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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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Analytics Automation
ABOUT AUDITNET® LLC
• AuditNet®, the global resource for auditors, serves the global audit
community as the primary resource for Web-based auditing content. As the first online
audit portal, AuditNet® has been at the forefront of websites dedicated to promoting the
use of audit technology.
• Available on the Web, iPad, iPhone, Windows and Android devices and
features:
• Over 3,100 Reusable Templates, Audit Programs, Questionnaires, and
Control Matrices
• Webinars focusing on fraud, data analytics, IT audit, and internal audit
with free CPE for subscribers and site license users.
• Audit guides, manuals, and books on audit basics and using audit
technology
• LinkedIn Networking Groups
• Monthly Newsletters with Expert Guest Columnists
• Surveys on timely topics for internal auditors
Introductions
HOUSEKEEPING
This webinar and its material are the property of AuditNet® and its Webinar partners.
Unauthorized usage or recording of this webinar or any of its material is strictly forbidden.
• If you logged in with another individual’s confirmation email you will not receive CPE as the
confirmation login is linked to a specific individual
• This Webinar is not eligible for viewing in a group setting. You must be logged in with your
unique join link.
• We are recording the webinar and you will be provided access to that recording after the
webinar. Downloading or otherwise duplicating the webinar recording is expressly prohibited.
• You must attend the entire Webinar to receive CPE (no partial CPE will be awarded).
• If you meet the criteria for earning CPE you will receive a link via email to download your
certificate. The official email for CPE will be issued via NoReply@gensend.io and it is important
to white list this address. It is from this email that your CPE credit will be sent. There is a
processing fee to have your CPE credit regenerated post event.
• Submit questions via the chat box on your screen and we will answer them either during or at
the conclusion.
• You must answer the survey questions after the Webinar or before downloading your
certificate.
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Analytics Automation
IMPORTANT INFORMATION
REGARDING CPE!
• WEBINAR ATTENDEES - If you attend the entire Webinar, and meet the eligibility criteria, you will
receive an email with the link to download your CPE certificate. The official email for CPE will be issued
via NoReply@gensend.io and it is important to white list this address. It is from this email that your
CPE credit will be sent. There is a processing fee to have your CPE credit regenerated post event.
• We cannot manually generate a CPE certificate as these are handled by our 3rd party provider. We
highly recommend that you work with your IT department to identify and correct any email delivery
issues prior to attending the Webinar. Issues would include blocks or spam filters in your email system
or a firewall that will redirect or not allow delivery of this email from Gensend.io
• We are not responsible for any connection, audio or other computer related issues. You must have
pop-ups enabled on you computer otherwise you will not be able to answer the polling questions
which occur approximately every 20 minutes. We suggest that if you have any pressing issues to see to
that you do so immediately after a polling question.
The views expressed by the presenters do not necessarily represent the views, positions, or
opinions of AuditNet® LLC. These materials, and the oral presentation accompanying them, are
for educational purposes only and do not constitute accounting or legal advice or create an
accountant-client relationship.
While AuditNet® makes every effort to ensure information is accurate and complete, AuditNet®
makes no representations, guarantees, or warranties as to the accuracy or completeness of the
information provided via this presentation. AuditNet® specifically disclaims all liability for any
claims or damages that may result from the information contained in this presentation,
including any websites maintained by third parties and linked to the AuditNet® website.
Any mention of commercial products is for information only; it does not imply
recommendation or endorsement by AuditNet® LLC
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Analytics Automation
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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Analytics Automation
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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Analytics Automation
OVERVIEW AND DEFINITIONS
AUTOMATING ANALYTICS
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Analytics Automation
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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Analytics Automation
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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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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Analytics Automation
“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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Analytics Automation
The computer simulation of
human thought - such as reasoning
MACHINE LEARNING
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Analytics Automation
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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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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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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Analytics Automation
POLLING QUESTION 2
AUTOMATION OF ANALYTICS
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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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Analytics Automation
BENEFITS TO AUTOMATED ANALYTICS
Historical Real-time Predictive Risk-
focused
Expand
coverage
BUILDING BLOCKS TO AUTOMATION
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Analytics Automation
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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Analytics Automation
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Analytics Automation
Normalization Table
If Match with Value on Left
Change to Value on Right
Building Block
Apps To Explore
Your Data
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Analytics Automation
Results File
Summary of All Words
In Address Field
Reference Table
Use to Find Similarly
Spelled Words
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Analytics Automation
Use LISTFIND with
Reference Table ‘Norm List.txt’
To Find Variations and
Misspellings to Include in
Normalization Table
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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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Analytics Automation
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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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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Analytics Automation
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FUZZY VENDOR NAMES
Vendor Master File
Creating a Sort Normalize field to use for testing
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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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Analytics Automation
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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Analytics Automation
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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Analytics Automation
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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Analytics Automation
SKILLS NEEDED
Project Management
Change Management
Communication
Visual Reporting / Dashboard
Analytics
IT/Data Security
MANAGING EXCEPTIONS
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Analytics Automation
Exception Manager: ExampleWorkflow to Manage the Exceptions.
A user view of a test result in Exception Manager.
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Analytics Automation
User Explaining the Result with in the Notes section.
View of Exceptions after Closed by User.
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Analytics Automation
Creating Categories Based on Review of Note.
WHAT IS ROOT CAUSE ANALYSIS
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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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Analytics Automation
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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Analytics Automation
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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Analytics Automation
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
76
ROOT CAUSE ANALYSIS
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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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Analytics Automation
Comparison of 1st month to 2nd month
Summarizing Categories to findTrends.
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Analytics Automation
Exception Manager: Revised ExampleWorkflow
A revised user view of a test result in Exception Manager.
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Analytics Automation
Thank you!
Brad Thiessen
Director of Product Management
Arbutus Analytics
bthiessen@arbutussoftware.com
Keith Barber
Partner, Data Analytics Insight
Verracy
Kbarber@verracy.com
Questions?
83

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Driving More Value With Automated Analytics

  • 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 1 2
  • 2. Analytics Automation ABOUT AUDITNET® LLC • AuditNet®, the global resource for auditors, serves the global audit community as the primary resource for Web-based auditing content. As the first online audit portal, AuditNet® has been at the forefront of websites dedicated to promoting the use of audit technology. • Available on the Web, iPad, iPhone, Windows and Android devices and features: • Over 3,100 Reusable Templates, Audit Programs, Questionnaires, and Control Matrices • Webinars focusing on fraud, data analytics, IT audit, and internal audit with free CPE for subscribers and site license users. • Audit guides, manuals, and books on audit basics and using audit technology • LinkedIn Networking Groups • Monthly Newsletters with Expert Guest Columnists • Surveys on timely topics for internal auditors Introductions HOUSEKEEPING This webinar and its material are the property of AuditNet® and its Webinar partners. Unauthorized usage or recording of this webinar or any of its material is strictly forbidden. • If you logged in with another individual’s confirmation email you will not receive CPE as the confirmation login is linked to a specific individual • This Webinar is not eligible for viewing in a group setting. You must be logged in with your unique join link. • We are recording the webinar and you will be provided access to that recording after the webinar. Downloading or otherwise duplicating the webinar recording is expressly prohibited. • You must attend the entire Webinar to receive CPE (no partial CPE will be awarded). • If you meet the criteria for earning CPE you will receive a link via email to download your certificate. The official email for CPE will be issued via NoReply@gensend.io and it is important to white list this address. It is from this email that your CPE credit will be sent. There is a processing fee to have your CPE credit regenerated post event. • Submit questions via the chat box on your screen and we will answer them either during or at the conclusion. • You must answer the survey questions after the Webinar or before downloading your certificate. 3 4
  • 3. Analytics Automation IMPORTANT INFORMATION REGARDING CPE! • WEBINAR ATTENDEES - If you attend the entire Webinar, and meet the eligibility criteria, you will receive an email with the link to download your CPE certificate. The official email for CPE will be issued via NoReply@gensend.io and it is important to white list this address. It is from this email that your CPE credit will be sent. There is a processing fee to have your CPE credit regenerated post event. • We cannot manually generate a CPE certificate as these are handled by our 3rd party provider. We highly recommend that you work with your IT department to identify and correct any email delivery issues prior to attending the Webinar. Issues would include blocks or spam filters in your email system or a firewall that will redirect or not allow delivery of this email from Gensend.io • We are not responsible for any connection, audio or other computer related issues. You must have pop-ups enabled on you computer otherwise you will not be able to answer the polling questions which occur approximately every 20 minutes. We suggest that if you have any pressing issues to see to that you do so immediately after a polling question. The views expressed by the presenters do not necessarily represent the views, positions, or opinions of AuditNet® LLC. These materials, and the oral presentation accompanying them, are for educational purposes only and do not constitute accounting or legal advice or create an accountant-client relationship. While AuditNet® makes every effort to ensure information is accurate and complete, AuditNet® makes no representations, guarantees, or warranties as to the accuracy or completeness of the information provided via this presentation. AuditNet® specifically disclaims all liability for any claims or damages that may result from the information contained in this presentation, including any websites maintained by third parties and linked to the AuditNet® website. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by AuditNet® LLC 5 6
  • 4. Analytics Automation 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. 7 8
  • 5. Analytics Automation 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 9 10
  • 6. Analytics Automation OVERVIEW AND DEFINITIONS AUTOMATING ANALYTICS 11 12
  • 7. Analytics Automation 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 13 14
  • 8. Analytics Automation 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 15 16
  • 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 17 18
  • 10. Analytics Automation “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) 19 20
  • 11. Analytics Automation The computer simulation of human thought - such as reasoning MACHINE LEARNING 21 22
  • 12. Analytics Automation 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 23 24
  • 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 25 26
  • 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 27 28
  • 15. Analytics Automation POLLING QUESTION 2 AUTOMATION OF ANALYTICS 29 30
  • 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 31 32
  • 17. Analytics Automation BENEFITS TO AUTOMATED ANALYTICS Historical Real-time Predictive Risk- focused Expand coverage BUILDING BLOCKS TO AUTOMATION 33 34
  • 18. Analytics Automation 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 35 36
  • 20. Analytics Automation Normalization Table If Match with Value on Left Change to Value on Right Building Block Apps To Explore Your Data 39 40
  • 21. Analytics Automation Results File Summary of All Words In Address Field Reference Table Use to Find Similarly Spelled Words 41 42
  • 22. Analytics Automation Use LISTFIND with Reference Table ‘Norm List.txt’ To Find Variations and Misspellings to Include in Normalization Table 43 44
  • 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 45 46
  • 24. Analytics Automation 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 47 48
  • 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 49 50
  • 26. Analytics Automation 51 FUZZY VENDOR NAMES Vendor Master File Creating a Sort Normalize field to use for testing 51 52
  • 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. 53 54
  • 28. Analytics Automation MANAGING EXCEPTIONS COMPONENTS OF EXCEPTION MANAGEMENT Workflow Establishing roles – Individuals / Groups Notification System Integration with Analytics Rules/ Boundaries Monitor & Reporting Survey 55 56
  • 29. Analytics Automation BARRIERS TO CREATING EXCEPTION MANAGEMENT • Change Management • Poor Training • Improperly defined processes • Lack of proper/formal oversight • Insufficient EM system flexibility Deployment / Implementation Challenges 57 58
  • 30. Analytics Automation 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 59 60
  • 31. Analytics Automation SKILLS NEEDED Project Management Change Management Communication Visual Reporting / Dashboard Analytics IT/Data Security MANAGING EXCEPTIONS 61 62
  • 32. Analytics Automation Exception Manager: ExampleWorkflow to Manage the Exceptions. A user view of a test result in Exception Manager. 63 64
  • 33. Analytics Automation User Explaining the Result with in the Notes section. View of Exceptions after Closed by User. 65 66
  • 34. Analytics Automation Creating Categories Based on Review of Note. WHAT IS ROOT CAUSE ANALYSIS 67 68
  • 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 69 70
  • 36. Analytics Automation 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? 71 72
  • 37. Analytics Automation 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 73 74
  • 38. Analytics Automation 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 76 ROOT CAUSE ANALYSIS 75 76
  • 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. 77 78
  • 40. Analytics Automation Comparison of 1st month to 2nd month Summarizing Categories to findTrends. 79 80
  • 41. Analytics Automation Exception Manager: Revised ExampleWorkflow A revised user view of a test result in Exception Manager. 81 82
  • 42. Analytics Automation Thank you! Brad Thiessen Director of Product Management Arbutus Analytics bthiessen@arbutussoftware.com Keith Barber Partner, Data Analytics Insight Verracy Kbarber@verracy.com Questions? 83