Ask the right questions A significant portion of developing your analytical mindset happens before you analyze data.

 

The purpose of this exercise is to help you gain an understanding of a few accounting ratios, which will help you to develop the right questions about the companies you are analyzing. While you already have a fundamental understanding of the DuPont Method and are aware of the data elements available to you, you have not yet determined the best way to analyze the data to provide the most relevant insights. To gather the most relevant insights, you must start by asking the right questions of the data.

 

In this exercise which you will complete on Blackboard, you will think through the DuPont Method in detail, so you understand some of the business context. Read the Tableau Case Background document and then complete the exercise on Blackboard. See Blackboard for a sample worksheet which will help you organize your thoughts before entering the answers online.

 

Task 2: Load data and perform analytics

 

This task focuses on two primary parts of performing data analytics:

• Extract, transform and load process (ETL) – The data extraction has already been performed, and you will complete the transformation and load steps including verifying the data loaded completely and accurately.

• Analyze Data – You will answer specific questions by creating views/visualizations based on the starting data file.

• You will need information from the Tableau Case Background document to complete the ETL and Analytics portions of Task 2.

 

All the detailed steps for the ETL and Analytics portions of Task 2 are listed in the table starting on the page 4.

 

Attachments:

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