EBA 3630 Data driven management accounting
Management accountants have for many years been the drivers of analysis for decision-making in most organisations. Previously using reports from internal ERP systems, management accountants have extracted data, conducted spreadsheets, manual analysis, and arrived at a basis for providing relevant information to key decision-makers.
With the vast increase in available data, the management accountant is forced into a world previously hosted by computer scientists. In many cases, management accountants now have to extract the data themselves from various locations, filter and regroup data themselves, and then perform analysis to shed light on current decisions. This course intends to show how data from various sources within companies can be used to perform such tasks as cost estimation, variance analysis, performance measurement and to conduct forensic accounting.
During the course, students should have learned/acquired knowledge of:
- The importance of external and internal data sources to solve management accounting problems
- Relevant management accounting tools
- How to translate management accounting problems into Python Code and/or other relevant programming languages
- How to use data for management decision making
After completing the course, students shall be able to:
- Identify relevant data
- Scope a management accounting problem
- Apply the correct management accounting tool
- Ttranslate management accounting problems into Python Code and/or other relevant programming language
- Conduct financial accounting forensic analysis
- Communicate and interpret the results of using management accounting tools
- Derive recommendations for actions based on their analysis
- Evaluate the meaningfulness of certain types of data to the problem being analyzed
- Understand the benefits and limitations of certain management accounting tools
- In-house data
- ERP systems
- External relevant data
- Extraction of data
- Decision analysis
- Management science
- Cost estimation for various profitability calculations
- Customer profitability analysis
- Accounting forensics
- Predictive analytics
- Optimisation techniques
- Visualisation of management accounting analytics
The course will consist of a mix of lectures and workshops where students do on-hand analysis. Students are expected to use Excel, Python and SQL to solve problems.
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
Students are expected to have a fundamental understanding of Excel, Python and SQL programmes/languages, and had fundamental courses in management accounting and financial accounting.
|Exam category||Weight||Invigilation||Duration||Grouping||Comment exam|
Form of assessment:
Internal and external examiner
Examination every semester
|Form of assessment:||Written submission|
|Exam code:||EBA 36301|
|Resit:||Examination every semester|
Prepare for teaching
Student's own work with learning resources
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.