Solution Manual for Data and Analytics in Accounting: An Integrated Approach, 1st Edition by Ann C. Dzuranin, Guido Geerts, and Margarita Lenk

Solution Manual for
Data and Analytics in Accounting An Integrated Approach 1e Ann C.
Dzuranin, Guido Geerts, Margarita Lenk




CHAPTER 1
DATA AND ANALYTICS IN THE ACCOUNTING
PROFESSION

Learning Objectives:
LO 1.1: Summarize how advances in data and technology are impacting accounting professionals.
LO 1.2: Describe the stages of the data analysis process.
LO 1.3: Identify the skills necessary to perform data analysis.
LO 1.4: Explain how to apply a data analytics mindset during the data analysis process.




ANSWERS TO MULTIPLE CHOICE QUESTIONS
LO 1.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC:
1. A Leverage Technology to Develop and Enhance Functional Competencies
LO 1.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC:
Leverage Technology to Develop and Enhance Functional Competencies
7. D
2. C LO 1.2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC:
LO 1.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
Leverage Technology to Develop and Enhance Functional Competencies
8. C
3. C LO 1.2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC:
LO 1.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
Leverage Technology to Develop and Enhance Functional Competencies
9. A
4. B LO 1.3, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC:
LO 1 2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
Leverage Technology to Develop and Enhance Functional Competencies
10.D
5. B LO 1.3, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC:
LO 1.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
Leverage Technology to Develop and Enhance Functional Competencies
11.B
6. C

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, LO 1.3, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC:
Leverage Technology to Develop and Enhance Functional Competencies
14.A
LO 1.4, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC:
Leverage Technology to Develop and Enhance Functional Competencies
12.A
LO 1.4, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC:
Leverage Technology to Develop and Enhance Functional Competencies
15.B
LO 1.4, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC:
Leverage Technology to Develop and Enhance Functional Competencies
13.D
LO 1.4, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC:
Leverage Technology to Develop and Enhance Functional Competencies




ANSWERS TO REVIEW QUESTIONS

1. Both the CPA exam and the CMA exam have added data analytic content to their exams. This is in
response to what new professionals need to know as they enter the accounting profession. The CPA
Exam Evolution is a strong indication of how the accounting profession is changing. The new CPA exam
will have more technology and data analytics questions in the Core exam, as well as the Discipline
exams.
LO 1.1, BT: K, Difficulty: Easy, TOT: 6 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies



2.

Changes Accounting Practice Area
1. Ability to use entire data sets to identify ANS: a. Auditing, c. Managerial accounting
exceptions, anomalies, and outliers
2. Automation of manual processes ANS: a., b., c., d. (All areas)
3. Automation of journal entries ANS: b. Financial accounting
4. Risk identification ANS:. a. Auditing, c. Managerial accounting
5. Forecasting ANS: b. Financial accounting, c. Managerial
accounting
6. Compliance reporting ANS: d. Tax accounting
LO 1.1, BT: C, Difficulty: Medium, TOT: 8 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies



3. Data are raw facts and figures. Technology helps covert that data into information. Information is the
knowledge gained from analyzing the data.
LO 1.1, BT: C, Difficulty: Medium, TOT: 6 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies



4.

Purpose Method
1. Understanding what is happening currently ANS: a. Descriptive
and what has happened in the past.

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,2. Understanding what should happen to meet ANS: d. Prescriptive
our goals and objectives.

3. Understanding what might happen in the ANS: c. Prescriptive
future.
4. Understanding why something happened. ANS: b. Diagnostic


LO 1.2, BT: C, Difficulty: Medium, TOT: 6 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies



5. The first step is to identify the motivation for the analysis. The motivation for a project is the why the
i i i i i i i i i i i i i i i i i i i i



analysis is being performed. It is important to understand the reason for the analysis before beginning
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the analysis.
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The second step is to set an objective. In other words, “What is the goal of the analysis?” This step will help
i i i i i i i i i i i i i i i i i i i i i i



articulate the goal and specific questions that will be analyzed.
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The third step is to develop a strategy. In this step, we determine the data and analysis methods
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necessary to achieve the project’s goal. i i i i i




LO i1.2, iBT: iK, iDifficulty: iEasy, iTOT: i8 imin, iAACSB: i Knowledge, iAICPA iFC: iLeverage i Technology i to iDevelop iand iEnhance iFunctional iCompetencies



6. Extracting is the process in which data is retrieved from a source. This could involve downloading an
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Excel file or extracting data from a database or a data warehouse.
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Transforming the data occurs when data is cleaned, restructured, and/or integrated prior to using it for
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analysis.

Loading data is the process of importing transformed data into the software used to perform analyses.
i i i i i i i i i i i i i i i




LO i1.2, iBT: iK, iDifficulty: iEasy, iTOT: i8 imin, iAACSB: i Knowledge, iAICPA iFC: iLeverage i Technology i to iDevelop iand iEnhance iFunctional iCompetencies




7.

Example Data Analysis Process Stage i i i



1. Develop a model to calculate contribution margin. ANS: b. Analyze
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2. Get data from a database.
i i i i i ANS: b. Analyze i i



3. Upload data into analysis software.
i i i i i ANS:b. Analyze i



4. Determine the objective of the analysis.
i i i i i i ANS: a. Plan i i



5. Create a forecast of net income.
i i i i i i ANS: b. Analyze i i



6. Identify the data needed for analysis.
i i i i i i ANS: a. Plan i i



7. Identify relationships within the data.
i i i i i ANS: b. Analyze i i



8. Create a visualization to show the results of the
i i i i i i i i i i ANS: c. Report i i



analyses.
9. Identify sales patterns.
i i i ANS: b. Analyze i i




LO i1.2, iBT: iC, iDifficulty: iMedium, i TOT: i 12 i min, iAACSB: iAnalytic, iAICPA iFC: iLeverage iTechnology i to iDevelop iand iEnhance iFunctional iCompetencies




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, 8. A data analytics mindset is the habit critically thinking through the planning, analysis, and
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interpretation of data results before making and communicating a professional choice or
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decision. Individuals with a data analytics mindset are inquisitive. They always ask “why” when
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interpreting results, are open to learning new technologies, and take the time to evaluate their own
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thinking.
LO i1.3, iBT: iC, iDifficulty: iMedium, iTOT: i6min, iAACSB: iAnalytic, iAICPA iFC: iLeverage i Technology ito iDevelop iand iEnhance iFunctional iCompetencies




9. The ability to communicate well is important in all areas of accounting. In data analytics it is important
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to have good communication skills to explain the results of analyses so others can understand and act if
i i i i i i i i i i i i i i i i i i i



needed.
LO i1.3, iBT: iC, iDifficulty: iMedium, iTOT: i6min, iAACSB: iAnalytic, iAICPA iFC: iLeverage i Technology i to iDevelop iand iEnhance iFunctional iCompetencies




10. 10.

Definition Skill
1. Willingness to try new technology.
i i i i i ANS: c. Technological agilityi i i



2. The ability to read, write, and communicate
i i i i i i i i ANS: b. Data literacy i i i



data in context.i i



3. Ability to create effective data visualizations
i i i i i i ANS: d. Communication i i



4. Disciplined reasoning used to investigate,
i i i i i ANS: a. Critical thinking i i i



understand, and evaluate an event, opportunity, i i i i i i



or an issue.
i i




LO1. i3, iBT: iK, iDifficulty: iEasy, iTOT: i8min, iAACSB: iKnowledge, iAICPA iFC: iLeverage i Technology i to iDevelop iand iEnhance iFunctional iCompetencies.




11. 11.

 Stakeholders: Understand the internal and external parties impacted by the data analysis i i i i i i i i i i i i



results.
 Purpose: Determine the reason for the analysis. i i i i i i



 Alternatives: Evaluate and rank all potential data and analysis choices during the data analysis
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process.
 Risks: Consider risks to the data and the analysis choices, including assumptions and potential
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biases.
 Knowledge: Identify and acquiring the knowledge necessary to properly prepare and interpret
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the analyses. i




 Self-reflection: Review decisions and processes for lessons learned and apply them to future i i i i i i i i i i i i i



projects.
LO i1. i4,iBT: iC,iDifficulty: iMedium, iTOT: i10min, iAACSB: iAnalytic, iAICPA iFC: iLeverage iTechnology i to iDevelop iand iEnhance iFunctional iCompetencies




12. Student answers will vary. General examples follow. i i i i i i




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