Analyze the Data: Exploratory Business Analytics

Analyze the Data: Exploratory Business Analytics �(Descriptive and Diagnostic Analytics)

Explain where ‘Analyze the Data’ fits in the SOAR Analytics Model

There are all sorts of business questions we could and ask, but we can group most of them into these five types of relatively simple questions that correspond to the five types of analytics:
Descriptive Analytics: What happened? What is happening?
Diagnostic Analytics: Why did it happen? What are the causes for past results? Why are results different than expectations?
Predictive Analytics: Will it happen in the future? What is the probability something will happen? Can we forecast what will happen?
Prescriptive Analytics: What should we do, based on what we expect will happen? How do we optimize our performance based on potential constraints?
Adaptive Analytics: How can we continuously learn using artificial intelligence? Can we learn from past and current events with adaptive learning?

Exploratory data analytics as initial descriptive and diagnostic data analytics investigations to summarize and explain performance and activity.
Exploratory analytics explores the data and begins to generate questions and hypotheses that need further explanation.
Exploratory data analytics includes the practice of exploring the data by summary performance statistics, anomaly identification, and pattern detection.
Confirmatory Data analytics are predictive and prescriptive analytics that use statistics to judge the likelihood (probability) of a future event or outcome occurring.

1. Introduction to Business Analytics © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 1 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

2. Analyze the Data: Exploratory Business Analytics (Descriptive and Diagnostic Analytics) Chapter 4 2 © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

3. Learning Objectives 4.1 Explain where ‘Analyze the Data’ fits in the SOAR analytics model. 4.2 Describe the five types of data analytics: descriptive, diagnostic, predictive, prescriptive, and adaptive/autonomous analytics. 4.3 Define exploratory analytics and confirmatory analytics. 4.4 Define descriptive analytics and enumerate its various techniques. 4.5 Define diagnostic analytics and describe its techniques. 4.6 Describe and use the techniques in Excel’s Data Analysis Toolpak . 3 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

4. Explain where ‘Analyze the Data’ fits in the SOAR Analytics Model LO 4.1 © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 4 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

5. The SOAR Analytics Model S pecify the Question O btain the Data A nalyze the Data R eport the Results EXHIBIT 4.1 The SOAR Analytics Model 5 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

6. Progress Check 4.1 Q: Is “Analyze the Data” done before or after finding the appropriate data to address the question? Why? © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 6 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

7. Describe the Five Types of Analytics LO 4.2 © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 7 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

8. 8 Five Questions – Five Analytics Types There are all sorts of business questions we could and ask, but we can group most of them into these five types of relatively simple questions that correspond to the five types of analytics: Descriptive Analytics: What happened? What is happening? Diagnostic Analytics: Why did it happen? What are the causes for past results? Why are results different than expectations? Predictive Analytics: Will it happen in the future? What is the probability something will happen? Can we forecast what will happen? Prescriptive Analytics: What should we do, based on what we expect will happen? How do we optimize our performance based on potential constraints? Adaptive Analytics: How can we continuously learn using artificial intelligence? Can we learn from past and current events with adaptive learning? © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

9. 9 Descriptive Analytics – What Happened? Marketing Operations Accounting Finance Descriptive Analytics What happened? What is happening? How many units were sold in the period? By region? By store? What was the average revenue for each month? What are the top 10 products in terms of sales and profit? How many web page views resulted in a sale? What is the customer fill rate? What is the average supplier lead time? What is the average customer service level? What is the annual employee turnover rate? What division has the highest turnover rate? What is our average time-to-fill (an employee position)? How much did we pay in federal taxes last year? Which product is the most profitable one for the company? What is the balance of inventory on hand? What is the effective tax rate paid? How big is the difference between taxable income and net income? What are the descriptive performance statistics (e.g., means, medians, maximums and minimums) for the retail industry? What was the Return on Asset, Asset Turnover, Return on Profits ratios last year? © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

10. 10 Diagnostic Analytics – Why did it Happen? Marketing Operations Accounting Finance Diagnostic Analytics Why did it happen? What are the reasons for past results? Can we explain why it happened? Why did revenue change? Why did web page views change? Why did the click-through rate change from last month to this month? Why did customer fill rate change? Why did average supplier lead time change? Why did average customer service level change? Why has employee turnover changed? Why has time-to-fill changed? Why is revenue up on the west coast but down in the Midwest? Why did SG&A (sales, general and administrative) costs increase as compared to the industry? Why is the difference between taxable income and net income getting bigger? Why did the interest rate increase for the company overall? Why did overall income tax increase even though net income did not? Why did stock returns increase last year in the U.S. as compared to China? Why did bond prices fall? © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

11. 11 Predictive Analytics – What is Likely to Happen in the Future? Marketing Operations Accounting Finance Predictive Analytics Will it happen in the future? What is the probability something will happen? Is it forecastable? What will product sales (and units) be? What will web page views be? Does the weather predict sales of certain products? Can we forecast demand for our new product? What happens if the company changes its inventory policy (e.g., order quantity)? How long will an employee stay with the company? What will the demand for our products be based on new products from our competitors? Can we forecast future revenues/cash flows/earnings with reasonable accuracy? Can we predict if the financial statements might be misstated or might be fraudulent? What is the chance the company will go bankrupt? Do we extend credit or not to customers based on customer characteristics (credit score, payment history, existing debt, etc.)?   © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

12. 12 Prescriptive Analytics – What action should we take? Marketing Operations Accounting Finance Prescriptive Analytics What should we do, based on what we expect will happen? How do we optimize our performance based on potential constraints? What product recommendations might increase conversion rates? How can we improve customer sentiment? What is the optimal inventory policy to maintain a specified item fill rate? What is the optimal level of production? What is the optimal scheduling policy? What is the best delivery route (in terms of costs and time)? Where should the warehouses be located? How many employees are needed for the summer season? Which employees need to go to training to improve productivity (before productivity declines or depart)? Should the company buy or lease their headquarters office building? Should the company make its products or outsource to other producers? What is the level of sales that will allow us to breakeven? What is optimal for the company? Move its operations to Ireland to minimize taxes or stay in the USA? Should the company move operations to Ireland to minimize taxes? How will our taxes change if certain tax laws change in the next U.S. Congress legislative section? What is the optimal investment: Real estate, stocks or bonds? © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

13. 13 Adaptive/Autonomous Analytics – How can we continuously learn? Marketing Operations Accounting Finance Prescriptive Analytics How can we continuously learn using artificial intelligence? How can we learn from past and current events with adaptive learning? Is our current marketing campaign working? How should we adjust our strategy today? How should we adjust our strategy given the way we expect the competition/industry to change? How do we continuously update our predictions of needed inventory to more closely meet customer demand? Could a computer learn from the decisions of the internal audit departments which transactions might constitute fraud (and require further investigation)? Can the company rebalance its investment portfolio to optimize its portfolio based on the change in oil prices last week? What opportunities do changes in the exchange rates for foreign currencies present? © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

14. Progress Check 4.2 Q. How is the investigation of which product recommendations affect conversion rates (getting interested observers to actually buy the products rather than just read reviews) be an example of prescriptive analytics instead of diagnostic or predictive analytics? © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 14 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

15. Define Exploratory Data Analytics LO 4.3 © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 15 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

16. 16 Exploratory Data Analytics Exploratory data analytics as initial descriptive and diagnostic data analytics investigations to summarize and explain performance and activity. Exploratory analytics explores the data and begins to generate questions and hypotheses that need further explanation. Exploratory data analytics includes the practice of exploring the data by summary performance statistics, anomaly identification, and pattern detection. Confirmatory Data analytics are predictive and prescriptive analytics that use statistics to judge the likelihood (probability) of a future event or outcome occurring. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

17. Descriptive Analytics LO 4.4 17 © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

18. 18 Define Descriptive Analytics Descriptive analytics addresses the questions of “What happened?” or “What is happening?”. We define descriptive analytics as analytics performed which characterizes, summarizes, and organizes features and properties of the data to facilitate understanding of the results and the underlying data. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

19. 19 Business Data Sources to Consider in Descriptive Analytics Human Resource Management Systems (HRMS) Customer Relationship Management Systems (CRM) Supply Chain Management Systems (SCM) Financial Reporting (Accounting) Systems (FRS) © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

20. 20 Human Resource Management Systems A human resource management system (HRMS) is an information system for managing all interactions with current and potential employees. Data Included: Recruiting data and leads Employee training (current and desired certifications) Payroll and compensation (including tax elections like 401k contributions, # of dependents, amount of state and federal withholding, etc.) Employee benefits (stock options, bonuses, health insurance, etc.) © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

21. 21 Customer Relationship Management System A customer relationship management (CRM) system is an information system for managing all interactions with current and potential customers. Data Included: Customer contact history: What happened in the interaction (what they asked about or what was decided)? Any interest in new products or services? Customer order history. Customer level of trade discounts or payment terms. Customer credit score (is their credit improving or declining?). © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

22. 22 Supply Chain Management Systems A company’s supply chain is the sequence of processes to get a good or product from original production to final customer delivery. The system used to track those supply chain processes is called supply chain management systems. Data includes: active vendors (their contact info, where payment should be made, how much should be paid), the orders made to date (how much, when the orders are made), or demand schedules for what component of the final product is needed when and where. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

23. 23 Financial Reporting Systems Financial reporting systems (FCS), or accounting systems, are systems that capture and measure financial transactions and communicate financial performance to interested parties. Financial statements are collection of reports that communicate a company’s financial results, financial condition, financial health and its cash flows. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

24. Descriptive Analytics Techniques 24 What would you use to address the questions of “What happened?” or “What is happening?”. What would you use to “ characterize, summarize, and organize ” what happened? © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

25. Descriptive Analytics Techniques Mean, Median, Mode What is the average employee salary? What is the average stock market return over the past five years? Minimums, Maximums What are the descriptive performance statistics (e.g., means, medians, maximums and minimums) for the retail industry last year? Standard Deviation What is the standard deviation of the company’s stock price over the last quarter? Quartiles/Deciles What quartile is Ford in based on its leverage compared to the automobile industry as a whole? Counts How many sales transactions did we have last year? Totals, Sums, Subtotals What is the average supplier lead time? What is the average customer service level? 25 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

26. More Descriptive Analytics Techniques Graphs (Bar Charts) What is our average time-to-fill an employee position over time? And how has that changed over time? Percentage Change What is the percentage increase in sales, general and administrative expenses from last year to this year? PivotTables What is the total profitability of each customer or each inventory item? Histogram What is the frequency of aged receivables in each 30-day bucket? Ratio Analytics The computation of the debt-to-equity ratio as a measure of solvency. Vertical Analysis The comparison of interest expense in relation to net sales revenue from one period to the next. Horizontal Analysis What is the percentage increase in accounts receivable from the prior balance to the current balance sheet? 26 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

27. Descriptive Statistics (From Excel Data Analysis Toolpak ) EXHIBIT 4.4 Descriptive Statistics for Exotic Fruits (From Lab 4.1 Excel) 27 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

28. 28 Horizontal Analysis Horizontal analysis is a descriptive analysis technique that calculates and displays the dollar and percentage change in financial statement line items from one period to the next. Exhibit 4.6 Horizontal Analysis for Tyson Foods Inc. Income Statement © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

29. 29 Vertical Analysis Vertical analysis examines financial performance over a period of time by expressing financial information in relation to some relevant figure, or base. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

30. Diagnostic Analytics LO 4.5 © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 30 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

31. Define Diagnostic Analytics Diagnostic analytics is performed to investigate the underlying reasons for past results that cannot be answered by simply looking at the descriptive data. 31 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

32. Define Diagnostic Analytics Diagnostic analytics is broken down into two broad categories to help determine “Why Something Happened?”: Identifying Anomalies and Outliers Finding Previously Unknown Linkages, Patterns, or Relationships between Variables Performing Drill-Down Detailed Analytics Performing Statistical Analyses 32 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

33. 33 Identifying Anomalies and Outliers Sometimes analysts use diagnostic analytics to help find an anomaly, which is something that departs, or deviates, from the expected, or the norm. In many ways, an outlier is similar to an anomaly, it is an observation, or a data point, that lies outside its expected distribution. It could just be a mistake, or the result of a fraud or it could be truth. To detect an anomaly or an outlier, it is important to establish the norm, or the expectation. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

34. © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 34 Identifying Anomalies and Outliers: What’s the Expectation? Branch of Business The Business Analysts’ Expectation Example of Anomaly Possible Explanation for the Anomaly/Outlier Marketing The increase in sales is roughly equivalent to the increase in sales for other companies in the same industry Our companies’ sales grew at twice the rate of our competitor. New products sold particularly well, causing an increase in sales greater than the competition Operations The employee turnover rate will be roughly equivalent to the rate experienced last year by our company. The employee turnover is 25% higher than what the company experienced last year. Other businesses in the workplace are in need of skilled workers and are offering higher compensation packages due to their unique skills. Accounting Accountants record transactions in conformity with Generally Accepted Accounting Principles (GAAP) PwC found its client capitalized rather than expensing R&D expenditures, contrary to GAAP. The company bought testing equipment for future R&D projects. Should it be capitalized? Finance Our company’s bond prices are priced just lower than the U.S. government treasury yields. Our company’s bond prices fell in relation to the U.S. government treasury yields. Due to poor financial performance, our bonds were graded as risky, thus lowering the bond price. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

35. 35 Diagnostic Techniques to Find Anomalies Technique Expectation Potential Anomaly How Test Works to Identify Anomaly Cash/Bank Reconciliation All cash transactions recorded by the company are also recorded by the bank. Some cash transactions are recorded by the company or by the bank but not both. Conditional formatting or Excel Match () commands highlights transactions not recorded by both (Illustrated in Lab 4.2). Bank reconciliations are used to make sure all transactions are recorded properly. Benford’s Law As part of Benford’s law, the first digit of naturally occurring numerical datasets follow an expected distribution. The leading digit of some naturally occurring datasets does not follow the expected distribution suggesting something that requires further investigation. The test of Benford’s Law compares first digits of numbers in the dataset to first digits of numbers expected by Benford’s Law (Illustrated in Lab 4.3). Benford’s Law is often used to identify fraud. Testing for Duplicate Transactions Each transaction is recorded once and only once. Each transaction is sometimes duplicated, leading to errors. Duplicate transaction test looks for the same amount recorded two or more times in order to correct possible errors. Fuzzy Matching Different names represent different individuals. Different addresses represent different locations. Although names or addresses might not be exactly the same, they actually represent the same person. Fuzzy matching attempts to find a match which, although not a 100 percent match, is above the threshold matching percentage set by the application (Illustrated in Lab 9-1). © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

36. Benford’s Law – Theoretical Distribution of the First Digit of a Set of Naturally Occurring Numbers 36 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

37. Actual vs. Expected Distribution of First Digits – Can It Identify Fraud? 37 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

38. Benford’s Law What’s the benchmark? What’s the outlier? What’s the anomaly? 38 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

39. 39 Diagnostic Analytics to Find Previously Unknown Linkages, Patterns, or Relationships between Variables Performing Drill-Down Detailed Analytics – By looking for patterns in the underlying data set or an external data set, we can identify potential correlations. Drill-down analytics works to summarize data at different levels and uncover additional details to understand why something happened. Performing Statistical Analyses – This category of data analytics works to uncover statistical patterns in the data, or how data moves together. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

40. Moneyball! The movie starring Brad Pitt tells the true story of finding patterns to identify undervalued talent for the Oakland Athletics in Major League Baseball. This is an example of using analytics to find previously unknown patterns. 40 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

41. Drill-Down Tools/Techniques Used in Diagnostic Analytics Drill into the analytics to help explain why or how something has occurred: Analysis of the Highest Owing Customer: Which customers included in the total accounts receivable owing have the highest balances outstanding? Do some accounts need to be written off due to uncollectibility? Analysis of the Most Profitable Customer: By learning which customers are most profitable, the company can better work and cater to these customers. Analysis of the Most Profitable Product: By learning which products are most profitable, the company can better promote them to their customers. At the same time, they will be learning which products are not profitable and either work to make them more profitable (raise sales price, get product from another cheaper source), or just cut them from their offering altogether. 41 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

42. 42 Statistical Analysis © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

43. Hypothesis Testing Hypothesis Testing using a Difference in Means: Are mean production cycle times in the Phillipines the same or different from mean production cycle times in Vietnam? Hypothesis Testing using Regression: Is employee turnover associated with rapidly rising wages in the economy? 43 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

44. Progress Check 4.5 Q. You’ve developed a hypothesis that it is faster to source artificial Christmas trees from Indonesia (to Seattle) than from the Philippines (to Seattle). Using statistical analysis, how would you test that hypothesis? Why is this an example of diagnostic analytics? © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. 44 © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

45. Introduction to the Data Analysis Toolpak (Excel) LO 4.6 45 © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

46. 46 Excel’s Data Analysis Toolpak Offers a Variety of Analytics Tools EXHIBIT 4.17 Analytics Techniques Available within the Data Analysis Toolpak © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

47. 47 Some Available Techniques in Excel’s Data Analysis Toolpak Correlation: To understand if and the extent to which variables are related to each other Descriptive statistics: To understand the basic statistics, including the mean (average), standard deviation, and minimums and maximums, of a data set Histogram: To understand the frequency of the data using a display of rectangles whose areas are proportional to the underlying frequency of the data Regression: To understand the relationship between specific inputs and outputs t -test: To understand the probability of a statistical difference in averages between two independent samples or between a paired sample through time © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

48. 48 Progress Check 4.6 Q. Which data analysis toolpak technique would be use to test if sales at one store are statistically greater than sales at another store? © 2022 McGraw Hill. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw Hill. © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.

49. Labs Associated with Chapter 4 Lab # Lab Name 4.1 Excel: Evaluating Inventory Using Inventory Turnover, Waste and Profit Margins 4.2 Excel: Using Conditional Formatting to Perform a Bank Reconciliation 4.3 Excel: Applying Benford’s Law © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.