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2024/2025  KAN-CACAO2406U  Data & Analytics in Management Accounting

English Title
Data & Analytics in Management Accounting

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Quarter
Start time of the course Second Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for cand.merc. and ASC
Course coordinator
  • Pall Rikhardsson - Department of Accounting (AA)
Main academic disciplines
  • Management
  • Accounting
Teaching methods
  • Blended learning
Last updated on 11-11-2024

Relevant links
Learning objectives
  • Identify ways in which data and analytics is influencing changes in the accounting profession.
  • Explain common decision-making biases and describe how they can potentially influence accounting decisions.
  • Describe how data is acquired for analysis and the steps necessary to prepare it
  • Apply data analysis principles to accounting decision analyses.
  • Develop statistical analyses to predict and evaluate accounting information.
  • Apply appropriate technology to evaluate data, analyses, and communicate results
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 1
Compulsory home assignments
One out of two has to be approved.

1. Weekly quiz: Every week a mandatory multiple-choice test is administered. The purpose of the test is to provide students with an overview of which topics they master and which not. More specifically, the multiple-choice test examines students’ capabilities with respect to (i) knowledge, (ii) comprehension, (iii) application, and (iv) problem solving for the topics covered in the course. This helps students to better prepare for the final exam.

2. Case analyses and presentation: The students are required to work in teams to solve a case, deliver a case solution as well as produce a video recording where they present and discuss the solution. Each team will get written feedback.

Students will not have extra opportunities to get the required number of compulsory activities approved prior to the ordinary exam. If a student has not received approval of the required number of compulsory activities or has been ill, the student cannot participate in the ordinary exam. If a student prior to the retake is still missing approval for the required number of compulsory activities and meets the pre-conditions set out in the program regulations, an extra assignment is possible.

The extra assignment is a 10 page home assignment that will cover the required number of compulsory activities. If approved, the student will be able to attend retake
Examination
Data & Analytics in Management Accounting:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Case based assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Aids Open book: all written and electronic aids, including internet access
Read more here about which exam aids the students are allowed to bring and will be given access to : Exam aids and IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Course content, structure and pedagogical approach

In today's data-driven business environment, the role of accountants has evolved from mere financial record-keepers to strategic advisors who leverage data and analytics to drive decision-making and enhance organizational performance. The "Data and Analytics in Management Accounting" course is designed to equip students with the essential skills and knowledge to harness the power of data analytics in the realm of accounting. By the end of this course, students will have the skills and knowledge necessary to leverage business analytics techniques to enhance accounting practices, support decision-making, and add significant value to organizations in a data-driven world.

Contents:

The rise of analytics as a discipline.

Data driven decision making including common decision biases

The role of analytics in accounting.

The role of accountants in data-driven decision-making processes.

How analytics can add value to financial reporting, auditing, and management accounting.

Data Acquisition and Preparation including:

a.  Acquire data from various sources, including financial systems, databases, and external data providers.

b. Clean, transform, and prepare data for analysis to ensure accuracy and relevance.

Working with descriptive analytics including:

a.  The basics of descriptive statistics to summarize and visualize financial data.

b. Data visualization tools to communicate financial insights effectively.

Working with predictive analytics in accounting:

a.  Predictive modelling techniques such as regression analysis and time series forecasting.

b. Predictive analytics to financial forecasting, risk assessment, and budgeting.

 
Description of the teaching methods
Face to face Lectures
Group work
Case studies/Exercises

Lectures: Lectures are a fundamental teaching method for a course in business analytics for accounting. In these sessions, the instructor introduces key concepts, theories, and frameworks that form the foundation of business analytics in the accounting context.
Case Studies and exercises: Case studies and exercises bridge the gap between theory and practical application. In a business analytics for accounting course, students analyze real-world accounting scenarios, apply analytical techniques, and make data-driven decisions. Case studies also encourage critical thinking and problem-solving skills as students work through complex accounting problems. The chosen textbook offers a veriety of cases and exercises including access to data sets that the course will utilize. Other cases and data sets will also be added to these. To solve cases and train analytical skills Power BI will be used in this course.
Guest Lecturers: Inviting guest speakers from the accounting and analytics industry can offer students valuable insights into how analytics is applied in real-world accounting settings. These experts can share their experiences, best practices, and the latest trends in business analytics, providing students with a broader perspective on the field.
Group Discussions and Debates: Group discussions and debates can promote active learning and critical thinking. Students can analyze accounting scenarios, debate different analytical approaches, and defend their viewpoints. These discussions encourage collaborative problem-solving and enhance students' ability to articulate their ideas
Feedback during the teaching period
The students will solve cases and exercises in groupwork during the course that will be commented on and discussed by the lecturer as well as other students. Students will receive written feedback on compulsory activies.
Student workload
Lectures 100 hours
Exercises 70 hours
Examination 36 hours
Expected literature

Main textbook: Ann C. Dzuranin, Guido Geerts, Margarita Lenk (2022). Data and Analytics in Accounting: An Integrated Approach. Wiley. ISBN: 978-1-119-72315-8
Excerpts from the book Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Supplementary articles and cases will be added before course start
The  analytics software used for this course will be MS Power BI. 
Last updated on 11-11-2024