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Data Analytics Elevating Internal Audit’s
Value
Matt Petrich Grant Thornton
Mark Salamasick University of Texas System
Why Dallas IIA Chapter Is Such a
Great Chapter?
• Gives Many Opportunities to Members• Shares with Others• Gives Time and Talent• Supports University Programs• Supports the Next Generation of Internal
Auditors • Chapter Research• Donations of Significant Magnitude –
Internal Audit Foundation(IAF)
www.dallasiia.org
st Annual Dallas IIA SuperConference
October 29, 2012Creating Value Through Assurance. Insight. Objectivity.
October 29, 2012Hilton Anatole
Dallas IIA
Internal Audit Foundation 2012 – Lessons Learned on the Audit Trail (Spring 2014)
2013 - Data Analytics book - GT (Spring 2016)
2014 - Internal Auditing Textbook 4th ed. (Spring 2017)
2015 - Trusted Advisors (Spring 2017)
2016 - Cybersecurity Book (Spring 2018)
2017 -Internal Audit Consulting Book (Summer 2018)
Lessons Learned on the Audit Trail
by Richard Chambers
Data Analytics Book – Grant Thornton
Internal Auditing Textbook, 4th Edition
Trusted Advisors: Key Attributes of Outstanding
Internal Auditors by Richard Chambers
Maximizing Valuefor Internal AuditUtilizing Data Analytics
and DataVisualization
Agenda
• Data Analytics Book
• Data Analytics Maturity Model Framework
• Vision
• The Future is Now
• Next Steps
Polling Question 1
How many dedicatedresources do you have to
perform data analyticswithin the audit group?
GAM Polling Question 1 Results
How many dedicated resources do you have to perform data analytics within the audit group?
43%
24%
23%
3%
1%
7%
Dedicated Resources
Performing Data Analytics
0
1
2 to 5
5 to 10
Greater than 10
We don't have any data
analytics in our audit group
Internal Audit Foundation
Data Analytics Book
Chapter-by-chapter description
• Chapter 1: What Does Data
Analytics Mean to Internal Audit?
• Chapter 2: The Data Analytics Framework
• Chapter 3: Develop a Vision
• Chapter 4: Evaluate Current Capabilities
Chapter-by-chapter description
• Chapter 5: Enhance People,
Process and Technology
• Chapter 6: Implement, Monitor, Evolve
• Chapter 7: The Future of Data Analytics in Internal Auditing
Internal Audit Foundation
Data Analytics Book
CAE Interviews (partial-Dallas)
Internal Audit Foundation
Data Analytics Book
Key takeaways from research
1. Most IA shops are in the infancy stage of DA initiatives.
2. Accessing and understanding data is the first stepto a successful DA initiative.
3. CAEs want visualization and predictive analyticSolutions.
4. Developing in-house staff around DA is a significant challenge.
5. Momentum around DA is gained through financialresults (i.e., how much did this save me?)
Internal Audit Foundation
Data Analytics Book
2016 All Star Conference
Data analytics framework:
Understanding how data analytics
will elevate internal audit
Data Analytics Maturity Model
Framework
Strategic evaluation allows for developmentinto the "optimized" maturity level
Assess capabilities in:
• People
• Process
• Technology
Data Analytics Maturity Model
Framework
Five phases of data analytics maturity:
• Ad hoc
• Defined
• Repeatable
• Institutionalized
• Optimized
People MaturityAd Hoc Defined Repeatable Institution
-alized
Optimized
Dedicated IA
function with
limited data
analytics skillset
Capability to
“borrow” data
analytics
expertise from
other
departments
Dedicated data
analytics staff in IA
with advanced
capabilities (e.g.,
CAATs)
Dedicated data
science within IA
Dedicated data
scientist within IA
and significant
number of
internal auditors
with data
analytics skills
Use cases
understood and
prioritized by
staff
Established
success metrics
around desired
skills
Developed
strategy for
additional
capabilities
Risk coverage
profile and other
constraints
captured and
used to optimize
scheduling
Data governance
framework
established and
understood by
staff
Continual training
requirements
specific to data
analytics
Road map for
implementation
across
enterprise
Compensation
connected to data
analytics skillset
Process MaturityAd Hoc Defined Repeatable Institution
-alized
Optimized
Small sample
size
Large sample
sizes
Significant sample
sizes
Significant or all
data audited
Real-time data
monitoring with
alerts
Inconsistent
reporting
Process does
not leverage
prior audits
Consistent
reporting
Standard reporting
Process applies a
standardized
approach
Continuous
auditing
throughout the
IA function
Continuous
monitoring
throughout
business function
Heavy reliance
on IT to obtain
data
Established data
access protocol
with IT Process
leverages
historical less
learned
Data verification
and accuracy
protocol
established
Reporting
shared across
stakeholders
Real-time
reporting
accessed through
self-service
business
intelligence
Technology Maturity
Ad Hoc Defined Repeatable Institution
-alized
Optimized
Spreadsheets Other reporting
and relationship
databases
Data access on
demand
Access to central
enterprise data
store
Automated data
extraction,
transfer, and load
(ETL)
Data visualization
tools (limited
basis)
Data interrogation
scripts are defined
Automated
scripting and
testing
Advanced
analytics available
for use within
function
Workflow and
data capture
technology
Data
visualization
tools integrated
for data input,
analytics, and
reporting
System
information
management
software (SM)
Data visualization
tool for reporting
Polling Question 2
Which phase of theanalytics maturity model do you believe your audit
group is in?
GAM Polling Question 2 Results
Which phase of the analyticsmaturity model do you believe
your audit group is in?
Polling Question 3
Which phase of theanalytics maturity model would you like your audit group to be in 3 years?
GAM Polling Question 3 Results
Which phase of the analytics maturity model do you believe your audit group is in?
4%
8%
48%
26%
13%1%
Analytics Maturity Model
Ad hoc
Defined
Repeatable
Institutionalized
Optimized
Not sure
What is Data Analytics and Data
Visualization
Key Definitions
Data Analytics and Risk
Management
Board-directed data-driven risk decisions
The Perfect Storm
Explosive growth in raw data, technological
advances in data storing and analysis, looking for
data-driven decision making with a board-directed
focus on credit risk, anti-money laundering and
high-risk entity analysis
Data Analytics and Risk
Management
What the future looks like
1. The board looking for data-driven decisions
on risk
2. The C-suite looking for key risk analytics and
their relevance to the organization
3. The ability to “foresee” future risks before
manifestation
Data Analytics and Risk
Management
How can data analytics be applied to the internal audit function
• Historical Perspective – Error detection and
quantification
• Continuous Review – Continuous monitoring
and continuous auditing
• Future Perspective – Key Risk Indicators along
with predictive and prescriptive analytics
Data analytics frameworkImplementing data analytics into internal audit is no longer a question of when but how.
Vision
The
Future is Now
Actual
Examples
Polling Question 4
What is the most significantchallenge to incorporating
data analytics into the audit process?
GAM Polling Question 4 Results
What is the most significantchallenge to incorporating dataanalytics into the audit process?
Polling Question 5
Do you believe there will be agreater emphasis on data
analytics in your organization inthe next 3 to 5 years?
GAM Polling Question 5 Results
Do you believe there will be a greater emphasis on data analytics in your organization in the next 3 to 5 years?
What can you all do!
Questions?
Thank You
Contact information
Matt Petrich
Forensic AdvisoryServices
312-602-8648
Mark Salamasick
Executive Director Audit Academic
512-499-4535
