Practical Data Analytics with ACL - ISACA Data Analytics with ACL FRANK OBERMEYER JOINT IIA AND...
Transcript of Practical Data Analytics with ACL - ISACA Data Analytics with ACL FRANK OBERMEYER JOINT IIA AND...
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Practical Data Analytics with ACL FRANK OBERMEYER JOINT I IA AND ISACA EVENT, CINCINNATI 10/9/2012
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Setting the Stage
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Learning From Past Mistakes Barriers to overcome
Traps to avoid
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Overcoming Barriers to Effective Data Analytics
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Unstructured or Inaccessible Data Start with data that’s readily available in tabular format.
Avoid data that my change structure from one iteration to the next.
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Lack of Data Owner Buy In Look for and communicate mutual benefit.
Involve them in the process. They know their data better than you do.
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Lack of ACL “Talent” Achieve critical mass.
Use the help!
ACL Help Desk – patient and technical
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Other Barriers?
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Avoiding Traps When Working with ACL
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Failure to Adequately Ensure Security & Privacy Avoid even obtaining sensitive data where possible.
Use a secure folder.
Delete old files timely.
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Poor Quality Data Beware of unofficial data sources.
Test for data quality, accuracy, and completeness early in the process.
Don’t assume data definitions.
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Procedures That Can’t Be Reused Document your work.
Clean up/delete old files.
Use naming conventions.
Use scripts.
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Untimely Reporting Understand important deadlines and plan to meet them.
Make data requests as early as possible.
Anticipate the need for slack time.
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Other Traps to Avoid?
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Case Study: Company Card Data Analytics Structured, high quality, authoritative, and accessible data from an external source.
Common issues across industries.
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Company Cards: The Process
• Accounting runs bank-supplied query at month end
Acquire Data
• Profiling
• Data quality testing
• Targeted analytics
• Test for duplicates
Run Analytics • Excel-based report
with summary and details
• Top apparent issues highlighted
Report Results
• Dig deeper upon request
• Refine analysis for next time
Follow Up
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Company Cards: Data Elements
• Transaction Date
• Transaction Amount
• Merchant Name
• Merchant Category Code (& Description)
• Merchant Category Group (& Description)
• Taxpayer ID
• Credit Limit (supplied separately)
• Cardholder Name
• Card Account Number (last 4 digits)
• Transaction Reference Number
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Company Cards: Summary of Analytics
• Transaction count and total amount across transaction amount strata
• Transaction amount and count by monthly period
• Smallest and largest transactions
• Missing tax ID’s or credit limits
• Amount and count by Merchant Category Group
• Top 10 merchants by frequency and amount
• Unusual merchant categories
• Top cardholders by frequency and amount
• Expenses that appear to conflict with company policy
• Potential duplicate payments (daily and monthly views)
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Live Practice with ACL
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What do you want to see?
• Import a cleansed credit card transaction file?
• Perform a random sample of 25 records?
• Export a sample to Excel?
• Save the above activities to a script?
• Edit a script to accept an input variable (ACCEPT) and display a dialogue box upon completion (PAUSE)?
• Attach a script to the ACL menu bar?
• Export an ACL log?
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Random Selection with Excel
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Questions? Frank Obermeyer, I.A. Officer
Ting Zhang, Senior Auditor
Tom Buschelmann, Corp. Acct. Mgr