OpenText Introduces the First Auto-Classification Solution with Built-in Transparency and Defensibility This new offering delivers consistent, defensible classification of enterprise content including email, Social Media and legacy content without end-user intervention.

2. What if...

3. It is possible to...
REDUCE
Compliance
Legal Risk
Security
eDiscovery Costs
Responsiveness
Storage Cost
INCREASE

4. Using Records Management
driven Archiving
for responsible
Retention, Disposition and Legal Hold

5. You don’t have to take
our word for it...

6. Business Drivers
When you consider current projects and priorities for managing electronic
records, which THREE of the following are the strongest business drivers for your
organization? 0% 10% 20% 30% 40% 50%
Compliance with statutory
Compliance
records legislation winning out this
Reduce storage costs time over sharing
Compliance with industry knowledge (just),
regulations
Exploit and share our
knowledge resources
Improve litigation
but reducing
performance and cut costs storage costs a
Quality improvement
program high priority.
Better response to
events, accidents, press, FOI…
More personal & accurate
service to customers
Green initiatives
Compliance with
employment regulations
Faster and cheaper financial
audits N=717
©AIIM 2011
6

7. E-Discovery - ROI
By how much do you feel your audit, legal costs, court costs, fines and damages
could be reduced if you applied best practice records management, security and
e-Discovery procedures in your organization?
% of respondents
0% 5% 10% 15% 20% 25% Average is 23%
reduction in
0% - We feel we already do this!
costs (inc.
5% less “already do
10% less this”, 27%
15% less
otherwise).
25% (by a quarter)
25% reduction
33% less is a common
50% (by a half)
estimate.
More than halved
N=358, excl. 292 Don't Knows
©AIIM 2011
7

8. All you have do is...

9. Ask end-users to
sort and identify business records and transitory content
CLASSIFY
CONTENT
because classifying information drives
retention and disposition

10. As if..!

11. Introducing
OpenText Auto-Classification
OpenText Auto-Classification is the first automated classification
application with built-in transparency and defensibility
Transparent Defensible
Step-by-step tuning Built-in statistical sampling
guide and feedback and quality assurance

12. OpenText Auto-Classification
Classifies content based
on understanding of
the content, rules, or a
combination of both
Critical for low touch, high
volume content
Remove end-users from the equation

13. Records Manager’s Requirements
for Auto-Classification
A transparent process Facilitate adequate
such that the basis for sampling to demonstrate
automated classification both classification accuracy
decisions can be readily (precision) and
understood, tuned and completeness (recall) that
explained aligns with the risk profile
of the information

14. Can we trust the
technology..?

15. Can We Trust the Technology
YES!
If it is used effectively
and is accompanied by
a defensible, consistent
classification program
Auto-Classification practices will be challenged

16. Is Perfection the Standard?
Auto-Classification
demonstrates a
programmatic, consistent
and thorough approach
to retention and
disposition
Courts do not demand perfection – just
reasonable, good-faith efforts

17. Was Perfection Ever the
Standard?
Accuracy Buy-In Relative Accuracy
90% 50% 45%
Human
(reference)
60% - 90% 100% 60% - 90%
Machine
(challenger)

18. Auto-Classification can be
part of our RM program

19. OpenText Auto-Classification
Transparent, Defensible Auto-Classification
 Apply RM Classifications  Addresses the
as part of a consistent, fundamental issues
programmatic element of associated to applying
an Information classifications to high
Governance Program volume, low touch
 Reduce content
 Litigation risk  Addresses Records
 Storage Costs Manager’s and
 eDiscovery costs Compliance concerns for
 Improve end-user defensibility and
productivity and reduce transparency
compliance concerns

20. OpenText Information
Governance Solutions
Striking a balance between
competing priorities in the
creation, retention and disposition
of content