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ARRANGING AND ORGANIZING DATA

The "Payment Tender Net" ifile previously displayed shows all the tender amounts listed in a single field or column. In order to do additional analysis steps, we need to have each of the different tender payment amounts in their own separate columns. All tender types are extracted into separate files and then joined back using the TXNID field to achieve this. Analytical software can easily rearrange and organize data into more useful layouts as shown in Figure 3.15 by using Direct Extraction to extract the

Direct File Extraction of Each Payment Tender Type

FIGURE 3.15 Direct File Extraction of Each Payment Tender Type

relevant fields and then using Visual Connector in Figure 3.16 to join them back to the desired layout as in Figure 3.17. This type of data layout is not uncommon and is highlighted in the example.

Data can further be enriched and transformed optimally for analysis, if you join information from other data files. This is especially so when data from separate systems is combined. A simple example is combining a dentist's electronic appointment scheduling software with the accounting software. Once this is done, it would be easy to determine if there are any errors in the recording of fees by filtering for appointments where there were no amounts paid or included in accounts receivable.

Compare the results displayed in Figure 3.17, which has individual tender columns, to the original layout in Figure 3.4, which has all the tender amounts in a single column.

Using IDEA'S Visual Connector to Join the Payment Tender Type Files

FIGURE 3.16 Using IDEA'S Visual Connector to Join the Payment Tender Type Files

Resulting Tender Master File with Each Tender Type in Individual Columns

FIGURE 3.17 Resulting Tender Master File with Each Tender Type in Individual Columns

CONCLUSION

After you have successfully completed the steps of obtaining the data in a format that you can use, ensured the data integrity, normalized the data, and prepared the data for analysis, you can apply various analytical tests. The general analytical tests are outlined in Chapter 5 and advanced tests in Chapter 6 . Applicable tests to specific audit areas follow those two chapters. However, before we proceed to tests for anomalies, it is better to have a basic understanding and a review of statistics, which we discuss in Chapter 4 .

NOTES

1. American Institute of Certified Public Accountants, "Consideration of Fraud in a Financial Statement Audit," aicpa.org/Research/Standards/AuditAttest/Downloadable Documents/AU-00316.pdf.

2. SAP, "SAP Business Solutions: Software, Applications & Data Management," accessed November 24, 2013, global.sap.com/campaign/na/usa/CRM-XH12-PPC-PPC-BRAND/index.html?SOURCEID=DE&campaigncode=CRM-XH13-PPC-CROBRND2DE& mid=s4G9sJJ8t%7cdc_2722p1v19626_27672116097_sap%2520ag_e&kwid=4G9sJJ8t.

3. CaseWare IDEA Inc., "A Practical Guide to Obtaining Data for Auditors," August 2013.

 
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