According to Exhibit 4-8, which is the best chart for showing a distribution of a single variable, like height? How about hair color? Major in college?
> Which attributes from the College Scorecard data would you need to compare SAT scores across types of institutions (public, private non-profit, or private for-profit)?
> What is included in mastering the data as part of the IMPACT cycle described in the chapter?
> If you were conducting a data analysis in order to compare the percentage of students who receive federal loans at universities above and below the median cost of attendance across all institutions, you would be conducting several steps in your analysis.
> If you were analyzing the levels of diversity across public and private institutions using the College Scorecard data, how would you define diversity in terms of the data provided? Would it be beneficial to combine fields?
> Consider the 2013 declined loan data from Lending Club titled “RejectStatsB2013.” Similar to the analysis done in the chapter, let’s scrub the employment length. Because our analysis requires risk scores, debt-to-income data, and employment length, we ne
> Consider the 2013 declined loan data from LendingClub titled “RejectStatsB2013.” Similar to the analysis done in the chapter, let’s scrub the debt-to-income data. Because our analysis requires risk scores, debt-to-income data, and employment length, we n
> Consider the 2013 declined loan data from LendingClub titled “RejectStatsB2013” from the Connect website. Similar to the analysis done in the chapter, let’s scrub the risk score data. First, because our analysis requires risk scores, debt-to-income data,
> Download the rejected loans dataset of LendingClub data titled “Reject Stats A Ready” from the Connect website and do an Excel PivotTable by state; then figure out the number of rejected applications for each state. Reorder these and make a graph orderin
> 1. Mastering the data can also be described via the ETL process. The ETL process stands for: a. Extract, total, and load data. b. Enter, transform, and load data. c. Extract, transform, and load data. d. Enter, total, and load data. 2. The goal of the ET
> 1. Big Data is often described by the three Vs, or a. Volume, velocity, and variability. b. Volume, velocity, and variety. c. Volume, volatility, and variability. d. Variability, velocity, and variety. 2. Which approach to Data Analytics attempts to assi
> 1. The DuPont analysis of return on equity (ROE) includes all of the following component ratios except: a. Asset turnover. b. Inventory turnover. c. Financial leverage. d. Profit margin. 2. XBRL stands for: a. Extensible Business Reporting Language. b. E
> 1. What would you consider to be Financial Performance KPIs? a. Total Shareholder Return b. Customer Profitability Score c. Market Growth Rate d. Klout Score 2. What would you consider to be an Operational KPI? a. Inventory Shrinkage Rate b. Brand Equity
> In the ETL process, the first step is extracting the data. When you are obtaining the data yourself, what are the steps to identifying the data that you need to extract?
> 1. Which items would be currently out of scope for Data Analytics? a. Direct observation of processes b. Evaluation of time stamps to evaluate workflow c. Evaluation of phantom vendors d. Duplicate payment of invoices 2. What would be the sampling interv
> 1. Under the guidance of the chief audit executive (CAE) or another manager, these individuals build teams to develop and implement analytical techniques to aid all of the following audits except: a. Process efficiency and effectiveness. b. Governance, r
> 1. Gold, silver, and bronze medals would be examples of: a. Nominal data. b. Ordinal data. c. Structured data. d. Test data. 2. In the late 1960s, Ed Altman developed a model to predict if a company was at severe risk of going bankrupt. He called his sta
> 1. Is a set of data used to assess the degree and strength of a predicted relationship. a. Training data b. Unstructured data c. Structured data d. Test data 2. Data that are organized and reside in a fixed field with a record or a file. Such data are
> Can you think of any other settings, besides financial reports, where tagged data might be useful for fast, accurate analysis generally completed by computers? How could it be used in a hospital setting? Or at your university?
> Go to finance.yahoo.com and type in the ticker symbol for Apple (AAPL) and click on the statistics tab. Which of those variables would be useful in assessing profitability?
> Go to https://xbrl.us/data-rule/dqc_0015-lepr/ and find the XBRL element name for Other NonOperating Income and indicate whether XBRL says that should normally be a debit or credit entry.
> Go to https://xbrl.us/data-rule/dqc_0015-lepr/ and find the XBRL element name for Interest Expense and Sales, General, and Administrative expense.
> Why do audit firms perform analytical procedures to identify risk? Which type of ratios (liquidity, solvency, activity, and profitability ratios) would you use to evaluate the company’s ability to continue as a going concern?
> Would you recommend the Securities and Exchange Commission require the use of sparklines on the face of the financial statements? Why or why not?
> Describe the IMPACT cycle. Why does its order of the processes and its recursive nature make sense?
> Which would you predict would have more positive sentiment in a 10-K, the financial statements or the MD&A (management discussion and analysis) of the financial statements? More positive sentiment in the footnotes or MD&A? Why?
> In which of the four components of a Balanced Scorecard would you put the Walton College’s diversity initiative? Why do you think this is important for a public institution of higher learning?
> If the data underlying your digital dashboard are updated in real time, why would you want to update your digital dashboard in real time? Are there situations when you would not want to update your digital dashboard in real time? Why or why not?
> Why is Customer Retention Rate a great KPI for understanding your Tesla customers?
> For a company like Walmart, how would the Balanced Scorecard help balance the desire to be profitable for its shareholders with continuing to develop organizational capacity to compete with Amazon (and other online retailers)?
> For an accounting firm like PwC, how would the Balanced Scorecard help balance the desire to be profitable for its partners with keeping the focus on its customers?
> Amazon, in the author’s opinion, has cared less about profitability in the short run but has cared about gaining market share. Arguably Amazon gains market share by taking care of the customer. Given the “Suggested 75 KPIs That Every Manager Needs to Kno
> We know that a Balanced Scorecard is comprised of four components: financial (or stewardship), customer (or stakeholder), internal process, and organizational capacity (or learning and growth). What would you include in a dashboard for the internal proce
> We know that a Balanced Scorecard is comprised of four components: financial (or stewardship), customer (or stakeholder), internal process, and organizational capacity (or learning and growth). What would you include in a dashboard for the financial and
> One type of descriptive analytics is simply sorting data. Why is seeing extreme values helpful (minimums, maximums, counts, etc.) in evaluating accuracy and completeness and in potentially finding errors and fraud and the like?
> How might Data Analytics be used in financial reporting? And how might it be used in doing tax planning?
> An example of prescriptive analytics is when an action is recommended based on previously observed actions. For example, an analysis might help determine procedures to follow when new accounts are opened for inactive customers, such as requiring supervi
> Using Table 6-2 as a guide, compare and contrast predictive and prescriptive analytics. How might these be used in an audit? Or a continuous audit?
> When do you believe that Data Analytics will add value to the audit process? How can it most help?
> How do nature, extent, and timing of audit procedures help us identify when to apply Data Analytics to the audit process?
> Why would audit firms prefer to use proprietary workpapers rather than just storing working papers on the cloud?
> Would an auditor view heterogeneous systems as an audit risk? Why or why not?
> Why is it better to extract data from a data warehouse than a production or live system directly?
> How does the systems translator software work? How does it store the merged data into a data warehouse?
> Is it possible for multinational firms to have many different financial reporting systems and ERP packages all in use at the same time?
> Is it possible for a firm to have general journals from a product like JD Edwards actually reconcile to the general ledger in SAP? Why or why not?
> Even though it is preferable to store data in a relational database, storing data across separate tables can make data analysis cumbersome. Describe three reasons why it is worth the trouble to store data in a relational database.
> Why has most innovation in Data Analytics originated more in an internal audit than an external audit? Or if not, why not?
> The text mentions, “If your data analysis project is more declarative than exploratory, it is more likely that you will perform your data visualization to communicate results in Excel.” In your opinion, why is this true?
> Box and whisker plots (or box plots) are particularly adept at showing extreme observations and outliers. In what situations would it be important to communicate these data to a reader? Any particular accounts on the balance sheet or income statement?
> Explain Exhibit 4-2 and why these four dimensions are helpful in describing information to be communicated? Exhibit 4-2 lists conceptual and data-driven as being on two ends of the continuum. Does that make sense, or can you think of a better way to orga
> How does fuzzy match work? Give an accounting situation where it might be most useful
> How is similarity matching different from clustering?
> How might classification be used in approving or denying a potential fraudulent credit card transaction?
> How might the data reduction approach be used in auditing?
> What is the difference between training data sets and test (or testing) data sets?
> The advantages of a relational database include limiting the amount of redundant data that are stored in a database. Why is this an important advantage? What can go wrong when redundant data are stored?
> What is the difference between a supervised and an unsupervised approach?
> Figures 3-1 through 3-4 suggest that volume and distance are the best predictors of “days to ship” for a wholesale company? Any other variables that would also be useful in predicting the number of “days to ship”?
> Compare and contrast the profiling data approach and the development of standard cost for a unit of production at a manufacturing company? Are they substantially the same or do they have differences?
> What is the purpose of a data dictionary? Identify four different attributes that could be stored in a data dictionary, and describe the purpose of each.
> Among the advantages of using a relational database is enforcing business rules. Based on your understanding of how the structure of a relational database helps prevent data redundancy and other advantages, how does the primary key/foreign key relationsh
> In the ETL process, one important step to process when transforming the data is to work with NULL, N/A, and zero values in the dataset. If you have a field of quantitative data (e.g., number of years each individual in the table has held a full-time job)
> The advantages of a relational database include integrating business processes. Why is it preferable to integrate business processes in one information system, rather than store different business process data in separate, isolated databases?
> Give an example of how Data Analytics creates value for accounting.
> Give an example of how Data Analytics creates value for businesses.
> What is the difference between a target and a class?
> Which attributes from the College Scorecard data would you need to compare cost of attendance across types of institutions (public, private non-profit, or private for-profit)??
> According to the text and your own experience, why is Tableau ideal for exploratory data analysis?
> To address the question “Will I receive a loan from LendingClub?” we had available data to assess the relationship among (1) the debt-to-income ratios and number of rejected loans, (2) the length of employment and number of rejected loans, and (3) the cr
> What would be the best chart to use to illustrate earnings per share for one company over the past five years?
> Based on the data from datavizcatalogue.com, how does a box and whisker plot show if the data are symmetrical?
> Based on the data from datavizcatalogue.com, what are some major flaws of using word clouds to communicate the frequency of words in a document?
> Based on the data from datavizcatalogue.com, a line graph is best at showing comparisons, relationships, compositions, or distributions? Name the best two.
> Using Figure 3-5 as a guide, what are three data approaches associated with the supervised approach?
> What data approach mentioned in the chapter might be used by Facebook to find friends?
> In the chapter, we mentioned eight different data approaches. Which data approach was used by Alibaba, as mentioned in the chapter-opening vignette?
> In the ETL process, if the analyst does not have the security permissions to access the data directly, then he or she will need to fill out a data request form. While this doesn’t necessarily require the analyst to know extraction techniques, why does th
> Why is identifying the question such a critical first step in the IMPACT process cycle?
> Using Figure 3-5 as a guide, what are three data approaches associated with the unsupervised approach?
> Why might the debt-to-income attribute included in the declined loans dataset considered in the chapter be a predictor of declined loans? How about the credit (risk) score?
> Download the rejected loans dataset of LendingClub data titled “RejectStatsA Ready” from the Connect website and do an Excel PivotTable by state; then figure out the number of rejected applications for the state of Arkansas. That is, count the loans by s
> Download and consider the rejected loans dataset of LendingClub data titled “RejectStatsA Ready.” Given the analysis performed in the chapter, what three items do you believe would be most useful in predicting loan acceptance or rejection? What additiona
> Download and consider the data dictionary file “LCDataDictionary,” specifically the LoanStats tab. This represents the data dictionary for the loans that were funded. Seeing all of the data attributes listed there, which attributes do you think might pre
> Go to Loughran and McDonald’s sentiment word lists at https://www3.nd.edu/~mcdonald/Word_Lists.html and download the Master Dictionary. These are what they’ve used to assess sentiment in financial statements and related financial reports. Give five words
> Go to Loughran and McDonald’s sentiment word lists at https://www3.nd.edu/~mcdonald/Word_Lists.html and download the Master Dictionary. These are what they’ve used to assess sentiment in financial statements and related financial reports. Give five word
> The preceding question asked you to figure out how the stock market responded to Amazon’s announcement that it would purchase Whole Foods. The question now is if the stock market for Amazon had higher trade volume on that day than the average of the mont
> You’re asked to figure out how the stock market responded to Amazon’s announcement on June 16, 2017, that it would purchase Whole Foods—arguably a transformational change for Amazon, Walmart, and the whole retail industry. Required: a. Go to finance.yaho
> We noted in the text that negative words in the financial dictionary include words like loss, claims, impairment, adverse, restructuring, and litigation. What are other negative words might you add to that list? What are your thoughts on positive words t
> Can you think of situations where sentiment analysis might be helpful to analyze press releases or earnings announcements? What additional information might it provide that is not directly in the overall announcement? Would it be useful to have sentiment
> Why is Order Fulfillment Cycle Time an appropriate KPI for a company like Wayfair (which sells furniture online)? How long does Wayfair think customers will be ready to wait if Amazon Prime promises items delivered to its customers in two business days?
> Which data approach might be used to assess the appropriate level of the allowance for doubtful accounts?
> If Time to Market is considered a key KPI for a company, what would be an appropriate benchmark? The industry’s Time to Market? The average Time to Market for the company for the past five years? The competitors’ Time to Market? a. How will you know if t
> If ROA is considered a key KPI for a company, what would be an appropriate benchmark? The industry’s ROA? The average ROA for the company for the past five years? The competitors’ ROA? a. How will you know if the company is making progress? b. How might
> How does Data Analytics help facilitate the use of the Balanced Scorecard and tracking KPIs? Does it make the data more timely? Are you able to access more information easier or faster, or what capabilities does it give?
> From Exhibit 7-5, choose 10 Marketing KPIs to answer the following three questions. This URL (https://www.linkedin.com/pulse/20130905053105-64875646-the-75-kpis-everymanager- needs-to-know) provides links with background information for each individual K
> From Exhibit 7-5, choose 10 Employee Performance KPIs to answer the following three questions. This URL (https://www.linkedin.com/pulse/20130905053105-64875646-the- 75-kpis-every-manager-needs-to-know) provides links with background information for each
> From Exhibit 7-5, choose 5 Financial Performance KPIs to answer the following three questions. This URL (https://www.linkedin.com/pulse/20130905053105-64875646-the- 75-kpis-every-manager-needs-to-know) provides links with background information for each
> How do you think sentiment analysis of the 10-K might assess the level of bias (positive or negative) of the annual reports? If management is too positive about the results of the company, can that be viewed as being neutral or impartial?
> How artificial intelligence could be used to help with the evaluation of the estimate for the allowance for doubtful accounts? Could past allowances be tested for their predictive ability that might be able to help set allowances in the current period?
> Which distributions would you recommend be tested using Benford’s law? What would a Benford’s law evaluation of sales transaction amounts potentially show? What would a test of vendor numbers or employee numbers show? Anything different from a test of in
