General Information
Data analytic skills are core for accounting practitioners in our data-intensive business environment. In this subject, we build on advanced data and analysis concepts to develop an analytical mindset where students learn to frame complex managerial questions, assemble the data, compute relevant metrics and models, identify actionable insights, and design effective and efficient communication of the outcomes. Students explore and apply these skills in a variety of contexts, including management accounting, tax, audit and assurance, and financial statement analytics to develop practical skills in working with multiple analytics tools and develop skills in critically evaluating which tool is best suited for a particular problem or question. Finally, students will explore how to best interpret and communicate the results from data analytic procedures using visualisations.
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Details
Academic unit: Bond Business School Subject code: ACCT71-306 Subject title: Data Analytics for Accountants Subject level: Postgraduate Semester/Year: January 2024 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Computer Lab: x12 (Total hours: 24) - Computer Lab 1
- Computer Lab: x12 (Total hours: 24) - Computer Lab 2
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: Books
- Vernon J. Richardson,Ryan Teeter,Katie Terrell Data Analytics for Accounting. n/a,
iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.
Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | ACCT71-306 |
| Subject title: | Data Analytics for Accountants |
| Subject level: | Postgraduate |
| Semester/Year: | January 2024 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
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| Attendance and learning activities: | Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
| Prescribed resources: | Books
|
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):
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| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Explain the role of data analytics for decision making in accounting.
- Apply advanced quantitative methods and techniques to collect and analyse financial and non-financial accounting data.
- Review, interpret, and critically evaluate results from data analytics procedures including hypothesis testing in an accounting context.
- Demonstrate the ability to reflect and apply relevant feedback from others to an accounting analytics project.
- Communicate complex accounting analytics information in a clear, concise writing style tailored to a professional audience.
- Articulate complex accounting analytics information to a diverse professional audience using appropriate visual aids.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Computer-aided Test (Open) Quiz to reinforce understanding of concepts and techniques. Consists of multiple-choice and/or short answer questions 20.00% Week 4 1,2,3 Computer-aided Test (Open) Quiz to reinforce understanding of concepts and techniques. Consists of multiple-choice and/or short answer questions 20.00% Week 8 1,2,3 Presentation Presentation of project plan to class for feedback. 10.00% Week 8 2,3,5,6 Presentation Produce an effective and engaging recorded presentation of key findings and recommendations from your project. 20.00% Week 12 2,3,5,6 Project Report Demonstrate the application of analytic knowledge and skills and the ability to incorporate feedback to use a variety of data sources to generate valuable business insights through accounting analytics. 30.00% Week 13 2,3,4,5,6 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-aided Test (Open) | Quiz to reinforce understanding of concepts and techniques. Consists of multiple-choice and/or short answer questions | 20.00% | Week 4 | 1,2,3 |
| Computer-aided Test (Open) | Quiz to reinforce understanding of concepts and techniques. Consists of multiple-choice and/or short answer questions | 20.00% | Week 8 | 1,2,3 |
| Presentation | Presentation of project plan to class for feedback. | 10.00% | Week 8 | 2,3,5,6 |
| Presentation | Produce an effective and engaging recorded presentation of key findings and recommendations from your project. | 20.00% | Week 12 | 2,3,5,6 |
| Project Report | Demonstrate the application of analytic knowledge and skills and the ability to incorporate feedback to use a variety of data sources to generate valuable business insights through accounting analytics. | 30.00% | Week 13 | 2,3,4,5,6 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless the Lead Educator grants an extension. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
Students’ performance in some assessment tasks will be dependent on the completion of assigned preparatory activities and submission of required materials prior to class. This is a compulsory subject for Certified Practising Accountants Australia (CPA Australia) and Chartered Accountants Australia and New Zealand (CA ANZ) eligibility requirements. Basic excel skills are required for learning activities. As part of the Business School quality accreditation requirements, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially, and no student grades will be affected.
Subject curriculum
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Analytical Framework
We introduce the concept of data analytics and discuss how it is used in the accounting profession. We cover the skills needed by accountants and provide an overview of how this subject will help you build these skills and an analytical mindset. We also consider how accountants can translate common business questions and problems into the required analytic fields and values. Finally, we introduce important analytic terminology including sampling, probability, uncertainty, non-parametric and parametric analytical tests and how they all relate to hypothesis testing and ultimately decision making.
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Foundational Data Analytics
We cover essential analytical and statistical concepts that will be used throughout this subject. We apply common analytic techniques in an accounting context to demonstrate the concepts of sampling, confidence intervals, hypothesis testing using non-parametric testing such as chi-square and parametric methods such as multiple linear regressions. We frame these techniques around the concepts of descriptive, diagnostic, predictive, and prescriptive analytic procedures to show how these can be used to cluster the analytic techniques.
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Mastering the Data
We consider the importance of data in the process of using data analytics to solve business problems. This topic provides an overview of important skills that will be used in this subject including mapping and understanding relational databases, data extraction procedures from Capital IQ, Bloomberg, and EDGAR, verifying and cleaning the data. Throughout this topic the issues of data management, data governance, and privacy are emphasised to ensure students understand the ethical and legal issues in today’s business environment.
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Audit & Assurance Analytics
In this topic we apply analytical techniques to gather insight about controls and transaction data. Specifically, we identify when to use descriptive analytics to understand the business environment, and when to use diagnostic analytics to compare expectation with reality. We also consider how predictive analytics can assist us in identifying common attributes of problematic data to help identify similar events in the future, and how prescriptive analytics can assist auditors as they work to resolve issues with the processes and controls. The techniques we cover include descriptive (age analysis and summary statistics), diagnostic (Z-Score, Benford’s law, matching, sequence checks, stratification, clustering, and hypothesis testing), predictive and prescriptive (regression and AI).
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Tax Analytics
We highlight the use of data analytics for the tax function. First, we consider how tax departments can better control the data they receive from the financial reporting system. Second, we investigate how data analytics is used to help with tax compliance issues as we consider the role of data analytics at the ATO. Finally, we consider how data analysis might be used to assist in tax planning including what-if analysis for new legislation, the possibility of a merger with another company, a shift in product mix or a plan to set up operations in a new low-tax jurisdiction (and/or transfer pricing). In this topic we focus on the application of non-parametric testing such as chi-square to help us solve problems.
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Managerial Analytics
We apply data analytics to answer managerial accounting questions. Management accountants must use descriptive analytics to understand and direct activity, diagnostic analytics to compare with a benchmark and control costs, predictive time-series analytics to plan, and prescriptive analytics to guide their decision process. Data and information are becoming the key components for decision making in organisations, replacing gut response. This topic emphasises the importance of analytic metrics and KPIs and how digital dashboards assist accountants and managers by providing a high-level overview of real-time business performance.
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Financial Statement Analytics
In this topic we focus on how to access, analyse, and forecast financial statement data. We highlight the use of XBRL to gain access quickly and efficiently to financial statement data while addressing the data quality and consistency issues of XBRL data. We use multiple sources to gather data including Capital IQ, Bloomberg, and EDGAR. The data is then decomposed, allowing us to adjust and recompose financial statements. We use discriminant analysis to assess risk, forecast the time-series properties or earnings, assess earnings quality using regression approaches, and forecast accounting “fair value” and “value in use”. Finally, we use text mining to analyse the sentiment in financial reporting data.
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Communicating Analytic Procedures Using Visualisations
This topic focuses on how to communicate the results of data analytic procedures. We emphasise the importance of using visual elements to provide end-users with structure and insights from the data, as well as using interactive methodologies to allow users to explore data without manual assistance. Specifically, we focus on recent developments in visualisation tools and how they can aid accountants in effectively communicating the answers to underlying business problems. In doing so we cover the range of descriptive, diagnostic, predictive, and prescriptive analytic procedures and how each one is best communicated.