While the use of Data Analytics produces excellent results, they’re commonly applied in a tactical way for specific functional areas within an organization. This tactical approach often falls short of realizing the full potential of Data Analytics. Going beyond initial results, a more systematic approach to Data Analytics can help drive organizational learning (human and machine) from the various remediation processes.
In this Webinar, we’ll discuss 3 areas of Analytics Automation: (1) Producing the findings, (2) Managing the findings, and (3) Learning from the findings.
Key takeaways:
· The value of Analytics Automation
· Understanding the various technologies (i.e. RPA, AI, etc.)
· Practical ideas for deploying and managing Analytics Automation
· Using a more structured approach to remediation exceptions
· Benefits of Root Cause Analysis
· Using Analytics Automation to get a broader, more complete view of your organization over time
1. Analytics Automation
AuditNet Webinar: Driving More Value with Automated Analytics!
Key Presenters:
• Keith Barber, Partner Data Analytics Insight, Verracy
• Brad Thiessen, Director of Product Management, Arbutus Analytics
About Jim Kaplan, CIA, CFE
President and Founder of AuditNet®,
the global resource for auditors
(available on iOS, Android and
Windows devices)
Auditor, Web Site Guru,
Internet for Auditors Pioneer
IIA Bradford Cadmus Memorial Award
Recipient
Local Government Auditor’s Lifetime
Award
Author of “The Auditor’s Guide to
Internet Resources” 2nd Edition
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ABOUT AUDITNET® LLC
• AuditNet®, the global resource for auditors, serves the global audit
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Introductions
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IMPORTANT INFORMATION
REGARDING CPE!
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Brad Thiessen
Director of Product Management
ArbutusAnalytics
With over 25 years of experience in Data Analytics.
Brad has been with Arbutus since 2007 and has worked with 100’s of organizations to help
minimize the user adoption gap and deliver more value using audit and fraud detection
analytics.
He continues to share his expertise in data analytics helping companies achieve confidence and
success in the use of analytics.
Keith Barber
Partner, Data Analytics Insight
VERRACY
Keith is a Partner with Verracy responsible for Data Analytics Insight, where he specializes in data
analytics training, data analytics program implementations, and advancing data analytics
programs.
Keith has extensive experience helping clients solve complex data issues using various software
tools. His professional expertise includes data analytics, IT audit, fraud detection, automated
control monitoring, the use of Business Intelligence to support strategic decision making, and
personnel/project management. During his tenure with the Big 4, Keith delivered a number of
audit data analytics projects in support of financial audit, tax engagements, business risk and IT
engagements.
Over the course of his career, Keith’s technical expertise and seasoned judgment were key to
satisfying client needs, delivering the right level of customization to ensure that clients received
the highest value for their investment. Using data analytics Keith identified and quantified
evidence of a $5 million fraud. The evidence provided the board and management with
concrete evidence of the fraud and facilitated recovery of the funds.
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POLLING QUESTION 1
AGENDA
Overview and
Definitions
Automation of
Analytics: Gaining
Insights – The
Findings
Managing
Exceptions:
Remediate the
Findings
Root Cause
Analysis: Learn
from the Findings
Q & A
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AUTOMATION
- Automatically controlled operation of an apparatus, process, or system by mechanical or
electronic devices that take the place of human labor (allow it to be better used
elsewhere)
- The technique of making an apparatus, a process, or a system operate automatically
~Merriam-Webster Dictionary
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WHY WE ARE HERE TODAY
1. You are not going to be “replaced” by automation
2. “Human labor” will take exciting, achievable steps forward in analytics
3. Business Intelligent will facilitate automation
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9. Analytics Automation
BOT-Process executed on 1,800+ daily log files.
Opening the
vendor
application
Load a single log
file
Click on a button
to decrypt
Open notepad
Save decrypted
file
Close notepadClose log fileLoad next log file
BOT Process- A Short Video
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“There's a lot of automation that can happen that isn't a replacement of humans
but of mind-numbing behavior.” – Stewart Butterfield Founder of Slack
ARTIFICIAL INTELLIGENCE (AI)
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Subset of AI focused on the scientific study of algorithms and statistical models
used to extract complex relationships in data, without explicit instructions, and
presumably, those which a human would not find.
DEEP MACHINE LEARNING
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13. Analytics Automation
Class of machine learning algorithms focused specifically on building “deep”
(multi-layered) neural networks, a form of AI widely used in fraud detection.
ROBOTIC PROCESS AUTOMATION
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14. Analytics Automation
Emerging form of business process automation based on the notion of
metaphorical software robots or artificial intelligence (AI) workers
RPA
Automation
Anywhere
Automation
Anywhere
UiPathUiPath
Blue PrismBlue Prism
General
Analytics
Alteryx
Server
Alteryx
Server
Trifacta
Enterprise
Trifacta
Enterprise
Audit
Analytics
Arbutus
Server
Arbutus
Server
Galvanize
AX
Galvanize
AX
IDEAIDEA
Exception
Management
Arbutus AMArbutus AM
Caseware
Monitor
Caseware
Monitor
HighBondHighBond
Query
Languages
SQLSQL
RR
PythonPython
AI / ML
MindBridgeMindBridge
OSP LabsOSP Labs
ANALYTICS AUTOMATION - TECHNOLOGY LANDSCAPE
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16. Analytics Automation
SKILLS AND EXPERIENCE NEEDED
Project
Management
Business
Acumen
Audit/Fraud
Expertise
Data Acquisition
Data Analytics /
Science
Visual Reporting
Techniques
Communication
AUTOMATED ANALYTICS – KEY STEPS
Link DA goals and
Business Objectives
Know the Data
Leverage Exiting
Insights
Test and learn Make it Actionable Exceptions
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BENEFITS TO AUTOMATED ANALYTICS
Historical Real-time Predictive Risk-
focused
Expand
coverage
BUILDING BLOCKS TO AUTOMATION
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Pre-built Apps/Scripts
Connectors to Data Sources
• Visual Scripting
• Auto Create Apps from Results
• Specialized Commands
• Visual Scripting
• Auto Create Apps from Results
• Specialized Commands
Development Helpers
Are these the same addresses?
Addr1: 2847 Congress Pkwy West
Addr2: Suite 201
Addr1: #201, 2847W Congress Parkway
Addr2:
Addr1: 125 Fifth Str. E Addr1: 125 East 5th Street
Addr1: 707 Rooke Road Addr1: 707 Rook Rd
Addr1: 3960 Monjah Circle Addr1: 3960 Monja Circle
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22. Analytics Automation
Use LISTFIND with
Reference Table ‘Norm List.txt’
To Find Variations and
Misspellings to Include in
Normalization Table
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23. Analytics Automation
BARRIERS TO AUTOMATED ANALYTICS
MANAGEMENT
• Resources
• Planning
• Resources
• Planning
TRAINING
• Learning curve
• No budget
• Confidence
• Learning curve
• No budget
• Confidence
ROI
• Team effort
• Career Path
• Documentation
• Team effort
• Career Path
• Documentation
BARRIERS TO AUTOMATED ANALYTICS
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FACTORS THAT DERAIL AUTOMATED ANALYTICS
Documentation
Internal / external
Personnel
Turnover
Technology
Change management
X
and
and
Same
Same-Same-Same
Same-Same-Different
Same-Same-Similar
Same-Same-Near
FUZZY LOGIC
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25. Analytics Automation
DAMERAU-LEVENSHTEIN is FUZZY LOGIC
Logic that compares approximate values as opposed to exact
String String Distance
123 Main Street 123 Main St 4
34567 34566 1
Rob Robert 3
Gary Mary 1
POLLING QUESTION 3
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27. Analytics Automation
Testing the Sort Normalize field using
Duplicates and Fuzzy matching (Similar (0).
Results of the DuplicateTest.
Each result will be sent to Exception Manager for tracking.
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MANAGING EXCEPTIONS
COMPONENTS OF EXCEPTION MANAGEMENT
Workflow
Establishing
roles –
Individuals /
Groups
Notification
System
Integration
with
Analytics
Rules/
Boundaries
Monitor &
Reporting
Survey
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BARRIERS TO CREATING EXCEPTION MANAGEMENT
• Change Management
• Poor Training
• Improperly defined processes
• Lack of proper/formal
oversight
• Insufficient EM system
flexibility
Deployment /
Implementation
Challenges
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USER ADOPTION & SUSTAINABILITY CHALLENGES
Management Lack of Training Workload
Transparency Usability/Reliability
BENEFITS OF AUTOMATING EXCEPTION MANAGEMENT
Reduction in risk exposure
Removal/reduction of the dependence to 'spot' exceptions
Best practices
Automation/Workflow of activities that exceptions cause
Improvement in employee and company performance
Consistent, detailed information supporting exceptions & decisions
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35. Analytics Automation
Root cause analysis is a series of tools and methodologies that work in concert
with each other, rather than a single approach.
Using these tools, users are able to identify the underlining cause of an issue and
take steps prevent reoccurrence.
POLLING QUESTION 4
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ROOT CAUSE TECHNIQUES
5 Why’s Kipling - 5W1H Ishikawa -
Fishbone Analysis
Failure Mode and
Effect Analysis
SIPOC
Statistical
Correlation
Lean Six Sigma
Waste Walks
RACI Matrices Spaghetti
Diagrams
ROOT CAUSE STEPS
1. Define the Problem
2. Collect Data and Analyze
3. Determine & Implement
Solutions
4. Follow-up and Monitor
What’s the
Problem
Why did it
happen?
What can be
done?
Was it
effective?
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HOW DO WE KNOW WE FOUND ROOT CAUSE?
General
agreement
Cause(s) are
logical & provide
insight
Cause identified
can be changed
and controlled
Prevents
reoccurrence
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Root Cause Analysis and Exception Management
Provides a data repository of exceptions for analysis
Gives view of exceptions over time
Provides a new way and new place to look for answers
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ROOT CAUSE ANALYSIS
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39. Analytics Automation
Next Run the Analytic is Updated to Include Employee ID
View of exceptions after closed by user, not the Employee ID is captured.
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