Practical Data Analytics with ACL - ISACA Data Analytics with ACL FRANK OBERMEYER JOINT IIA AND...

Practical Data Analytics with ACL FRANK OBERMEYER JOINT I IA AND ISACA EVENT, CINCINNATI 10/9/2012

Setting the Stage

Learning From Past Mistakes Barriers to overcome

Traps to avoid

Overcoming Barriers to Effective Data Analytics

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.

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.

Lack of ACL “Talent” Achieve critical mass.

Use the help!

ACL Help Desk – patient and technical

Other Barriers?

Avoiding Traps When Working with ACL

Failure to Adequately Ensure Security & Privacy Avoid even obtaining sensitive data where possible.

Use a secure folder.

Delete old files timely.

Poor Quality Data Beware of unofficial data sources.

Test for data quality, accuracy, and completeness early in the process.

Don’t assume data definitions.

Procedures That Can’t Be Reused Document your work.

Clean up/delete old files.

Use naming conventions.

Use scripts.

Untimely Reporting Understand important deadlines and plan to meet them.

Make data requests as early as possible.

Anticipate the need for slack time.

Other Traps to Avoid?

Case Study: Company Card Data Analytics Structured, high quality, authoritative, and accessible data from an external source.

Common issues across industries.

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

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

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)

Live Practice with ACL

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?

Random Selection with Excel

Questions? Frank Obermeyer, I.A. Officer

Ting Zhang, Senior Auditor

Tom Buschelmann, Corp. Acct. Mgr