Past Presentation at IIA GAM Aaron Boor, IT Audit & Project Automation Manager talks about how he uses technology and data analytics to deliver more value to his organization. SLIDESHARE: www.slideshare.net/CaseWare_Analytics WEBSITE: www.casewareanalytics.com BLOG: www.casewareanalytics.com/blog TWITTER: www.twitter.com/CW_Analytic

1. HOW TO FIND NEW WAYS TO ADD VALUE
TO YOUR AUDITS
IIA GAM CONFERENCE

2. PRESENTER
Aaron Boor, CISA
IT Audit & Project Automation Manager
Internal Audit Department
Donegal Insurance Group

3. CORPORATE CHALLENGES
• Use of technology for more processes is causing more data
to be created
• Data is compiled and available but not documented or
communicated
• Minimal training on how to interpret data
• Proper tools are unavailable to analyze data
• Data analysis skills are lacking in individuals with
institutional knowledge

4. “The Internal Audit function must embrace analytics to keep
pace with or outpace the business; it must become a natural
part of the thought process. This will involve not only the
adoption of new tools and techniques but also a
change in mindset.”
- Ernst & Young

5. TERMINOLOGY
• Data Analysis
• Data Analytics
• Business Analytics
• Business Intelligence
• Big Data Analytics

6. “Data analytics is the application of statistical models and
techniques to business information to derive conclusions that
are beneficial to that business.”
- ISACA

7. OPPORTUNITIES
• Traditionally, auditors attempt to understand populations
and build a representative sample that can be extrapolated -
with analytics IA can now examine entire populations
• IA can identify and focus on attributes that previously were
out of reach, and discern relationships and correlations that
were never before visible
Source: Ernst & Young

8. OPPORTUNITIES
• Internal Audit has a unique perspective into how
data is compiled for financial reporting purposes
• Process walkthroughs
• Control identification
• Understanding of data generation
• Understanding of dataflow between systems
• Data security measures
• Financial report testing

9. “… innovation in audit is essential… This is about what
business and investors really value in audit and,
in the light of the opportunities data analytics presents,
how that might be achieved.”
- ICAEW

10. SKILLS TO CAPTURE OPPORTUNITIES
• The Harvard Business Review cites “Data Scientist” (a
specialized role with a hybridized blend of technical and
statistical skills) as the Sexiest Job of the 21st Century
• Increased knowledge of data analytics technology and how
it interacts with financial data
• Bridge between Information Technology and Accounting
departments utilizing a wealth of company and industry
specific knowledge
• Ability to communicate data analysis results to management
effectively utilizing visualizations

11. “These already-scarce and in-demand skills are likely to remain
challenging to acquire for the future. A survey from The Data
Warehousing Institute (TDWI) cites ‘inadequate staffing or
skills for big data analytics’ as the current top barrier for
implementation of big data analytics in enterprises.”
- ISACA

12. ANALYTICS AND AUDIT QUALITY
• Audit quality increases by having the ability to:
• Analyze full datasets
• Identify outliers in a population for which to sample
• Find the needle(s) in a haystack
• Visualize datasets
• Ask better questions regarding financial data
• Conduct more effective follow-up interactions with
Management

13. GOALS OF ANALYTICS
• Bolster audit quality through increased company specific
knowledge derived from a more detailed understanding of
financial data
• Provide more precise reports to management specifying
root causes to exceptions
• Conduct effective interactions with management more
frequently
• Stronger relationship with Management generating
additional buy-in on future analyses

14. “ … efficiency isn’t about “cutting hours”, it’s about getting to
the things that matter quicker and spending more time on
them instead of ploughing slowly through random samples
that often tell you very little. These techniques shrink the
population at risk. It means we’re fishing in a smaller pond
and we can often go straight to the high risk areas.”
- ICAEW

15. QUESTIONS FOR AUDIT SCOPE
• What is the scope of the data analysis project?
• What are the audit objectives?
• Is data available to be analyzed?
• How much data will be analyzed?
• Is the data organized/documented?
• Where is the data located?
• What authorizations are required to access the data?
• What data types are available from these systems?
• To not hinder production, when can the data be
imported?

16. QUESTIONS ABOUT ANALYSIS TOOLS
• What data analysis tools are available to handle the
scope of this project?
• Data integrity is maintained
• Record/Size limitations
• Does the data need to be “polished” for further use?
• Speed of data availability for use in the tool
• Does the project require data from multiple systems?
• Speed of data calculations (automatic statistics)
• Visualization capabilities

17. DATA ACQUISITION
• Added value becomes possible in the data acquisition stage
• In the planning stage it is determined what data is needed, where it
comes from, how to access it, how it will be analyzed, and what the
expected outcome of the analysis will be.
• Do not exclude fields from data requests. These additional fields
provide valuable insights for further analysis
• Look into datasets acquired in other areas of the audit, they have
the potential to provide valuable insights as well
• Take the time to understand the data obtained across all audit areas
and find commonalities between them for which to provide
additional insights

18. STEPS FOR VALIDATING DATA
• Ensure data to be analyzed was fully received
without errors
• Complete data acquired (record count and control totals
of key amount fields)
• Empty data where there should be data
• No data import errors
• Ensure original production environment was not
harmed during the import process

19. DATA ANALYTICS PROCESS
• Using process documentation (walkthroughs, flowcharts,
contracts, etc.), organize parameters by which data will be
analyzed
• Mirror these parameters using the selected data analysis
tool. (Could be one step, could be hundreds)
• Continue to look for added value opportunities in your
dataset(s)
• If considered a repeatable task, look into scripting
• If scripted, look into scheduling for continuous monitoring
capabilities

20. Most people stop looking when
they find the proverbial needle in
the haystack. I would continue
looking to see if there were
other needles.

21. DATA ANALYSIS PITFALLS & ACTIONS
• Data analysis pitfalls
• False positives
• Flawed analysis logic
• Out-of-date software
• One-offs!
• Human judgement and follow-up regarding results
is a must!
• Practice makes perfect – get the proper training

22. GET MANAGEMENT’S ATTENTION
• Provide powerful insights gained by analyzing the detail behind the
summary information management is used to analyzing
• Utilize visuals like graphs and dashboards to make the statistics standout

23. MORE PRESENTATION TIPS
• Add drill down capabilities – Show that detail!
• Utilize a tool that inherently delivers insights

24. CONCLUSION
“IA must integrate analytics into its audit process to keep pace
not only with the business, but also with the organization’s
competitors. Analytics, properly developed, can help IA provide
business insights and act as a strategic advisor while holding the
line on costs or even reducing them. When it comes to big data
and analytics, the future for internal audit is now.”
-Ernst & Young

25. RESOURCES USED
Ernst & Young Financial Executives
Research Foundation
(FERF)
Institute of Chartered
Accountants in England
& Wales (ICAEW)
Information Systems Audit &
Control Association (ISACA)

26. HOW TO FIND NEW WAYS TO ADD
VALUE TO YOUR AUDITS
IIA CONFERENCE PRESENTATION
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