Excel Analytics Lab Chapter 10

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Nov 14, 2023
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LO 10-6 Describe the first stage of the AMPS model— asking appropriate questions. LO 10-7 Describe the second stage of the AMPS model—mastering the data. Chapter 10 Data Analytics in Accounting: Concepts and the AMPS Model 257 The AMPS Model: Ask the Question The best way to develop critical thinking skills is to ask questions, which is the first step in the analytics mindset."" Given that analyzing data strengthens critical thinking (and vice versa),'® students should ask questions, which they can often solve using data and data analytics, Ask questions that might address a problem the company is facing, such as: 1. Which product is most profitable at stores in Missouri? 2. Is it more profitable to produce an item in the United States or in Mexico (or Indonesia)? 3. How much overhead should we apply to each one of our products? 4, Why are our costs increasing in the West but decreasing in the East? 5. What is the probability that our audit client will go bankrupt or need to restate its financial statements? 6. How will the company respond to the various possible scenarios considered in tax legislation? 7. How will increasing petroleum prices and shipping expenses affect our level in sales to break even? Generally the more succinct the question, the better. For example, it is hard to think about a question like "How does Walmart grow net income?" but easier to potentially address a question like "How do we sell more bicycles at Walmart in Fayetteville, Arkansas, store 3597" Narrowing the scope of the question helps enhance the focus on a specific question. In data analysis, the axiom "your data won't speak unless you ask it the right data analy- sis questions™!" really speaks to the expertise the accountant can offer by asking questions that are answerable by the data. Given accountants' knowledge of business processes, how information flows through the organization (from customer to order desk to shipping dock to customer), and how and when transactions hit the income statement, accountants can help management create specific questions to address the heart of the problem, opportunity, or challenge at hand. Moreover, accountants should have a thorough knowledge of an organization's data to determine what internal data can potentially answer the question and what additional (external) information should be gathered. Once we know what question we are addressing and we know what data might be avail- able to address the question, we can continue to the next step of accessing appropriate data to ask the question. The AMPS Model: Master the Data Once the accountant understands the question, he or she starts to consider appropriate data that could be used to address the question. Data questions quickly arise, such as the following: 1. Data accessibility < What data will be needed to answer the question? Do we have access fo it? What is the potential cost of acquiring and processing the data as compared to the potential value provided by use of the data? 17, Sullivan, "How Does Bloom's Taxonomy Relate to Critical Thinking Information?" CLASSROOM, 2018, hitps://classroom.synonym.com/blooms-relate-critical-thinking-information-6233382.htmi (accessed January 21, 2019). 8K, F. Reding and C. Newman, "Improving Critical Thinking through Data Analysis," Strategic Finance, June 2, 2017, hitps//sfmagazine.com/post-entry/june-20 1 7-improving-critical-thinking-through-data- analysis/ (accessed January 21, 2019). 19M. Lebied, "Your Data Won't Speak unless You Ask it the Right Data Analysis Questions," datapine, June 21, 2017, hitps://www.datapine.com/blog/data-analysis-questions/ (accessed January 23, 2018).
258 Chapter 10 Data Analytics in Accounting: Concepts and the AMPS Model L0 10-8 Describe how audit data standards are useful in sharing data between a corpany and its auditors in preparation for data analysis. 2. Data reliability + s it clean, reliable data? Does it have lots of missing values? Does it need to be cleaned or transformed in some way before it can be used? - How old is the data? Wil it address the question we have now if it is really old data? 3. Data integrity » Does the data exhibit high levels of data integrity, where data is accurate, valid, and consistent over time? Accountants need to understand the trade-offs between relevant data and reliable data (such as data that might exhibit more representational faithfulness). 4. Data type Arethere privacy concerns associated with our data? Are we aliowed to use it? What would happen if there is a data breach and the data is exposed? + Is the data unstructured, semi-structured, or structured? Does this help determine how we will use it in our analysis? - Isthe data internal or external to the company? + Is the data machine readable? If the data item is stuck in a PDF file, will it be hard to extract and use in our analysis? + Does the data come categorical or numerical? Whereas numerical data has logical order, categorical data has no logical order and can't be easily translated into a numerical value (like sex or hair color). Exchange of Data between Auditors and Audit Clients: The Use of Audit Data Standards We noted above that the extract, transform, and load (ETL) processes to get needed data for analysis can take a great deal of effort—as much as 50 percent to 90 percent of the data analyst's time. External auditors (like Grant Thornton, PwC, Deloitie, Bob's Accounting Firm, etc.) require clients to share their data that is to be audited. If both the audit client and its exter- nal auditor agreed on the same data standards to share their data, this cost of cleaning and formatting the data could be alleviated. For this reason, the American Institute of Certified Public Accountants (AICPA) worked to develop Audit Data Standards (ADS). ADS is a set of standards for data files and fields typically needed to support an external audit in a given financial business process area. These standards also include questionnaires that may need to be considered to ensure that the data to be accessed is a complete and valid population. While the AICPA's most immediate goal is to support the financial statement audit process, in practice there may be very similar data requirements for external audit, internal audit, and compliance testing.20 Because there is wide variability in the file and field names and data types in underlying accounting and enterprise systems, the objective of the ADS is to produce data in a stan- dard structure that can then be used consistently across financial audits of most organiza- tions. The potential benefits include the following: e Reduces the time and effort involved in accessing data by: = Providing a precise request of what data is required and the format in which it should be provided. m Reducing the risk that incorrect or incomplete data will be provided by IT. = Reducing the need for an IT specialist to clean or scrub the data. 2Paudit Standard Library (as of July 2015), http/fwww.aicpa.org/interestareas/frc/assuranceadvisoryser- vices/pages/auditdatastandardworkinggroup.aspx (accessed February 6, 2019).
FIGURE 10.5 Standards for the General Ledger under Audit Data Standards Source: hitps://www.aicpa .org/TnterestAreas/FRC/ AssuranceAdvisoryServices/ DownloadableDocuments/ AuditDataStandards/ AuditDataStandards GL.July2015.pdf (accessed February 6, 2019). Chapter 10 Data Analytics in Accounting: Concepts and the AMPS Model ~ 259 m Potentially having financial reporting systems (such as SAP or Oracle) to ontput this information directly. o Works well with standard audit and risk analytic tests often run against data sets in specific accounts or groups of accounts (such as inventory or accounts receivable or sales revenue transactions). o Allows software vendors, such as ACL Ine., to produce data extraction programs for given enterprise systems to help facilitate fraud detection and prevention and risk management. Facilitates testing of the full population of transactions, rather than just a small sample. Connects/interacts well with XBRL GL Standards (as introduced in Chapter 9.) In Figure 10.5, we present the general ledger standards suggested under the Aundit Data Standards by the AICPA. You'll note the fields, field names, data types, and lengths, as well as a description of what is included in the general ledger. Note, for example, field 12, which specifies whether this is a debit or credit as well as field 17, which is the User ID of the individual that approved the journal entry. Field Number Fleld Name Data Type Length Description Journal 1D Identifier that is unique for each journal.entry, May require concatenation of multiple flelds. Identifler that is unique for each line within a Journal entry. Joumal | ID tine Number JE Header Description JE Line Description - Business Unit Code Posting source {code for source form which the Jjournal entry otiginated, such as sales journal, cash recelpts, journal, genetal Journal, payroll journal, accountant manual entry; spreadsheet, and 5o on). Used to identify the biisiness:unit, region; branch; andsoon; at the level that financlal statements are being audited and for which the trial balance is generated. Effecflve Data The date of the journal entry, no matter what date the entry is recelved or entered. Fiscal yearin which Effective. Date occurs— YYYY for delimited, CCY- MM:-DD fiscal year end (ISO 8601} Fiscal Year {continued)
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