Audit Assignment with Tableau
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Auditing Assignment with Tableau
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I. Title: Using Visualization Software in the Audit of Revenue Transactions to
Identify Anomalies
II. Introduction:
In this project, you will use Tableau, data visualization software, to audit revenue
transactions to identify anomalies.
The project includes a group component to reflect
the collaborative nature of accountants who must work together on audit teams. You
will be randomly placed into an online discussion group in LEO. The purpose of the
online discussion group is to help one another learn how to use Tableau and make
decisions regarding the results of your data analysis. Participation in the online
discussion group is mandatory.
After collaboratively discussing the case with your colleagues (classmates) in the online
discussion group, you will write and submit an individual report written in your own
words to your boss (professor).
The project provides opportunities for you to further develop the following
competencies:
Graduate School Core Competencies:
Communication
▪ Organize document or presentation clearly in a manner that promotes
understanding and meets the requirements of the assignment.
▪ Develop coherent paragraphs or points to be internally unified and function as
part of the whole document or presentation.
▪ Provide sufficient, correctly cited support that substantiates the writer’s ideas.
▪ Tailor communications to the audience.
▪ Tailor Use sentence structure appropriate to the task, message and audience.
▪ Follow conventions of Standard Written English.
▪ Create neat and professional looking documents appropriate for the project or
presentation.
▪ Create clear oral messages.
Critical thinking
▪ Identify and clearly explain the issue, question, or problem under critical
consideration.
▪ Locate and access sufficient information to investigate the issue or problem.
▪ Evaluate the information in a logical and organized manner to determine its
value and relevance.
▪ Consider and analyze information in context to the issue or problem.
▪ Develop well-reasoned ideas, conclusions or decisions, checking them against
relevant criteria and benchmarks.
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Accounting Competencies:
Risk assessment, analysis, and management
Assess, analyze, and manage risk using appropriate frameworks, professional judgment
and skepticism for effective business management.
Reporting
Identify the appropriate content and communicate clearly and objectively to the intended
audience the work performed and the results as governed by professional standards,
required by law or dictated by the business environment.
Technology and tools
Identify and utilize relevant technology and tools to analyze data, efficiently and
effectively perform assigned tasks as well as support other competencies.
Business Competencies:
Governance perspective
Understand the legal and regulatory environments affecting an organization and their
effects on an organization’s operations, internal controls and enterprise risk
management. Recognize an organization’s social and environmental responsibilities.
Professional Competencies:
Professional behavior
Practice in a manner that is consistent with the character and high standards set by the
AICPA, the accounting profession, and TGS Accounting degree programs.
Demonstrate a work ethic and respect for diversity, as well as a commitment to
continuously acquire new personal and professional skills and knowledge.
Decision making
Objectively identify and critically assess issues and use professional judgment to
develop appropriate decision models, identify and analyze the costs and benefits of
alternative courses of action and recommend optimal solutions.
Collaboration
Work productively with diverse individuals in a variety of roles, with multiple interests in
outcome to achieve acceptable and optimal results.
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Scenario:
You are a senior auditor at the CPA firm of Aoife & Josephine, LLC. Your manager
(professor) calls you into her office to discuss the use of Tableau, data analytic and
visualization software, on an upcoming audit for a client. She highly suggests you learn
how to use Tableau to perform data analytics on sales revenue. Further, she suggests
you read the following articles to prepare for this audit:
• Cao, M., R. Chychyla, and T. Stewart. 2015. Big Data analytics in financial
statement audits. Accounting Horizons 29 (2): 423–429.
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.ed
u/login.aspx?direct=true&db=bth&AN=103541034&site=eds-live&scope=site
• Raphael, J. 2017. Rethinking the audit. Journal of Accountancy 223 (4): 28–32.
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.ed
u/login.aspx?direct=true&db=heh&AN=122600698&site=eds-live&scope=site
• On the ICAEW Website, you may download an excellent report:
ICAEW.com. (2016). Data analytics for external auditors. [online] Available at:
https://www.icaew.com/-/media/corporate/files/technical/iaa/tecpln14726-iaae-
data-analytics—web-version.ashx [Accessed 13 Jul. 2019].
III. Steps to Completion
Step 1: Read the articles recommended by your manager (professor) to gain an
understanding about how big data, data analytics, and new technologies are
transforming external audits.
Step 2: Read the case: Using Visualization Software in the Audit of Revenue
Transactions to Identify Anomalies, which is posted in LEO: Contents>Course
Resources>Projects & Rubrics.
Step 3: Review resources to learn how to use Tableau in Appendix A on the last page
of this document. Collaborate with your online discussion group to learn Tableau tips.
Step 4: Complete the case requirements that start on page 35 of Using Visualization
Software in the Audit of Revenue Transactions to Identify Anomalies.
Step 5: Complete one additional requirement not included in the case; an audio-
enhanced presentation or video of yourself presenting your findings.
Prepare a video or audio-enhanced PowerPoint presentation to present your
findings to your manager (professor) and audit team (classmates).
Prepare a Power Point presentation that includes the following:
▪ Graphs, tables, and other data visualization tools to explain your findings
▪ Speakers notes must be placed under each slide. Use the speaker’s notes to
create either a/an:
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=bth&AN=103541034&site=eds-live&scope=site
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=bth&AN=103541034&site=eds-live&scope=site
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=heh&AN=122600698&site=eds-live&scope=site
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=heh&AN=122600698&site=eds-live&scope=site
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1. Audio enhanced PPT presentation with accurate transcription (or accurate
Closed Captioning) that you will present to your boss and colleagues,
OR
2. Video of yourself presenting the content of your slide to your boss (professor)
and colleagues (classmates). You may use your cell phone to create the
video file.
IV. Deliverables
▪ Report to the boss in MS Word
o Using the report template in Appendix C
o Include a reference page and cite all sources
▪ Audio-enhanced Video or PowerPoint presentation
§ As your professor, I eagerly look forward to reading your deliverables and or viewing
your presentation or video. It is fine to quote sources to illustrate or support your own
thoughts, however, every graded assessment in graduate accounting courses will be
based on the content you have thought about and you have written in your own
words. To properly guide you, I need to read your thoughts and interpretations and
observe you making presentations which demonstrate your comprehension of the
learning goals and ability to perform the competencies.
▪ Check your report for plagiarism using a free online tool, such as Grammarly,
Papers Owl, Citation Machine. Your professor will use TurnItIn.com to check all
graded assignments.
▪ Read the grading rubric before beginning the project to fully understand the
requirements; ask questions about the requirements if needed.
▪ Review.
o Developing your graduate level writing skills:
https://owl.english.purdue.edu/owl/section/1/2/
o What Constitutes Graduate Level Writing; source unknown. In LEO,
Content, Week 9.
▪ Seek feedback for submitting your final version for a grade.
o Ask a classmate, friend or family member to read your report, watch your
presentation, and share constructive feedback to help improve your final
version.
o Submit your draft Word document to the Graduate Writing Center at least
1 week before the project due date. This FREE resource can be
accessed in your LEO classroom. Make edits to your report after
reviewing feedback from the writing center tutors.
https://owl.english.purdue.edu/owl/section/1/2/
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o Submit the milestone to your professor to receive constructive feedback
and use the feedback to improve your work before submitting a final
version for grading.
▪ Submit all required files on or before the due date. Late assignment policies can
be found in the syllabus. No assignments are accepted after the last day of
class.
▪ Ask questions as needed in the weekly Ask the Professor forums.
As a reminder, your deliverables will be assessed on the following competencies:
Graduate Program Competencies:
▪ Communication
▪ Critical thinking
Accounting Competencies:
▪ Risk assessment, analysis, and management
▪ Reporting
▪ Technology and tools
Business Competencies:
▪ Governance perspective
Professional Competencies:
▪ Professional behavior
▪ Decision making
▪ Collaboration
V. Rubric:
You will find the rubric in LEO under Contents>Course Resources>Projects & Rubrics.
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Reference
Christensen, B., R. Elder, and S. Glover. 2015. Behind the numbers: Insights into large
audit firm sampling policies. Accounting Horizons 29 (1): 61–81.
https://doi.org/10.2308/acch-50921 Using Visualization Software in the Audit of
Revenue Transactions to Identify Anomalies 35 Issues in Accounting Education
Volume 33, Number 4, 2018
https://doi.org/10.2308/acch-50921
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Appendix A
Tableau Tutorials
Go to the Tableau.com Website and type Free Training Videos into the search field.
https://www.tableau.com/learn/training
The Tableau Video Library includes over 9 hours of videos:
▪ Getting Started – 3 videos
▪ Connecting to Data – 11 videos
▪ Visual Analytics – 24 videos
▪ Dashboards and Stories – 8 videos
▪ Mapping – 11 videos
▪ Calculations – 16 videos
▪ Why is Tableau Doing That? – 5 videos
▪ How To – 10 videos
Finding the more relevant video resources is an excellent opportunity for your online
discussion group to help each other learn Tableau.
https://www.tableau.com/learn/training
ISSUES IN ACCOUNTING EDUCATION American Accounting Association
Vol.
33
, No. 4 DOI: 10.2308/iace-52146
November 2018
pp. 33–46
Using Visualization Software in the Audit of Revenue
Transactions to Identify Anomalies
Lauren M. Cunningham
The University of Tennessee
Sarah E. Stein
Virginia Polytechnic Institute and State University
ABSTRACT: Recent changes in the accounting profession require students to enter the workforce with technical and
critical-thinking skills using large datasets. In an audit setting, an important skill is the ability to identify anomalies and
risk factors in the client’s data. This instructional case provides students with experience using visualization to
identify anomalous transactions for further substantive testing based on relationships between financial data
(revenues) and nonfinancial data (weather patterns). Students must also create a memo for the workpaper files that
documents their findings, including recommendations for the audit team to select specific sales transactions for
substantive testing. Aside from gaining experience with Tableau visualization software, this case will improve
students’ problem-solving and analytical skills by encouraging them to work independently and to break a complex
problem into manageable pieces. This case is most applicable for implementation in undergraduate or graduate
auditing or internal auditing courses.
Keywords: audit risks; Big Data; data analytics; data visualization; identifying anomalies; revenues.
THE CASE
Company Background
You were recently promoted to audit senior at your firm, Aoife & Josephine LLP, and one of your primary clients is
Souper Bowl Inc. Souper Bowl (‘‘the company’’) is a privately held business headquartered in Maine, and has a fiscal year-end
of December 31. The company has been in business for nine years and prides itself on offering creative soups at a reasonable
price and that are made with locally sourced ingredients. The most popular soups include sweet potato corn chowder, curried
root vegetable and lentil, and maple-roasted butternut squash. Souper Bowl typically experiences increased sales during winter
months since soup hits the spot on a cold and snowy day. To further encourage sales on days when customers often avoid
venturing outside, the company provides a delivery service and guarantees that soup can be delivered to anyone no matter what
the weather. The company found this strategy to be particularly successful in 2015 when New England (including Maine)
experienced record snowfall during February and March.
Souper Bowl sells their soup at several restaurant locations throughout Maine. The company employs three managers that
direct the day-to-day operations for a group of stores that are organized by approximate geographic region: northern Maine
(Store Type 1), mid-Maine (Store Type 2), and coastal Maine (Store Type 3). Appendix A provides a map of these store
locations. Each manager knows their local market well and has the flexibility to advertise and offer promotions with the overall
goal of increasing sales year over year. If total sales at the end of the year exceed total sales from the prior year for that
manager’s set of locations (i.e., ‘‘Store Type’’), then the manager earns a monetary bonus from the company.
We appreciate the helpful and constructive feedback from Valaria P. Vendrzyk (editor), Ronald F. Premuroso (associate editor), and two anonymous
reviewers. We are also grateful to the 2016 Deloitte Foundation Trueblood Seminar for providing us with the inspiration for this case study. Lauren M.
Cunningham received financial support from the PwC INQuires Grant for the purpose of integrating technology into accounting programs.
Supplemental material can be accessed by clicking the link in Appendix C.
Editor’s note: Accepted by Valaria P. Vendrzyk.
Submitted: December 2017
Accepted: May 2018
Published Online: May 2018
33
An audit of the company is required to comply with debt covenants related to a large bank loan that the company entered
into when it began operations. Specifically, Souper Bowl must provide audited annual financial statements to the bank within
90 days of the fiscal year-end. The company must also provide unaudited quarterly financial statements to the bank within 45
days of the end of each quarter. The debt contract includes a financial covenant that requires pre-tax income in each quarter to
be greater than zero. If not met, the bank has multiple remedies at its disposal, including calling the loan such that the entire
balance is due immediately, seizing the company’s assets that are posted as collateral, or providing a waiver for the violation.
Souper Bowl’s net income for the year ended December 31, 2016 is $468,810, while net income for the prior year ended
December 31, 2015 was $825,229.
Auditing Revenues
As part of your new role as audit senior, you will be performing a large portion of the planning and testing of sales for the
2016 audit of Souper Bowl. AU-C Section 240.26 states that ‘‘when identifying and assessing the risks of material misstatement
due to fraud, the auditor should, based on a presumption that risks of fraud exist in revenue recognition, evaluate which types of
revenue, revenue transactions, or assertions give rise to such risks.’’ During planning for the audit, the partner and manager
determined that the following three management assertions represent significant risks for revenues:
(1) recorded sales occurred;
(2) sales are accurately recorded; and
(3) sales are recorded in the proper period.
In prior years, the audit approach relied on random sampling to test revenues. However, the partner wanted to develop more
focused procedures in the current year to hone in on potentially riskier sales transactions. As a result, the plan is to perform
disaggregated sales analytics to identify unusual trends in the daily sales data with the goal of identifying sales on specific days
at specific store locations that should be subjected to substantive testing due to heightened risks. The remainder of the
population would then be sampled using a random sampling approach.1
Based on your experience from prior audits, you know that Souper Bowl’s daily sales fluctuate with temperature and snow
accumulation. To perform your revenue analytics, you request a file from the client that includes daily sales by store location
for both 2016 (current year) and 2015 (prior year). You also retrieve daily weather data from the National Oceanic and
Atmospheric Administration’s (NOAA) website for the weather centers closest to Souper Bowl’s store locations. Total revenue
for the current year ended December 31, 2016 is $18.8 million, while total revenue for the prior year ended December 31, 2015
was $19.1 million. The audit team’s workpapers include the following lead sheet for revenue testing, and the total balances for
each year agree to the trial balance and the company’s draft financial statements for 2016.
Souper Bowl Inc.
Revenue Lead Sheet
December 31, 2016
2016 2015 Change
PBC
% Change
Revenue, Store Type 1 $4,062,390.97 $4,032,383.16 $30,007.81 0.74%
Revenue, Store Type 2 9,331,175.81 9,558,584.07 (227,408.26) �2.38%
Revenue, Store Type 3 5,425,421.53 5,546,767.89 (121,346.36) �2.19%
Total Revenue $18,818,988.31 $19,137,735.12 $(318,746.81) �1.67%
Your manager stated that Tableau is a popular data visualization tool that your firm recently adopted and she instructed that
you learn how to use it to perform these sales analytics. Since she is busy overseeing the planning and testing of other audit
areas, she wants you to take the first pass and then document your results in a memo for her review. The manager wants you to
provide thoughtful analyses and a thorough exploration of the possible relationships in the data. You are eager to impress her
with your work, especially following your recent promotion to senior.
1 As noted in Christensen, Elder, and Glover (2015), most of the large accounting firms emphasize the use of specific identification testing before random
sampling to increase the efficiency and effectiveness of substantive tests. Specific identification testing, also referred to as directed sampling, is usually
used to select and test individually significant items and those items identified as having a higher risk of misstatement.
34 Cunningham and Stein
Issues in Accounting Education
Volume 33, Number 4, 2018
Requirements
1. Read the articles assigned by your instructor to gain an understanding about how Big Data, data analytics, and new
technologies are transforming external audits. After reading these articles, provide a response to the following two
questions:
a. Discuss at least three specific ways in which Big Data, data analytics, and new technologies can enhance external
audits. How does each item discussed improve the effectiveness and/or efficiency of the audit?
b. On the other hand, what challenges do auditors face when using Big Data, data analytics, and new technologies
during an audit?
2. As noted in the case, auditing standards specifically require auditors to identify revenue recognition as a fraud risk in
most audits. Based on your understanding of the company, what factors may increase the risk of fraudulent financial
reporting in Souper Bowl’s 2016 revenues?
3. Use the daily sales by location as provided by the client (2016 and 2015) and the weather data from NOAA to perform
disaggregated sales analytics in Tableau. Your goal is to develop visualizations that identify potential outliers in the
2016 daily sales data related to the significant risks identified by the partner and manager. Using the memo template in
Appendix B, document your analyses and conclusions as to the specific daily sales from certain locations that you
recommend selecting for focused substantive testing.
Note: Your conclusion needs to be precise enough to pull specific transactions—for example, you would not list the
‘‘month of March’’ in store 1010 because this would result in too many observations to feasibly test. Also, you should
not recommend testing observations from 2015. Your engagement team completed that audit in the prior year—instead,
you are using 2015 data as a component of your baseline prediction for 2016.
REFERENCE
Christensen, B., R. Elder, and S. Glover. 2015. Behind the numbers: Insights into large audit firm sampling policies. Accounting Horizons
29 (1): 61–81. https://doi.org/10.2308/acch-50921
Using Visualization Software in the Audit of Revenue Transactions to Identify Anomalies 35
Issues in Accounting Education
Volume 33, Number 4, 2018
https://doi.org/10.2308/acch-50921
APPENDIX A
Map of Store Locations for Souper Bowl Inc.
36 Cunningham and Stein
Issues in Accounting Education
Volume 33, Number 4, 2018
APPENDIX B
Example Memo Template*
Souper Bowl Inc.—December 31, 2016
Disaggregated Revenue Analytics
Purpose: The purpose of this memo is to document plausible trends and expectations for disaggregated revenue data and to
identify specific days and locations that warrant further substantive investigation.
Data: We obtained a listing of daily sales by location from the client’s IT system. We tested the details for mathematical
accuracy, as summarized in the table below:
Total Sales, 2015 Total Sales, 2016
Store Type 1 $ $
Store Type 2 $ $
Store Type 3 $ $
Total $ $
Procedures: Based on our risk assessment process, we identified the following assertions as significant risks related to
revenues/sales:
� Recorded sales occurred.
� Sales are accurately recorded.
� Sales are recorded in the proper period.
Because Souper Bowl’s operations are solely in the state of Maine, we obtained disaggregated data that reports daily sales
by store location and Store Type. Based on discussions with management and our review of the board of director minutes, we
are unaware of any new store locations or other major changes to operations during the year. Therefore, we expect the prior
year’s revenues to be a reasonable baseline expectation for this year’s revenues (e.g., similar seasonal trends). Because the
business can also be impacted by weather conditions, which vary by year, we also perform analyses that consider changes in
weather patterns to predict expected changes from the prior year’s sales. We performed several analytics to identify unusual
trends as compared to the prior year’s sales, taking weather conditions into consideration. The purpose of these analytics is to
identify specific observations (or specific sets of observations) to select for further substantive testing. The analytics that we
performed are as follows:
� Visualization Analysis #1: Title
[Provide a description of the relationship you expected to observe in the data, along with screenshots of the
visualization results. Clearly identify (using circles, arrows, etc.) the part of the visualization that leads you to believe
that a specific location/day is an anomaly. Ensure that all tables and graphics are properly labeled (x axis, y axis,
etc.).]
* Results: [In each of the ‘‘Results’’ sections, include a brief summary of your findings so that your manager can
see (in words) the way that you interpret the visualization screenshots.]
� Visualization Analysis #2: Title
* Results:
� [The number of analyses that you perform is up to you. Remember that you want to impress your manager, but you also
know that the manager’s time is valuable. Therefore, each analysis that you report should offer new information and
conclusions (e.g., avoid repeating the same type of analysis with different coloring, shapes, etc., if the conclusions
drawn are the same.)
Conclusion: Based on the procedures described above, the audit team will pull supporting sales information to substantively
test transactions from the following locations and days:
� This section of the memo can be achieved by using lists or tables, but regardless of the style of presentation, it should
clearly describe which item(s) you are recommending that the audit team look into further (based on all the analyses
above). For each item, you should reference which analysis # the item comes from. The item should be a specific
location on a specific day, or a sample of certain days from a set of observations that exhibit the same unusual trend
based on your analyses above (e.g., if you identify an unusual relationship for Q4 for location #1001, but you cannot
identify one specific day or set of days that is driving the unusual relationship, you may choose to sample from Q4
Using Visualization Software in the Audit of Revenue Transactions to Identify Anomalies 37
Issues in Accounting Education
Volume 33, Number 4, 2018
instead). Remember that it takes time and resources to test each selection, so be strategic in your selections and include
justification for your decisions in this section of the memo.
* NOTE: Appendix B is available as a downloadable Word document, see Appendix C for the link.
APPENDIX C
iace-52146_Example Memo Template: http://dx.doi.org/10.2308/iace-52146.s01
38 Cunningham and Stein
Issues in Accounting Education
Volume 33, Number 4, 2018
http://dx.doi.org/10.2308/iace-52146.s01
Accounting Horizons American Accounting Association
Vol. 29, No. 2 DOI: 10.2308/acch-51068
2015
pp.
423
–429
Big Data Analytics in Financial Statement
Audits
Min Cao, Roman Chychyla, and Trevor Stewart
SYNOPSIS: Big Data analytics is the process of inspecting, cleaning, transforming, and
modeling Big Data to discover and communicate useful information and patterns,
suggest conclusions, and support decision making. Big Data has been used for
advanced analytics in many domains but hardly, if at all, by auditors. This article
hypothesizes that Big Data analytics can improve the efficiency and effectiveness of
financial statement audits. We explain how Big Data analytics applied in other domains
might be applied in auditing. We also discuss the characteristics of Big Data analytics,
which set it apart from traditional auditing, and its implications for practical
implementation.
Keywords: Big Data; analytical methods; auditing.
INTRODUCTION
B
ig Data includes datasets that are too large and complex to manipulate or interrogate with
standard methods or tools. It is characterized by ‘‘three Vs’’: volume, velocity, and variety
(McAfee and Brynjolfsson 2012). Volume refers to the sheer size of the dataset, velocity to
the speed of data generation, and variety to the multiplicity of data sources; the three Vs tend to be
interrelated.1 Traditional datasets utilized by auditors and academia, such as Compustat, CRSP, and
Audit Analytics, are not Big Data. Big Data is a relatively recent phenomenon, the product of a
technological environment in which almost anything can be recorded, measured, and captured
Min Cao is an Assistant Professor at Rutgers, The State University of New Jersey, New Brunswick,
Roman Chychyla is a Visiting Assistant Professor at the University of Miami, and Trevor Stewart is
a retired Deloitte partner and a Senior Research Fellow at Rutgers, The State University of New
Jersey.
The authors gratefully acknowledge the advice, help, and comments received from many individuals including
Khrystyna Bochkay, Alexander Kogan, Miklos Vasarhelyi, and seminar participants at Rutgers, The State University of
New Jersey. We also thank the editors, Arnold M. Wright, Paul A. Griffin, and Brad M. Tuttle, as well as two
anonymous reviewers for their helpful and insightful comments.
Submitted: February 2015
Accepted: February 2015
Published Online: February 2015
Corresponding author: Trevor Stewart
Email: trsny@verizon.net
1 Some also refer to the four Vs of Big Data, the fourth being ‘‘veracity’’ (Zhang, Yang, and Appelbaum 2015).
423
mailto:trsny@verizon.net
digitally, and thereby turned into data—a process that Mayer-Schönberger and Cukier (2013) refer
to as ‘‘datafication.’’ Datafication may track thousands of simultaneous events; be performed in real
time; involve a multiplicity of numbers, text, images, sound, and video; and require petabytes
(thousands of terabytes) of storage capacity.2 Examples of Big Data include more than 1 million
customer transactions every hour at Walmart, more than 50 billion photos on Facebook, and 200
gigabytes of astronomical data collected per night.3 Big Data has been used in marketing to target
potential customers, in political campaigning to study voter demographics, in sports to evaluate
teams and players, in national security to identify threats, in biology to study DNA, and in law
enforcement to identify crime suspects (Mayer-Schönberger and Cukier 2013).
Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data
to discover and communicate useful information and patterns, suggest conclusions, and support
decision making. For the purposes of this article, we assume that the auditor focuses on the
transactions, balances, and disclosures that underlie the financial statements and related
management assertions. In the auditing of financial statements in accordance with International
Statements on Auditing (ISAs), numerous potential opportunities arise for Big Data analytics. For
example, the following audit activities are likely to benefit from Big Data analytics:
� Identifying and assessing the risks associated with accepting or continuing an audit
engagement, for example, the risks of bankruptcy or high-level management fraud.
� Identifying and assessing the risks of material misstatement of the financial statements due to
fraud, and testing for fraud with regard to the assessed risks (ISA 240, IAASB 2014a).
� Identifying and assessing the risks of material misstatement through understanding the entity
and its environment (ISA 315, IAASB 2014b). This includes performing preliminary
analytical procedures, as well as evaluating the design and implementation of internal
controls and testing their operating effectiveness.
� Performing substantive analytical procedures in response to the auditor’s assessment of the
risks of material misstatement (ISA 520, IAASB 2014c).
� Performing analytical procedures near the end of the audit to assist the auditor in forming an
overall conclusion about whether the financial statements are consistent with the auditor’s
understanding of the entity (ISA 520, IAASB 2014c).
In this article, we hypothesize that a financial statement audit can potentially be improved by
analytical methods that use Big Data. In such audits, the data are transactions and balances that
usually reside in the entity’s ERP and data warehouse systems. These data are not Big Data per se
unless they are accumulated over a significant period of time or are complemented with additional
facts. Therefore, most Big Data opportunities discussed in this paper come from auxiliary data that,
after processing, may reveal matters of audit interest. The Big Data of potential audit interest
includes social media information, surveillance videos, and stock market transaction data.
EXAMPLES OF BIG DATA ANALYTICS
Since there are few, if any, current applications of Big Data analytics in external auditing, and
none that we are aware of, we describe examples from other disciplines and hypothesize how
similar applications could be implemented in external auditing.
2 See, http://archive.wired.com/science/discoveries/magazine/16-07/pb_intro for a good illustration of how much a
petabyte is.
3 When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first
few weeks than all data collected in the history of astronomy.
424 Cao, Chychyla, and Stewart
Accounting Horizons
June 2015
http://archive.wired.com/science/discoveries/magazine/16-07/pb_intro
First, the new availability of voluminous and informative sources of data has resulted in new
approaches to predict stock price averages. For instance, Bollen, Mao, and Zeng (2011) measure
global public mood based on Twitter data and successfully use it to predict daily fluctuations of the
Dow Jones Industrial Average (DJIA). They utilized Google’s Profile of Mood States and
academically developed OpinionFinder (Wilson et al. 2005) tools to generate daily time series of
the public mood shifts based on nearly 10 million public tweets posted by approximately 2.7
million users. By doing this, the authors were able to predict shifts in the DJIA three to four days
ahead. In addition to social media, news articles are also known to predict movements of stock
prices (Chan 2003; Mittermayer 2004). It is conceivable that similar data sources can be used to
predict bankruptcy or assess the overall financial state of a firm. Such tools might be used to better
identify and evaluate engagement risk and thus reduce litigation risk.
Second, demographic and weather data have been used to predict customers’ behavior.
OfficeMax, a large retailer of office supplies, uses LivePredict, a system built by online technology
provider Monetate, to personalize online landing pages based on customers’ demographics.4
Interestingly, this system tries to predict customers’ political views, and adjusts accordingly. The
system uses IP addresses to identify customers’ locations and U.S. census data to create
demographic profiles. In a weather-related application, Walmart analyzed its terabytes of
transactional data to determine that when hurricanes threatened, customers not only bought
additional flashlights, but that sales of strawberry Pop-Tarts (a popular breakfast snack) increased
sevenfold.5 This and similar findings from Big Data analytics help Walmart to better manage
inventories. Geographical and demographic data have a potential to reasonably predict revenues
and sales in individual business locations. The resulting estimates may be used as a benchmark to
assess sales amounts by locations. In addition, peer-based metrics can be utilized to draw attention
to possibly problematic branches. Similar analytics may improve the audit process by focusing
resources on more risky parts of the business.
Third, Big Data analytics commonly involves combining several sources of data, some
structured and others unstructured, including numbers, text, images, sound, and video—the
processing of which requires a combination of different analytical methods from different
disciplines. An example is Ayata’s Prescriptive Analytics, which is used in oil and gas exploration
to predict optimal drilling sites based on data such as images from well logs, videos of fluid flows
from hydraulic fractures, sounds from drilling operations, text from driller’s notes, and numbers
from production reports.6 The challenge of integrating different sources of Big Data including
news, audio and video streams, cell phone recordings, social media comments, and using them for
audit purposes is discussed by Moffitt and Vasarhelyi (2013), who propose using such data to
obtain new forms of evidence, confirm existence of events, and validate reporting elements.
Fourth, the Los Angeles Police Department analyzes data from crime scenes, including time,
location, nature, and actors in order to predict the most likely timing and location of crimes on that
day and to deploy forces most effectively.7 The result has been a significant improvement in the
LAPD’s ability to forestall criminal activity and neutralize potential perpetrators such as gang
members in the predicted area. Similar analytics that relies on information about a firm’s past
activities or outcomes of past audits could be used by auditors to identify fraud risks and direct audit
effort aimed at fraud detection.
4 See, http://www.forbes.com/sites/lydiadishman/2013/08/08/forget-ab-testing-office-max-uses-livepredict-to-
segment-red-and-blue-voters/
5 See, http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html
6 See, http://www.wired.com/insights/2014/01/big-data-analytics-can-deliver-u-s-energy-independence/
7 See, http://www.huffingtonpost.com/2012/07/01/predictive-policing-technology-los-angeles_n_1641276.html
Big Data Analytics in Financial Statement Audits 425
Accounting Horizons
June 2015
http://www.forbes.com/sites/lydiadishman/2013/08/08/forget-ab-testing-office-max-uses-livepredict-to-segment-red-and-blue-voters/
http://www.forbes.com/sites/lydiadishman/2013/08/08/forget-ab-testing-office-max-uses-livepredict-to-segment-red-and-blue-voters/
http://www.wired.com/insights/2014/01/big-data-analytics-can-deliver-u-s-energy-independence/
http://www.huffingtonpost.com/2012/07/01/predictive-policing-technology-los-angeles_n_1641276.html
Fifth, the SEC is investing in Big Data analytics applications to monitor market events, seek
out financial statement fraud, and identify audit failures. For example, Market Information Data
Analysis System (MIDAS), rolled out by the SEC in January 2013, collects about one billion
records a day from the proprietary feeds of each of the 13 national equity exchanges, time-stamped
to the microsecond. The data is extremely voluminous, challenging to process correctly, and
requires specialized data expertise. In July 2013, the agency announced the formation of Financial
Reporting and Audit Task Force to strengthen the effort to identify securities law violations relating
to the preparation of financial statements, issuer reporting and disclosure, and audit failures. The
task force uses an analytical Accounting Quality Model (AQM), better known in financial services
as ‘‘RoboCop,’’ to scan routine regulatory filings and flag high-risk activities warranting closer
inspection by SEC enforcement teams. At the same time, the SEC also announced the formation of
the Microcap Fraud Task Force to investigate fraud in the issuance, marketing, and trading of
microcap securities. The task force will monitor websites and social media because microcap
fraudsters frequently employ them to prey on unsophisticated investors.8 Similar analytics could be
used by auditors to identify fraudulent or high-risk activities by auditees.
Finally, we note that internal audit groups at some large companies are utilizing Big Data
within their organizations. For example, the internal audit team at BlueCross and BlueShield of
North Carolina uses Big Data analytics to identify duplicate insurance claims from millions of
claims each month.9 KPMG, Deloitte, and PwC all have publications on their websites explaining
how internal auditors can use data analytics to improve both efficiency and effectiveness. For
example, KPMG suggests that ‘‘With data analytics, organizations have the ability to review every
transaction—not just a sample—which enables a more efficient analysis on a greater scale’’ (KPMG
2013, 1). Many internal audit activities mirror those of external financial statement audits and
similar Big Data analytics can be applied.
CHARACTERISTICS OF BIG DATA ANALYTICS
There are certain characteristics of Big Data analytics that are causing users to rethink how data
are used. First, it is increasingly possible to analyze ALL or almost all the data rather than just a
small, carefully curated subset or sample. This can lead to models that are more robust than before.
For example, if an auditor wants to determine what characteristics of journal entries are indicators
of risk of error or fraud, then it is possible to analyze all the journal entries for however long records
have been kept and use this information to identify current journal entries that are truly unusual.
Whereas in the past one had to be very careful to eliminate polluted data, when all the data are
available a certain degree of messiness is acceptable.10
A second shift in thinking is from causation to correlation. Instead of trying to understand the
fundamental causes of complex phenomena, it is increasingly possible to identify and make use of
correlations. For example, Mayer-Schönberger and Cukier (2013, 132) report that ‘‘researchers at
the University of Ontario Institute of Technology and IBM are working with a number of hospitals
on software to help doctors make better diagnostic decisions when caring for premature babies . . .
The software captures and processes patient data in real time, tracking 16 different data streams,
such as heart rate, respiration rate, temperature, blood pressure, and blood oxygen level, which
8 http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171624975#.U0X3m6HD_IU (last accessed Janu-
ary 16, 2014).
9 See, https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/big-data-oceans .
10 We recognize that polluted data may be more of a problem in some applications than in others. For example, more
data dramatically help in the area of computational linguistics, even if data are messy (Weikum et al. 2012).
However, data quality may be more important than data size in movie-recommending systems (Pilászy and Tikk
2009).
426 Cao, Chychyla, and Stewart
Accounting Horizons
June 2015
http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171624975#.U0X3m6HD_IU
http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171624975#.U0X3m6HD_IU
https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/big-data-oceans
together amount to around 1,260 data points per second.’’ While these observations may allow
doctors to eventually understand fundamental causes, simply knowing that something is likely to
occur is arguably more important than understanding exactly why. It is not hard to imagine an
analogous auditing application in which restatements or other adverse events are correlated with
indicators culled from every public company filing and other information.
The ability to use correlation models with vast amounts of high-velocity data, in order to
pinpoint transactions or events of audit interest, becomes significantly more useful when applied
continuously. Continuous auditing and monitoring systems are likely to become particularly
relevant in this Petabyte Age, transforming audit practice (Vasarhelyi and Halper 1991; Alles,
Brennan, Kogan, and Vasarhelyi 2006) where, for example, statistical relationships between
different business elements and processes may be monitored continuously to detect irregular events
(Kogan, Alles, Vasarhelyi, and Wu 2011).
IMPLEMENTING BIG DATA ANALYTICS IN AUDITS
Implementing Big Data analytics is not a trivial endeavor. It requires individuals with expertise
in data analytics, as well as appropriate hardware and software resources. As a result, many
businesses outsource their Big Data applications to solutions providers such as Teradata, IBM, and
Wipro that offer specialist services. Similarly, the training related to Big Data analytics may go well
beyond the scope of the academic and professional level of an auditor. The auditing profession will
have either to hire new analytically trained professionals, or more likely to use the services of third-
party solutions providers for Big Data analytics. Relying on third-party solutions providers raises a
privacy concern, but this issue is not new—the profession already relies on third parties, such as
banks, when carrying out audits.
In identifying anomalies and exceptions for further audit investigation, current implementa-
tions of analytical methods sometimes generate more false positives than can feasibly be
investigated by the audit team, and result in information overload (Debreceny, Gray, and Rahman
2003; Alles, Kogan, and Vasarhelyi 2008). One of the opportunities of Big Data analytics is the
possibility of dramatically reducing the number of false positives through more accurate
identification of true anomalies and exceptions together with better systems of prioritization (Issa
and Kogan 2013).
There are several issues that the auditing profession will need to deal with related to Big Data
analytics. First, making successful use of Big Data requires a paradigm shift. Instead of using some
data in small clean datasets and focusing on causation (plausible relationships in ISA terms), the
auditor using Big Data will tend to use ‘‘all’’ the data in large relatively messy datasets, and will
focus more on correlation than causation. The degree to which this approach is implemented in
audit will vary according to the stage of an audit: using messy data is more tolerable for planning
and risk assessment as opposed to substantive procedures. For example, Big Data analytics can be
used to identify business patterns and trends, traditional audit analytics and computer-assisted audit
techniques can be used to conduct a more detailed analysis of potential issues, and conventional
auditing judgment can be used to determine the impact of findings on financial reporting. In
addition, messy data might not be appropriate for analytical procedures that are sensitive to noise.
Nevertheless, this thinking is somewhat foreign to the profession. It will certainly require significant
new guidance and education, and may even require auditing standards themselves to be modified.
Second, the volume of Big Data introduces significant computational challenges. Many
common analytical techniques used in auditing could not be applied to Big Data. The solution is
either to use simple analytical techniques that require less computational resources, or to select
subsets of data that could be managed by more complex analytical tools. The latter case is using Big
Big Data Analytics in Financial Statement Audits 427
Accounting Horizons
June 2015
Data to carefully select a subset that is more valuable for an audit. For example, there are methods
to select subsets of data that result in more accurate analytical models (e.g., see Settles 2009).
Third, privacy is a potential concern when Big Data is used. Some analytics may require
clients’ nonpublic information beyond that usually released to auditors. Others would benefit from
information about previously conducted audits, perhaps of other clients. The usage of such sensitive
information in Big Data applications presents a challenge, although this concern is not specific to
auditing. For example, the European Union is scrutinizing Google over a raft of antitrust and
privacy concerns related to its use of Big Data (Mayer-Schönberger and Cukier 2013).
Finally, when ‘‘all’’ the data are processed through the auditor’s analytical systems and there is
a failure to identify fraud or error, there is a risk that the auditor will be second-guessed. It is always
easy for others who have the benefit of hindsight to identify indicators that the auditor missed and to
connect the dots—just as the U.S. intelligence community was castigated for not connecting, in
advance, the dots that would have led to the apprehension of the bombers of the 2013 Boston
Marathon. This is not an entirely new problem, but auditors have traditionally based their work on
samples, and it is accepted that there is a statistical risk that fraud or error will not be identified.
Last, a change to Big Data analytics could identify fraud or error that was missed in the past. Again,
this is not a new problem, but it is an issue that auditors adopting Big Data analytics will likely have
to deal with.
Besides using Big Data analytics to perform audits, audit firms can potentially use it for
internal purposes. For example, since most audit working papers are electronic, there is an
opportunity for the firm to analyze audits across an entire portfolio in search of anomalies and
potential quality issues.
CONCLUDING REMARKS
Big Data is revolutionizing many fields at an increasing rate, and it seems only a matter of time
before the auditing profession adopts similar analytical methods. In this paper, we provide examples
of Big Data analytics and suggest analogous auditing applications. We briefly discuss certain
characteristics of Big Data analytics that are relevant to audit and identify some of the opportunities
and challenges of implementation.
REFERENCES
Alles, M., G. Brennan, A. Kogan, and M. Vasarhelyi. 2006. Continuous monitoring of business process
controls: A pilot implementation of a continuous auditing system at Siemens. International Journal
of Accounting Information Systems 7 (2): 137–161.
Alles, M., A. Kogan, and M. Vasarhelyi. 2008. Putting continuous auditing theory into practice: Lessons
from two pilot implementations. Journal of Information Systems 22 (2): 195–214.
Bollen, J., H. Mao, and X. Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational
Science 2 (1): 1–8.
Chan, W. 2003. Stock price reaction to news and no news: Drift and reversal after headlines. Journal of
Financial Economics 70 (2): 223–260.
Debreceny, R., G. Gray, and A. Rahman. 2003. The determinants of Internet financial reporting. Journal of
Accounting and Public Policy 21 (4): 371–394.
International Auditing and Assurance Standards Board (IAASB). 2014a. The auditor’s responsibilities
relating to fraud in an audit of financial statements. ISA 240. In Handbook of International Quality
Control, Auditing, Review, Other Assurance, and Related Services Pronouncements, Vol. 1. New
York, NY: International Federation of Accountants. Available at: http://www.ifac.org
International Auditing and Assurance Standards Board (IAASB). 2014b. Identifying and assessing the risks
of material misstatement through understanding the entity and its environment. ISA 315. In
428 Cao, Chychyla, and Stewart
Accounting Horizons
June 2015
dx.doi.org/10.1016/j.accinf.2005.10.004
dx.doi.org/10.1016/j.accinf.2005.10.004
dx.doi.org/10.2308/jis.2008.22.2.195
dx.doi.org/10.1016/j.jocs.2010.12.007
dx.doi.org/10.1016/j.jocs.2010.12.007
dx.doi.org/10.1016/S0304-405X(03)00146-6
dx.doi.org/10.1016/S0304-405X(03)00146-6
dx.doi.org/10.1016/S0278-4254(02)00067-4
dx.doi.org/10.1016/S0278-4254(02)00067-4
http://www.ifac.org
Handbook of International Quality Control, Auditing, Review, Other Assurance, and Related
Services Pronouncements, Vol. 1. New York, NY: International Federation of Accountants.
Available at: http://www.ifac.org
International Auditing and Assurance Standards Board (IAASB). 2014c. Analytical procedures. ISA 520. In
Handbook of International Quality Control, Audititing, Review, Other Assurance, and Related
Services Pronouncements, Vol. 1. New York, NY: International Federation of Accountants.
Available at: http://www.ifac.org
Issa, H., and A. Kogan. 2013. A Predictive Ordered Logistic Regression Model for Quality Review of
Control Risk Assessments. Working paper, Rutgers Accounting Research Center.
Kogan, A., M. Alles, M. Vasarhelyi, and J. Wu. 2011. Analytical Procedures for Continuous Data Level
Auditing: Continuity Equations. Working paper, Rutgers Accounting Research Center.
KPMG. 2013. Data Analytics for Internal Audit. Available at: https://www.kpmg.com/CH/en/Library/
Articles-Publications/Documents/Advisory/pub-20130412-data-analytics-internal-audit-en
Mayer-Schönberger, V., and K. Cukier. 2013. Big Data: A Revolution That Will Transform How We Live,
Work, and Think. Boston, MA: Eamon Dolan/Houghton Mifflin Harcourt.
McAfee, A. and E. Brynjolfsson. 2012. Big data: the management revolution. Harvard Business Review 90:
60–66.
Mittermayer, M. 2004. Forecasting Intraday Stock Price Trends with Text Mining Techniques. Proceedings
of the 37th Annual Hawaii International Conference on System Sciences, Hawaii, HI, January 5–8.
Moffitt, K., and M. Vasarhelyi. 2013. AIS in an age of Big Data. Journal of Information Systems 27 (2): 1–
19.
Pilászy, I., and D. Tikk. 2009. Recommending new movies: Even a few ratings are more valuable than
metadata. In Proceedings of the Third ACM Conference on Recommender Systems, 93–100. New
York, NY: RecSys 2009.
Settles, B. 2009. Active Learning Literature Survey 1648. Computer Sciences Technical Report, University
of Wisconsin–Madison.
Vasarhelyi, M., and F. Halper. 1991. The continuous audit of online systems. Auditing: A Journal of
Practice & Theory 10 (1): 110–125.
Weikum, G., J. Hoffart, N. Nakashole, M. Spaniol, F. Suchanek, and M. Yosef. 2012. Big data methods for
computational linguistics. IEEE Data Engineering Bulletin 35 (3): 46–64.
Wilson, T., P. Hoffmann, S. Somasundaran, J. Kessler, J. Wiebe, Y. Choi, C. Cardie, E. Riloff, and S.
Patwardhan. 2005. OpinionFinder: A system for subjectivity analysis. In Proceedings of HLT/
EMNLP on Interactive Demonstrations, 34–35. Stroudsburg, PA: Association for Computational
Linguistics.
Zhang, J., X. Yang, and D. Appelbaum. 2015. Toward effective Big Data analysis in continuous auditing.
Accounting Horizons 29 (2).
Big Data Analytics in Financial Statement Audits 429
Accounting Horizons
June 2015
http://www.ifac.org
http://www.ifac.org
https://www.kpmg.com/CH/en/Library/Articles-Publications/Documents/Advisory/pub-20130412-data-analytics-internal-audit-en
https://www.kpmg.com/CH/en/Library/Articles-Publications/Documents/Advisory/pub-20130412-data-analytics-internal-audit-en
dx.doi.org/10.2308/isys-10372
Copyright of Accounting Horizons is the property of American Accounting Association and
its content may not be copied or emailed to multiple sites or posted to a listserv without the
copyright holder’s express written permission. However, users may print, download, or email
articles for individual use.
EXAMPLE MEMO TEMPLATE
Souper Bowl Inc.—December 31, 2016
Disaggregated Revenue Analytics
Purpose: The purpose of this memo is to document plausible trends and expectations for disaggregated revenue data and to identify specific days and locations that warrant further substantive investigation.
Data: We obtained a listing of daily sales by location from the client’s IT system. We tested the details for mathematical accuracy, as summarized in the table below:
Total Sales, 2015
Total Sales, 2016
Store Type 1
$
$
Store Type 2
$
$
Store Type 3
$
$
Total
$
$
Procedures: Based on our risk assessment process, we identified the following assertions as significant risks related to revenues/sales:
· Recorded sales occurred.
· Sales are accurately recorded.
· Sales are recorded in the proper period.
Because Souper Bowl’s operations are solely in the state of Maine, we obtained disaggregated data that reports daily sales by store location and store type. Based on discussions with management and our review of the board of director minutes, we are unaware of any new store locations or other major changes to operations during the year. Therefore, we expect prior year to be a reasonable baseline expectation for this year’s revenues (e.g., similar seasonal trends). Because the business can also be impacted by weather conditions, which vary by year, we also perform analyses that consider changes in weather patterns to predict expected changes from the prior year’s sales. We performed several analytics to identify unusual trends compared to the prior year’s sales, taking weather conditions into consideration. The purpose of these analytics is to identify specific observations (or specific sets of observations) to select for further substantive testing. The analytics that we performed are as follows:
·
Visualization Analysis #1: Title
[Provide a description of the relationship you expected to observe in the data, along with screenshots of the visualization results. Clearly identify (using circles, arrows, etc.) the part of the visualization that leads you to believe that a specific location/day is an anomaly. Ensure that all tables and graphics are properly labeled (x axis, y axis, etc.).]
· Results: [In each of the “results” sections, include a brief summary of your findings so that your manager can see (in words) the way that you interpret the visualization screenshots.]
·
Visualization Analysis #2: Title
· Results:
· [The number of analyses that you perform is up to you. Remember that you want to impress your manager, but you also know that the manager’s time is valuable. Therefore, each analysis that you report should offer new information and conclusions (e.g., avoid repeating the same type of analysis with different coloring, shapes, etc., if the conclusions drawn are the same.)
Conclusion: Based on the procedures described above, the audit team will pull supporting sales information to substantively test transactions from the following locations and days:
· This section of the memo can be achieved using lists or tables, but regardless of the style of presentation, it should clearly describe which item(s) you’re recommending that the audit team look into it further (based on all the analyses above). For each item, you should reference which analysis # the item comes from. The item should be a specific location on a specific day, or a sample of certain days from a set of observations that exhibit the same unusual trend based on your analyses above (e.g., if you identify an unusual relationship for Q4 for location #1001, but you can’t identify one specific day or set of days that is driving the unusual relationship, you may choose to sample from Q4 instead). Remember that it takes time and resources to test each selection, so be strategic in your selections and include justification for your decisions in this section of the memo.
Project 1 Rubric: Data Analytics and Visualization Using Tableau
Top of Form
|
Ranges |
90 – 100% |
80 – 89% |
79 – 0% |
|
|
Criteria |
Exceeds Performance Expectations |
Meets Performance Expectations |
Does Not Meet Performance Expectations |
|
|
Communication |
Clear, correct, and concise use of English grammar with no spelling or punctuation errors. Employs professional writing without colloquial language. Organization is easy to follow and congruent with graduate level writing skills. |
Clear, correct, and concise use of English grammar with some spelling and/or punctuation errors. Employs professional writing with some colloquial language. Organization is not easy to follow but the submission is written at the graduate level. |
Confusing, incorrect, and or wordy use of English grammar with many spelling and or punctuation errors. Employs unprofessional writing with a significant amount of colloquial language. Organization is not easy to follow, and the submission is not written at the graduate level. Or, did not submit. |
|
|
Critical thinking |
Clearly identified explained the main issues, questions, or problems under critical consideration. Accessed more than enough information to investigate the issues or problems. Evaluated the information in a logical and organized manner to determine its value and relevance. Considered and analyzed information in context to the issue or problem. Developed outstanding ideas, conclusions or decisions, and checked them against the most relevant criteria and benchmarks. |
Adequately identified and explained the main issues, questions, or problems under critical consideration. Accessed sufficient information to investigate the issues or problems. Evaluated most of the information in a logical and organized manner to determine its value and relevance. Considered and analyzed most of the information in context to the issue or problem. Developed well-reasoned ideas, conclusions or decisions, and may have checked them against relevant criteria and benchmarks. |
Did not clearly identify explained the main issues, questions, or problems under critical consideration. May not have accessed an sufficient amount of information to investigate the issues or problems. Did not use a logical and organized manner to determine the value and relevance of information. May have considered and analyzed information but not in the context of the issue or problem. Did not develop well-reasoned ideas, conclusions or decisions, and may not have checked them against relevant criteria and benchmarks. Or, did not submit. |
|
|
Risk Assessment, Analysis, and Management |
Correctly identifies & provides accurate & detailed descriptions of the most relevant & serious issues, challenges, and/or problems facing the company. Shows superior knowledge of the company’s current financial situation & strategic issues. Provides a focused diagnosis of the issue(s) & justifies that diagnosis using evidence presented in the case. |
Correctly identifies & provides accurate & detailed descriptions for some of the most relevant & serious issues, challenges, and/or problems facing the company. Shows above average knowledge of the company’s current financial situation & strategic issues. Provides a focused diagnosis of some of the issue(s) & justifies that diagnosis using some evidence presented in the case. |
Does not correctly identify & provide accurate & detailed descriptions for most of the most relevant & serious issues, challenges, and/or problems facing the company. Shows below average knowledge of the company’s current financial situation & strategic issues. Does not provides a focused diagnosis of some of the issue(s) & does not justifies that diagnosis using evidence presented in the case. Or, did not submit. |
|
|
Reporting |
Identified all of the appropriate content to report and used the report template provided in the project. Provided more than enough information to satisfy the reporting requirement. |
Identified most of the appropriate content to report and used the report template provided in the project. Provided enough information to satisfy the reporting requirement. |
Identified some of the appropriate content to report and may or may not have used the report template provided in the project. Did not provide enough information to satisfy the reporting requirement. Or, did not submit. |
|
|
Technology and Tools |
Demonstrated use of Tableau at a high level of proficiency and contributed to helping others in the online discussion group to learn it too. |
Demonstrated use of Tableau at a moderate level of proficiency. Was proactive about seeking help from others in the online discussion group. |
Demonstrated use of Tableau at a low level of proficiency. Was not proactive about seeking help from others in the online discussion group. Or, did not submit. |
|
|
Governance perspective |
Correctly identified all of the relevant issues, standards and guidance. Indicates an exceptional understanding of the topic (connecting it with our readings or other sources). |
Correctly identified an adequate amount of the relevant primary issue(s). Indicates a satisfactory understanding of the topic. |
Did not identify the relevant primary issue(s). Or did not submit. Indicates a limited understanding of the topic, reflecting what other students have already posted or repeating information that was in the assigned project. Or, did not submit. |
|
|
Professional behavior |
Clear evidence of originality. Quoted content includes quotation marks and correct APA in-text citation including the page or paragraph number depending on the format of the source. Non-quoted content that needs to be cited provides correct APA in-text citation. The reference list includes cited sources only. There is no evidence of copying and pasting or other types of plagiarism. |
Clear evidence of originality. Quoted content includes quotation marks and an in-text citation that may contain errors or fail to include the page or paragraph number. Non-quoted content that needs to be cited, provides a mostly-accurate APA in-text citation. The reference list includes cited sources and may erroneously include sources that were not cited. There is no evidence of copying and pasting and or other types of plagiarism. |
Lacks clear evidence of original thoughts. May contain too many quotations thus rendering the paper a series of quotes and not a reflection of what the student thought about and wrote. May include inaccurate in-text citations. The reference list may erroneously include sources that were not cited, contain APA formatting errors, and or failed to provide the source for cited content. May contain evidence of copying and pasting and or other types of plagiarism. Or, did not submit. |
|
|
Decision Making |
Develops effective recommendations, solutions, and/or action plans that specifically solve the strategic issues, challenges, and/or problems identified as the organization’s most relevant & serious. Supports recommendation with convincing evidence. |
Develops some effective recommendation(s) solutions, and/or action plans that specifically solve the strategic issues, challenges, and/or problems identified as the organization’s most relevant & serious. Supports recommendation(s) with insufficient and unconvincing evidence. |
Develops only a few effective recommendation(s) solutions, and/or action plans that only tangentially address the strategic issues, challenges, and/or problems identified as the organization’s most relevant & serious. Does not support recommendation(s) with convincing evidence. Or, did not submit. |
|
|
Exceeds Performance Expectations |
Meets Performance Expectations |
Does Not Meet |
||
|
Collaboration |
Participated in the online discussion group in a stellar way. |
Participated in the online discussion group in a meaningful way. |
Did not participate in the online discussion group in a meaningful way. Or, did not participate. |
|
|
Overall Score |
Exceeds Performance Expectation |
Meets Performance Expectation |
Does Not Meet Performance Expectation |
|
Bottom of Form