Speakers: Doug Magnuson, Thomas E. Reding, Brian Tuemmler, & Marcia Zweerink, Ph.D. Data is growing at an astounding pace. The volume has long since outstripped the capacity of manual intervention in e-records management. As organizations struggle to improve RM their compliance and better their information governance, they are recognizing the critical need a for a much better (and probably radically different) way to manage their e-records and legacy data. An increasingly promising answer is content analytics - which, through more advanced tools, increasingly reduces business risks and provides new competitive advantages.Now, many believe that the time has come for content analytics to play an important role in the management of e-records and legacy data. Read more: http://www.rimeducation.com/videos/rimondemand.php

1. Cohasset Associates, Inc.
NOTES
Session 20
Analytics
The New Way to Manage e-Records
Tuesday, May 08, 2012 3:15 – 4:30 pm
G
What is Analytics?
Analytics is the application of computer
technology, operational research, and statistics to
solve problems in business and industry.
p y
Wikipedia
2
Why Analytics?
Identification of Official Records
Cleaning up ‘stuff’
Finding relevant files for e-discovery
g y
Business insight
3
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Why Analytics?
Ginormous volume 10 TB at 1 minute / doc = 311 years
Those unreliable users
g
Evolving locations
4
"In theory there is no difference
between theory and practice.
In practice there is."
p
Yogi Berra
5
"If you come to a fork in the road, take it."
Yogi Berra
6
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Enterprise Content Management
Doug Magnuson
IBM
© 2012 IBM Corporation
Enterprise Content Management
Traditional approaches are converging
More than keyword Analyzing unstructured
search is needed content no longer optional
“Making unstructured data “For many business process
searchable is now a presumed professionals, access to structured
primary interface for applications of Enterprise Business data, even when supported by BI or
all kinds, as well as for intranets predictive analytics, lacks sufficient
and content repositories.” Search Intelligence context for customer
service, finance, and other areas where
– Whit Andrews, Rita Knox Gartner
, communications with customers involves
Content many channels”
Analytics – Craig Le Clair Forrester
Increasing in business Converging toward
importance Text content analytics
“Early adopters of [text analytics] Analytics “Every enterprise should understand
are already gaining a competitive how content analytics can produce
advantage. Organizations that fail to answers to its critical questions;
do so will be at risk.” understanding this now will make it
possible to exploit these tools as their
– Sue Feldman IDC availability proliferates.”
– Rita Knox Gartner
8 © 2012 IBM Corporation
Enterprise Content Management
Content Analytics Explained
Analyzed Content
Extracted
Claimant: Soft Tissue Injury Concept (and Data)
Person Injury Body Part Location
Noun Verb Noun Phrase Prep Phrase
John sprained his ankle on the step
...
Source Information
Internal (ECM, Files, DBMS, etc.)
and External (Social, News, etc.)
What is Natural Language Processing?
NLP describes a set of linguistic, statistical, and
machine learning techniques that allow text to be
analyzed and key information extracted for business
integration
9 © 2012 IBM Corporation
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Enterprise Content Management
Real language is real hard
Chess
 A finite, mathematically well-defined search
space
 Limited number of moves and states
 Grounded in explicit, unambiguous
mathematical rules
Human Language
 Ambiguous, contextual and implicit
 Contains slang, riddles, idioms, abbreviations,
acronyms and more
 Grounded only in human cognition
 Seemingly infinite number of ways to express
the same concepts and meaning
10 © 2012 IBM Corporation
Enterprise Content Management
The key is: understanding natural language with confidence
and accuracy
 Where was Einstein born?
Unstructured Structured
One day, from among his city views of
Ulm, Otto chose a watercolor to send
to Alb t Ei t i
t Albert Einstein as a remembrance
b
of Einstein’s birthplace.
 Welch ran this?
If leadership is an art then surely Jack
Welch has proved himself a master
painter during his tenure at GE.
11 © 2012 IBM Corporation
Enterprise Content Management
Things we learned from The Jeopardy! Challenge
5 key dimensions to drive the technology
$200
If you're standing, it's the
direction you should look
to check out the
wainscoting
Broad/open domain
$800
Complex language
C l l In cell division, mitosis
splits the nucleus &
cytokinesis splits this liquid
High precision cushioning the nucleus
Accurate confidence $1000
Of the 4 countries in the
world that the U.S. does
High speed not have diplomatic
relations with, the one
that’s farthest north
12 © 2012 IBM Corporation
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Enterprise Content Management
Decision Plans Layer Multiple Methods for Records classification
Consistency Consistent
High Participation & Multiple
Enforcement
Accuracy Methods
Context Based
Imply
Classification
Rules Based
Inspect
Classification
Decision Plans
combine approaches t
bi h to
Ask classification
Manual
Classification
Cost Savings
Low Productivity
Low High
Context-based classification delivers high accuracy, rules-based classification
addresses hard-and-fast requirements. Combining methods delivers the best
results.
13 © 2012 IBM Corporation
Enterprise Content Management
High
Rule Systems - the Effect of Real-Time Learning
Multiple
Methods
Context Based
Classification
Rules Based
Classification
Use rule systems to act on existing meta data available in the
Manual
process, content system or document properties. Low
Classification
Low High
14 © 2012 IBM Corporation
Enterprise Content Management
High
Context Based Classification
Multiple
Methods
Context Based
Classification
Rules Based
Classification
Use context based classification to inspect the document when
Manual
there is not enough meta data already available Low
Classification
Low High
Simple rules or keyword based analysis can be too coarse to make fine distinctions between long-form texts
with very different intent
15 © 2012 IBM Corporation
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Enterprise Content Management
High
Critical dimensions of classification:
Multiple
Methods
Context Based
Classification
Magnified by exploding volumes
Rules Based
Classification
Manual
Classification
Low
Low High
Use manual classification for high
value documents or when other
methods do not provide enough
information. Manual Automated
Accuracy
y X
92% 60 – 90%
46%
Cost (per doc) $ 0.17 < $ 0.01
Consistency <50% 100%
Increasing volume and variety of information magnifies the challenges of
consistency and cost burdens
16 © 2012 IBM Corporation
Enterprise Content Management
Quickly Understand Timeline & Essence of Custodian & Business
Information
Quickly get a view of the
people, sender and recipient
domains, and companies
involved.
Combine facets and filters to
quickly include and eliminate
custodians and data – such as
people from certain locations or
other combination.
Automatically extracted phrases
in the content show the essence
of the information.
f th i f ti
Organize a topographical view
by key category. The “peaks”
show frequency and phrases to
quickly identify relevant
information.
17 © 2012 IBM Corporation
Tom Reding, CRM
Principal, Information Governance Practice
tom.reding@emc.com
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CIS - Automated Analytics
Content Analytics
CONTENT
CONTENT TEXT ENTITIES RELATIONSHIPS EASILY
ADDED ANALYZED EXTRACTED STORED FOUND
© Copyright 2011 EMC Corporation. All rights reserved. 19
Discover and Act on Legacy Information
File Intelligence – Understanding what you have
File System
Intelligently
Identify Records Migrate &
Secure
Records
Email Server
SharePoint
Secure Repository
File Intelligence
Content
+
Repositories
Retention Policy
Personal Email
Archives
Notebook and
Desktop
© Copyright 2011 EMC Corporation. All rights reserved. 20
How File Intelligence Works
Catalog Analyze Act
Classify Search Report
Crawl data Classify files based on metadata keyword
metadata, Robust action
sources content, and pattern matching set
Build index – Move, copy,
– Metadata basic Age, owner, location, file type, etc. delete,
– Metadata with retain,
document type Business value, security risk, intellectual export, tag
– Metadata with property, PII, PCI
hash Policy-based
– Deep crawl full Analyze data with search and report tools actions
text – Semantic search with – One-time
– Deep crawl Boolean, proximity, stemming, phrase support
with – Scheduled
– More than 30 pre-built reports out of the box
classification – Recurring
– Custom reports as needed
© Copyright 2011 EMC Corporation. All rights reserved. 21
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Solution Overview Secure, Retain, Discover 3
Enterprise
Retention
1 2
File Content
Intelligence Capture /
Archiving
Electronic
Discovery
4
• Crawl, Index , analyze, search, report information repositories in-place
File Intelligence • Take action upon the discovered information assets
RPS • Examples: Decommission non-required information in-place, capture
& classify records
© Copyright 2011 EMC Corporation. All rights reserved. 22
Experience with File Intelligence
• ~24% of unstructured data is actively
Active,
used known, 24%
• ~48% is stale: not touched in 6 months relevant
• ~18% are duplicates
• ~6% is unknown or orphaned
• ~4% is not business related - pictures
Stale 48%
• Cost to the Customer:
– It consumes expensive storage
capacity
– It gets managed, backed
Duplicates 18%
up, replicated, ...
– It poses serious legal & compliance 6%
Unknown
risks
Non-business 4%
– It gets recovered equally in a DR related
scenario * Results from 37 Kazeon customer assessments
Stale is defined as files not accessed or modified
for 6 months
© Copyright 2011 EMC Corporation. All rights reserved. 23
Analytics for eDiscovery
Full Case Management Workflow Case Tracking Preservation Notification
– New case creation – Collection Status – E-mail notification to custodians
– Assignment of lead attorney & reviewers – Document Review Status – Customize e-mail messages per matter
– Case specific collection & culling – Reviewer Workload – Full tracking during hold notice lifecycle
– Legal case processing – Reviewer Progress Tracking – Automatic reminders
– Document review & analysis – Custodian or Proxy acknowledgment
 Early case assessment throughout the Legal Hold Notices
eDiscovery process
Keyword Hit Report
 Identify relevant, important data
 Analytics on relevant legal case data
 E-mail communication threads
 Prepare for FRCP meetings
E-mail Threading
Custodian & Concept Analysis
24
© Copyright 2011 EMC Corporation. All rights reserved. 24
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Network Drive Transformation
Analytics
Brian Tuemmler
Information Management for Everyone 25
Enterprise Knowledge
Case
Data SAP
Mgt
GIS
Tech
Wikis & Blogs Supp
Dev
Share
ECRM Point
Other
Shared Drives M:
Hard Copy Central Off site Desks
Information Management for Everyone 26
Analytics Perspective
Taxonomy
Development
Records
ICM Tools
Retention
ECM
C Image
I
Repository Conversion
Architecture Services
ERM & Cleanup
Compliance and Preservation
Policies
Transform
Information Management for Everyone 27
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Program Approach
Repeat by Repeat by
Share Workgroup
Strategy
Record
Governance Group cleanup
Definition
and Policy
IT
Individual Content
Infrastructure
cleanup Migration
(Mapping)
Query
Auto cleanup Improvement
Definition
Information Management for Everyone 28
Cleanup Categories
Recent
Capture
Voluminous
Database
Non‐capturable
Important
Application
For review Garbage
Delete
opportunities
•Large Scheduled
ed
duplicates
Expire
Past
Past
Manually D
•Photos &
retention Identified
Opt in Delete
media
date garbage ‐ "To
be deleted" Temporary
Auto‐Delete
Policy Backup
deletes
Zero content
User Intent
Information Management for Everyone 29
Example
80000
Category Volume by Year
60000
40000
20000
Storage
0
1997
1998
1999
2000 Document Image
2001 Content
2002 Photo
2003
2004 Database
2005 Archive
2006 Media
2007 Application
2008
2009
Information Management for Everyone 30
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Discussion
31
Contact Information
Marcy Zweerink
Marcy.Zweerink@Cohasset.com
Doug Magnuson
dmagnuson@us.ibm.com
Tom Reding
Tom.Reding@emc.com
Brian Tuemmler
Brian.Tuemmler@gimmal.com
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