Data Analytics in Management Accounting

Curriculum guideline

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Course
ACCT 2315
Discontinued
No
Course code
ACCT 2315
Descriptive
Data Analytics in Management Accounting
Department
Accounting
Faculty
Commerce & Business Administration
Credits
3.00
Start date
End term
Not Specified
PLAR
No
Semester length
15 Weeks
Max class size
35
Course designation
None
Industry designation
None
Contact hours

In person: Lecture and/or Seminar: 1 X 3 hours per week OR 2 X 2hrs per week

or

Hybrid: minimum 50% in person and up to 50% online

or

Online: Synchronous and/or Asynchronous

Method(s) of instruction
Lecture
Seminar
Learning activities

Lecture and/or seminar

All methods of instruction apply to in class, hybrid and/or online modes of learning.

Course description
From financial statements to operating performance, the environment we are living in is “data rich, information poor.” With the decision-making pressure on accountants and managers, it is crucial to gather available data quickly, provide accurate analysis, and present financial information to stakeholders in a meaningful way.

This course covers the basic concepts and techniques of management accounting and combines it with current trends in data analytics as they pertain to management accounting. Topics include: critical thinking, financial modelling, cost behaviour, relevance for decision-making, and budgeting. Students will learn how to analyze both financial and operational data by designing financial models and dashboards, making reasonable financial forecasts, and monitor operational performance using data analytics tools.
Course content

1. Introduction to critical thinking

2. Introduction to managerial accounting

  • Management information and decisions
  • Cost behaviour (fixed vs. variable vs. mixed)
  • Introduction to cost systems (e.g., work-in-process vs. raw materials vs. finished goods, contribution margin, breakeven analysis)

3. Introduction to ratio analysis and data visualization (utilizing PowerBI or similar data visualization tool)

  • Evaluate existing data
  • Create visualization/dashboard for key ratios/to cater to different users of the financial statements
  • Define and calculate key ratios for a business owner
  • Design charts and graphs for different users of the financial statements
  • Present dashboard

4. Interpretation and analysis of pre-generated reports (e.g., developed through generative artificial intelligence tools) on a company’s financial health and assess its accuracy.

5. Forecasting, budgeting, and financial modelling

  • Why do we budget?
  • Introduction to the master budget
  • Critical areas of focus for a budget
  • Limitations and challenges with budgeting
  • Design and create an interactive financial model using an appropriate data analytics tool
  • Design the layout of the model clearly and logically
  • Create clearly defined inputs and assumption sections
  • Build powerful scenarios
  • Incorporate all related schedules

6. Flexible budgets and standards for control, operational dashboards

  • Use data visualizations to assess results and company performance compared to budget.
  • Define and calculate Key Performance Indicators (KPI)
  • Design charts and graphs for KPIs

7. Relevant information and decision-making

  • Evaluative criteria for decision-making, including quantitative and qualitative considerations (e.g. make vs. buy, sell or process further, keep or drop a business segment).
 
Learning outcomes

Upon successful completion of the course, the student will be able to:

  • Apply appropriate techniques to prepare both static and flexible budgets
  • Explain the concepts, techniques, and merits of various management control systems used in both domestic and international operations
  • Apply various cost allocation concepts and techniques to a variety of cost settings
  • Analyze financial statements using data analytics
  • Use information from automated data capture to form and analyze financial statements
  • Access, examine, and integrate different data files
  • Analyze data patterns, identify discrepancies, and extract unusual items
  • Examine financial data for existence of misrepresentations in areas such as matched or unmatched records, duplication, and limit violation
  • Create, interpret, and analyze dashboards
  • Apply the data analytics process to communicate insights and track outcomes
Means of assessment

Assessment will be based on course objectives and will be carried out in accordance with the Douglas College Evaluation Policy. An evaluation schedule is presented at the beginning of the course. This is a graded course.

Group Project(s):

  • A minimum of one group data analytics       30% 

Individual Activities:

  • In-class activities, homework assignments, and data analytics projects 30%
  • Final Exam              40%

Students must write all examinations in order to obtain credit for the course.

Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.

Textbook materials

Textbooks and Materials to be Purchased by Students

 

Horngren, Charles T., Sundem, Garry L., Statton, William O., Teall, Howard, D. 

             Management Accounting, latest Canadian ed.  Toronto:  Prentice Hall Canada.

Prerequisites

ACCT 1235 or equivalent

Corequisites

None

Equivalencies

None