Presentation by Amitabh Srivastav at the ARMA Winnipeg event in January 2020.

1. Artificial Intelligence and Auto-
Classification: Are They a Silver
Bullet for Records Management
and Compliance?
Amitabh Srivastav, VP, Operations & Governance
ARMA Winnipeg
January 29, 2020

2. Agenda – Part 1
2January 29, 2020
Introduction
Terms and Definitions
Digital Transformation Journey
Unstructured Information
1
4
2
3

3. Agenda – Part 2
3
Information Chaos
What is Artificial Intelligence?
THEMIS CS for Auto-classification
Key Takeaways
5
8
6
7
January 29, 2020

4. “Difficulties are just things to overcome, after all”
– Sir Ernest Henry Shackleton –
Part 1
4January 29, 2020

5. 1 Introduction
Part 1
5January 29, 2020

6. Profile
Amitabh Srivastav
IGP, CIP, PMP
Since 2001 worked with ECM technologies and implemented enterprise-
wide programs and projects worth several millions of dollars to clients in the
public and private sectors
Extensive IG / IM experience with a strong portfolio of qualifications in
strategy, transformation, and risk management
Combine strategic IG / IM thinking, risk management techniques, and
practical implementation experience
Provide CxO / VP-level consulting advice on current technology solutions,
industry trends, and best practices with a focus on digital transformation,
change management, records threat management, and compliance
6January 29, 2020

7. HELUX Highlights
7
• A Microsoft Preferred Partner in
Content Services specializing in
SharePoint, O365, and Cloud
technologies
• Using AI and machine learning, our
THEMIS products re-imagine the
way we do information
management and digital
transformation

8. Sample HELUX Clients
8January 29, 2020

9. 2 Terms and Definitions
Part 1
9January 29, 2020

10. Key concepts
10
Information
Governance
Compliance Security
Risk
Management
Data
Management
Digital Rights
Management
January 29, 2020

11. Key concepts (cont.)
11
Information
Governance
Digital Asset
Management
Content
Services
Information
Management
Data
Analytics
??
…
January 29, 2020

12. Key concepts (cont.)
12
Information
Management
Taxonomy Metadata
Information
Architecture
Knowledge
Management
ECM / EDRMS
User
Experience
User
Interface
File
Plan
Retention /
Disposition
Security
Model
Archiving
January 29, 2020

13. Content Services (CS) is ECM+
13
Document
Management
Records
Management XaaSECM+
Content
Services
CaaS,
MCaaS,
DaaS, BaaS?
January 29, 2020

14. Content Services and Microsoft’s Modern
Approach to ECM+ …
14
Content
Services
Records
Management
Document
Management
Information
Architecture
Artificial
Intelligence
Auto-
Classification
User
Experience
User
Interface
File
Plan
Retention /
Disposition
Security
Model
Archiving
January 29, 2020

15. … Content Services include …
15
Content
Services
Knowledge
Management
Search e-Discovery
Digital Asset
Management
Digital Rights
Management
January 29, 2020

16. 3 Digital Transformation Journey
Part 1
16January 29, 2020

17. The current state
17
85% will never be retrieved 50% are duplicates
“… digital technologies, tools, and social media platforms now allow individuals to create
information at a torrid pace and instantaneously share it globally …” (IGBoK, 1st Ed., p 111)
“Information chaos and confusion are preventing organizations from achieving their digital
transformation objectives. Many organizations believe they must modernize their
information management strategy in order to meet this challenge and survive.” (John
Brown, CEO, HELUX)
January 29, 2020

18. Digital Transformation (DT) “pain points”
Cyber
Attacks
Data
Breaches
BYOD
Remote
Workforce
Change
Manageme
nt
Cloud
Services
Content
Repositorie
s
Informatio
n Chaos
Content
Monetizatio
n
DT
January 29, 2020

19. Digital Transformation enablers
19
Cloud
Enablement
Intelligent
Capture
Repository
Neutral
Content
Integrated
Collaboration
Information
Governance
Content
Services
Auto-
Classification
Customer
Experience
1
2
3
45
6
7
8
“… deploying capabilities or
services … that exists outside
the firewall.”
“… workflows to convert
physical information into
digital formats using multiple
channels … “
“… repositories that are
independent of … different
systems and underlying
technology platforms …”
“… technology platform …
that allows teams to save,
search, and share information
assets …”
“… the end-users' “felt
experiences” … with an
organization’s on-line
services and digital products
…”
“… using rules … to automate
how content is captured,
analyzed, and governed over
its lifecycle.” (AIIM)
“… delivers content and / or
services on demand,
regardless of its source, to
any device, and anywhere
…”
“… specification of decision
rights and an accountability
framework to encourage
desirable behavior …”
(Gartner)
Source: Intelligent Information Management Maturity (I2M2) Model
January 29, 2020

20. ARMA:
“The structure and
interrelationship of information,
especially with an eye towards
using business rules, observed use
behaviors, and effective interface
design to facilitate access to
information.”
(Glossary of Records Management and Information
Governance Terms, 5th Edition, ARMA International TR 22-
2016)
20
What is Information Architecture (IA)?
Treasury Board Secretariat:
“Information Architecture is the
structure of the information
components of an enterprise, their
interrelationships, and the
principles and guidelines governing
their design and evolution over time.
Information architecture enables the
sharing, reuse, horizontal
aggregation, and analysis of
information.”
(The TBS Information Management Policy, Govt. of Canada)
January 29, 2020

21. Information Architecture applies structure to
content sources
21
Social
Media
Blogs
Information
Architecture
Videos /
Pictures
Audio
Emails and
Documents
Direct
Messages
January 29, 2020

22. 2222
Information Architecture auto-classifies content
Information Architecture and Content
Services
January 29, 2020

23. 4 Unstructured Information
Part 1
23January 29, 2020

24. Unstructured content growthUnstructured content growth
30M
15GB
12.5EB
25TB
30,000,000,000,000
24

25. The Cost of Search
25
 49% said they have
trouble locating
documents
 43% have trouble with
document approval
requests and document
sharing
 33% struggle with the
document versioning
The average knowledge workers spends:
2.5 hours per day
15% to 30% of the workday
searching for information (IDC)
The inability to find and retrieve document
costs organization, that employ 1,000
workers,
$25 million per year
January 29, 2020

26. The High Cost of Document
26
For every $1 spent to
create a document $10
is spent on
management
30 billion documents are created every
year
(McKinseyGlobalInstitute)85% will never be retrieved
85%
50% are duplicates
60% are
obsolete
50%
60%
Document
Creation
Document
Management
January 29, 2020

27. Unstructured information comes from
…
27
85% will never be retrieved 50% are duplicates
Source: https://www.statista.com/chart/17518/internet-use-one-minute/
January 29, 2020

28. Risk of
losing
control of
information
Content
sprawl
Risk of
data
breaches
Unmanaged
grown
Risk of
non-
compliance
Poor
Governanc
e
Risk of
litigation
Information
leaks
… result in information chaos
28
85% will never be retrieved 50% are duplicates
January 29, 2020

29. Agenda – Part 1 recap
29
Introduction
Terms and Definitions
Digital Transformation Journey
Unstructured Information
1
4
2
3
January 29, 2020

30. Agenda – Part 2 recap
30
Information Chaos
What is Artificial Intelligence?
THEMIS CS for Auto-classification
Key Takeaways
5
8
6
7
January 29, 2020

31. 5 Information Chaos
Part 2
31January 29, 2020

32. Beware of the “Document Chaos
Monster”
32
Data Security
Storage Costs
User
Productivity
Compliance
Chaos Monster Victims
Office 365Adoption
SILOED CONTENT
UNSTRUCTURED
CONTENT
ROT CONTENT (Redundant, Obsolete, or Trivial)
MISCLASSIFIED
CONTENT
January 29, 2020

33. The Challenge of Taming the “Document
Chaos Monster”
33
SILOED CONTENT
UNSTRUCTURED
CONTENT
ROT CONTENT (Redundant, Obsolete, or Trivial)
MISCLASSIFIED
CONTENT
 Rely on Users to Classify
Documents
- inconsistent, incomplete, lack of
knowledge
 Automated Classification
Processes
- not smart enough, incomplete
processes
 Classification Workflows
- incomplete, inconsistent, reliance
legacy date classification codes
 AI Auto-Classification
- intelligent, complete, up to date,
scalable to large data sets, consistent,
ongoing
January 29, 2020

34. “Document Chaos Monster pain points”
34
Internal Drivers
• e-Discovery
• Records management
• Analytics for decision-making
• Metrics for predictive analytics
• Process inefficiencies
• Uncontrolled storage costs
• ROT
• Business continuity and resiliency
• Disaster recovery
External Drivers
• Privacy regulations
• Regulatory fines
• Consumer trust
• Competitive environment
• Political and legal environment
• Reputational damage
• Monetize content
• Digital rights
January 29, 2020

35. 6 What is Artificial Intelligence
Part 2
35January 29, 2020

36. “At last … an AI solution!”
36
85% will never be retrieved 50% are duplicates“Artificial intelligence (AI) has crossed the chasm; more companies and more executives
than ever before have come to realize the value that augmented intelligence offers their
firms. These companies have actively moved to implement the technology in their
organizations.”
(AI's Disruption Of Data Management: Is A Different Approach Needed?,
https://www.forbes.com/sites/forbestechcouncil/2019/09/06/ais-disruption-of-data-
management-is-a-different-approach-needed/#1061832a24df)
January 29, 2020

37. 37
In the movies you see AI, but …
January 29, 2020

38. .. when is AI the same as natural
intelligence?
38January 29, 2020

39. AI Concepts
39
Artificial
General
Intelligence
Natural
Intelligence
Artificial
Intelligence
Neural
Networks
Machine
Learning
Expert
Systems
Deep
Learning
January 29, 2020

40. Natural Intelligence (NI):
“… is the opposite of artificial
intelligence: it is all the systems of
control present in biology.”
(http://www.cs.bath.ac.uk/~jjb/web/uni.html)
40
Definitions
January 29, 2020

41. Artificial General Intelligence
(AGI):
“… is the intelligence of a machine
that has the capacity to understand or
learn any intellectual task that
a human being can.”
(www.wikipedia.org)
41
Definitions
January 29, 2020

42. Artificial Intelligence (AI):
“… is intelligence demonstrated by
machines, in contrast to the natural
intelligence displayed by humans,
… that perceives its environment,
and learns, makes decisions, and
takes actions that maximize its
chances of successfully achieving its
goals without human input.”
(Amitabh Srivastav, HELUX)
42
Definitions (modified from Wikipedia)
Red text is my
modification
January 29, 2020

43. Neural Networks:
“A neural network is a network or
circuit of neurons, or in a modern
sense, an artificial neural network,
composed of artificial neurons or
nodes.”
(www.wikipedia.org)
43
Definitions
https://commons.wikimedia.org/w/index.php?curid=5084582
January 29, 2020

44. Machine Learning (ML):
“… is the scientific study of
algorithms and statistical models
that computer systems use to
perform a specific task without using
explicit instructions, relying on
patterns and inference instead.”
(www.wikipedia.org)
44
Definitions
January 29, 2020

45. Deep Learning (DL):
“Deep learning is part of a broader
family of machine learning methods
based on artificial neural networks.”
(www.wikipeida.org)
45
Definitions
January 29, 2020

46. Supervised Learning:
Is training the classifier using many
labeled examples such as images of
a child playing with a dog and
learning to differentiate between the
two. Another example is recognizing
handwriting.
(“Unsupervised Deep Learning Recommender System for
Personal Computer Users”, NTELLI 2017 : The Sixth
International Conference on Intelligent Systems and
Applications (includes InManEnt))
46
AI deep learning models for auto-
classification
January 29, 2020

47. Unsupervised Learning:
Is the process of the classifier
learning without labeled examples
organized into a dataset and there is
no feedback to the classifier
(“Unsupervised Deep Learning Recommender System for
Personal Computer Users”, NTELLI 2017 : The Sixth
International Conference on Intelligent Systems and
Applications (includes InManEnt))
47
AI deep learning models for auto-
classification
January 29, 2020

48. Semi-supervised Learning:
It uses a small amount of labeled
data bolstering a larger set of
unlabeled data
(“Unsupervised Deep Learning Recommender System for
Personal Computer Users”, NTELLI 2017 : The Sixth
International Conference on Intelligent Systems and
Applications (includes InManEnt))
48
AI deep learning models for auto-
classification
January 29, 2020

49. Transfer Learning:
Is an approach in which the classifier
is trained on data that is augmented
by some other already trained model
(“Unsupervised Deep Learning Recommender System for
Personal Computer Users”, NTELLI 2017 : The Sixth
International Conference on Intelligent Systems and
Applications (includes InManEnt))
49
AI deep learning models for auto-
classification
January 29, 2020

50. Reinforcement learning
Is useful in use cases where the
feedback to the learning system only
arrives after some end state is
reached, or after a significant delay
(“Unsupervised Deep Learning Recommender System for
Personal Computer Users”, NTELLI 2017 : The Sixth
International Conference on Intelligent Systems and
Applications (includes InManEnt))
50
AI deep learning models for auto-
classification
January 29, 2020

51. 51
Use AI to auto-classify and enable
AI
Complianc
e
e-
Discovery
Records
Mgmt.
ATIP
Response
s
Open
Govt.
Archival
Unstructure
d
Analytics
January 29, 2020

52. Use AI to search repositories to classify
content
Laptops
Desktops
Cell
phones
Tablets
On-
premise Cloud
storage
Cloud
services
Hybrid
Offsite
storage
Unstructured
data accounts
for 80% of
content on
devices and in
repositories
Very large
amount of
“dark data”
stored in
repositories
AI
January 29, 2020

53. Use AI to search repositories to classify
content
Laptops
Desktops
Cell
phones
Tablets
On-
premise Cloud
storage
Cloud
services
Hybrid
Offsite
storageAI
Unstructured
data accounts
for 80% of
content on
devices and in
repositories
Very large
amount of
“dark data”
stored in
repositories

54. 54
Are these products AI “in action?”
Product Description
Alexa, Siri,
Cortana, and
Google
Assistant
These are virtual (voice / digital) assistants that respond to voice queries
and use NLP to answer questions, make recommendations, and perform
actions
Watson An AI product from IBM that uses NLP to answer questions and is used in
healthcare, education, weather forecasting, etc.
Debater An AI project from IBM, designed to participate in a full live debates with
expert human debaters
January 29, 2020

55. 55
What about AI for content?
Term Description
Classifiers Classifiers can greatly increase the number of content items that
are labeled by learning from the input data given to it and then
using this knowledge to classify new observations
Entity Extractors Extract and process information to identify and classify key
elements from text into pre-defined categories to help transform
unstructured data to structured data
Image Recognizers Gives a machine the ability to interpret the input received
through computer vision and categorize what it “sees”
Independent Component
Analysis
Look for patterns in data that are not obvious to humans
Machine to Machine
Learning
How will AI treat content, especially ethical consideration
January 29, 2020

56. 56
What is Microsoft’s Project Cortex?
“Project Cortex uses AI to create a knowledge network that reasons over your
organization’s data and automatically organizes it into shared topics like projects and
customers. It also delivers relevant knowledge to people across your organization
through topic cards and topic pages in the apps they use every day.”
(www.microsoft.com)
January 29, 2020

57. 57
Using the metadata as a foundation
Coherent across
Microsoft 365
Discover enterprise
content based on
terms
Consistent tagging
experience with
contextual term
suggestions and Auto
Tagging
Improved enterprise
content type
syndication,
discovery, and
enforcement for
consistent metadata
schemas across
tenant
January 29, 2020

58. 7 THEMIS CS for Auto-classification
Part 2
58January 29, 2020

59. 59
Information Architecture and AI?
January 29, 2020
“There is no AI without IA!”
- John Brown, CEO, HELUX

60. How does THEMIS CS “Slay the
Monster?”
60
Unstructured
Content
Information
Architecture
Design
Artificial
Intelligence
Rules
Structured
Content with
metadata
Internal Drivers
January 29, 2020

61. THEMIS CS can search repositories to
classify content
Laptops
Desktops
Cell
phones
Tablets
On-
premise Cloud
storage
Cloud
services
Hybrid
Offsite
storage
Unstructured
content on
devices and in
repositories
Very large
amount of
“dark data”
stored in
repositories
THEMIS
CS
61January 29, 2020

62. THEMIS CS bookends the Office 365
content lifecycle
Create &
Capture
Classify
Document
Management Collaborate
Search
Share &
Distribute
Content
Preparation
Turningdocumentchaosinto
order, control,andstructure
Automated
InformationArchitecture
Deployment
Auto-Classification of
Unstructured Content
Compliance & GovernanceCollaboration & Document ManagementMigration & Organization
Ongoing
Governance
Ensuring ongoingdocument
control,governance,findability,&
organization
InformationArchitecture
Auto-Classification of
UnstructuredContent
RecordsManagement
62January 29, 2020

63. OLD WAY
Use Excel or Word
SLOW, TEDIOUS, AND COSTLY PROCESS
SPECIALIZED TEAM
Rinse &
Repeat
Requirements
Gathering
IA Assembly
in Excel
Send to
Development
Back to
Users
User Acceptance
(Maybe!)
IA Expert IT IM Developer
MODERN WAY
CREATE IA INTUITIVELY ONLINE
ANY TEAM
Import &
Analysis
Design &
Visualization
User
Acceptance
Rapid
Deployment
CREATE IA MANUALLY OFFLINE
QUICK, PAINLESS PROCESS
Your Team
IA … then and now … using THEMIS CS
63January 29, 2020

64. 64
IA made easy using THEMIS CS
THEMIS CS turns the complex, time-intensive
task of building and maintaining a robust IAinto
an automated, intelligent process using AI.
Cut your deployment times by 50% or more
Guaranteed 100% error-free deployments
Effective Team Collaboration
January 29, 2020

65. 65
IA made easy using the THEMIS CS
process
IA Visualization
Publish and GO LIVE!
1 2
3
THEMIS IA Designer
IA Analysis Wizard
Iteration and Acceptance
Deploy to Sandbox
4
5
6
THEMIS CS’s step-by-step wizard will ensure you deploy an error-free IA
built on top of industry best practices
January 29, 2020

66. 66
THEMIS CS features for auto-classification
THEMIS CS
Information
Architecture
Assistant
using a
Chatbot
THEMIS Blueprint
Information
Architecture
Assistant using
Machine
Learning
Chatbot assists in
designing the
information
architecture and
configuring GCdocs
or SharePoint
ML recommends
appropriate taxonomy,
folder structure
(GCdocs), site structure
(SharePoint) based on
user-provided
information and using
best practices
HELUX-hosted service
that stores information
architecture blueprint
snippets and then builds
a blueprint based on
best practices
January 29, 2020

67. Eight ways THEMIS CS enables Digital
Transformation
67
Digitize
Paper
Analyze
ROT
Import
Information
Architecture
Apply AI
Rules
Accurate
Auto-
Classification
Rapid
Deployment
Improve
e-Discovery
Increase ROI
on Content
1
2
3
45
6
7
8
January 29, 2020

68. 68
THEMIS CS uses AI for ROT analysis
January 29, 2020
Inventory the
target content
sources
• Shared drives
• Laptops / Desktops
• Off-line
repositories
• Cloud storage
• Mobile devices
Rules to
identify the
content types
• Personally
information
• Health information
• Employee
information
• Confidential data
• Public information
Schema to
classify
content
• File plan
• Taxonomy
• Metadata
• Retention and
disposition
schedule
Consider
additional
rules
• Relevant
regulations
• Industry standards
• Best practices

69. 69
THEMIS CS uses AI for email auto-
categorization
January 29, 2020
Extract
email
headers
• From
• To
• Subject
• Date
• Copied to
• Attachments
• Etc.
Rules to
identify
email
content
• Personal
information
• Health information
• Employee
information
• Confidential data
• Public information
Identify
duplicate
emails
• Duplicate
threshold
• “Near duplicates”
Put
unknown
emails into
“quarantine”
• Does not match
any rules
• Matches rules for
further analysis
Schema to
classify
email and
content
• File plan
• Taxonomy
• Metadata
• Retention and
disposition
schedule

70. 70
THEMIS CS Use Case for AI and Auto-
Classification
Problem
Description
Business Challenge Solution Benefits
• Several terabytes of
pictures and videos on
share drives
• Many years worth of
physical pictures
• Difficult to work with
physical pictures
• Storage costs are
increasing
• Volume of content is
increasing
• Identify duplicates and
“near duplicates”
• Identify content to
dispose
• Tag content with
appropriate metadata
• Retain content for on-
going operations and
possible litigation
• Reduce storage costs
• Accurately and
consistently classify
digital the physical
content
• Use THEMIS IA to
rapidly build the
architecture, rules, and
metadata to tag content
• Use THEMIS AI to
search the shared
drives and apply the
rules and auto-classify
the content
• Use THEMIS RM to
apply the retention and
disposition schedules to
the auto-classified
content
• Correctly and
consistently auto-
classify content
• Identify and dispose of
ROT
• Improve accuracy of
search
• Improve e-Discovery
• Reduce storage costs
• Teach THEMIS AI
additional rules to auto-
classify more content
• THEMIS AI is
“resource” available
24/7 to handle
increasing volumes
January 29, 2020

71. 8 Key Takeaways
Part 2
71January 29, 2020

72. THEMIS CS controls the “Document
Chaos Monster”
72
SILOED CONTENT
UNSTRUCTURED
CONTENT
ROT CONTENT (Redundant, Obsolete, or Trivial)
MISCLASSIFIED
CONTENT
Data Security
Storage Costs
User
Productivity
Compliance
Chaos Monster Victims
Office 365Adoption
January 29, 2020

73. 73
The THEMIS CS Advantage
THEMIS CS
Manually build IA
with spreadsheets
Hire consultant
Time to Deployment
Cost Effective
Accuracy
Error Free
User Satisfaction
Ongoing Monitoring &
Improvements
January 29, 2020

74. IA integrates:
File plan
Taxonomy
Retention and disposition
schedules
Security groups, permissions,
and user accounts
Metadata
Content types, document types,
categories and attributes
74
THEMIS CS benefits
IA enables:
Improved UX and UI via better
navigation
Improved search experience via
better auto-classification
Improved collaboration and
knowledge sharing via a more
intuitive design
Improved change management via
more user awareness and easier
user adoptions
January 29, 2020

75. Thank you
Amitabh Srivastav, VP, Operations & Governance
MSc (Proj Mgmt), MSc (Comp Sc), BCSc (Hons)
amitabh@heluxsystems.com
www.linkedin.com/in/amitabhsrivastav
@am_srivastav
75
Please contact or follow HELUX at
(613) 291-2683
https://www.heluxsystems.com
info@heluxsystems.com
@HeluxSystems
https://www.linkedin.com/company/helux-systems/
https://www.facebook.com/HELUXSystems/
Amitabh Srivastav
IGP, CIP, PMP
January 29, 2020