building information governance policies and workflows
TRANSCRIPT
Building IG Policies and Workflows
Presented by kCura and the Information Governance Initiative
@RelativityFest #IGwebinar
© 2015 kCura. All rights reserved.
Welcome to the Relativity Fest Webinar Series
• Check out a recording of last month’s judicial panel:www.kcura.com/relativity/resources/judicial-panel-webinar
• Stay tuned for more on our website’s Training Center or by subscribing to the Relativity Blog
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Quick Notes
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• We may grab questions along the way, but we’ll save most for the end of the webinar*
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© 2015 kCura. All rights reserved.
• Rene Laurens, kCura • Bennett Borden, IGI
Presenters
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IG, Data Remediation, and How They Intersect
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© 2015 kCura. All rights reserved.
What is Information Governance?
The activities and technologies that
organizations employ to maximize the value
of their information while minimizing
associated risks and costs.
93%
of Respondents Agreed
96%
of Practitioners Agreed
95%
of Providers Agreed
85%
of Analysts Agreed
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© 2015 kCura. All rights reserved.
Remediation is the process of bringing order to information through policies,
practices, and technology. It is the remedying of ungoverned data.
What about Data Remediation?
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• Data remediation is a common first step in developing an IG program.
• Good start toward a proactive approach to addressing growing data volumes because you’re:
How Do They Work Together?
1
2
Learning about your data sources
Analyzing the data’s content and context
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Rules about data retention, structuring, and disposition
Standardized, consistent legal hold policies
System-specific procedures
Information security and protection policies
Regular assessment and alignment as necessary
Building a Strong Foundation
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Parallels to e-Discovery and How to Get Started
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• Proper IG means less junk to collect, process, and review during e-discovery.
• If data remediation is a logical first step toward responsible IG, IG is a logical first step toward proportional e-discovery.
Where Does e-Discovery Come in?
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• IG teams are built by IT, legal, and other professionals—collaborate!
• Good sources for legacy content to consider:– Data being archived or migrated during hardware upgrades– Data being migrated to the cloud– Data that needs to be handled after an employee leaves the
company
How Can Your Team Begin?
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© 2015 kCura. All rights reserved.
Sort business information into three actionable buckets:
Data we understand well enough to
defensibly delete.
Data we understandwell enough to retain.
Data we don’t have enough information
about to make a decision either way.
How Can Your Team Start?
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IG and e-discovery workflows are similar
Use Existing e-Discovery Software
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© 2015 kCura. All rights reserved.
Custom workflows in your e-discovery
software can help you avoid the need
to expand your software and training
budgets
Use Existing e-Discovery Software
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© 2015 kCura. All rights reserved.
Use filtering options in collection and
processing stages to sort data into buckets early on
Use Existing e-Discovery Software
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© 2015 kCura. All rights reserved.
Data in the “not enough information”
bucket can be analyzed with the
same text analytics tools used for e-
discovery projects
Use Existing e-Discovery Software
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Example Workflow
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© 2015 kCura. All rights reserved.
• We have 40,380 documents to sort into our three buckets:
Example: Use Existing e-Discovery Software
Data we understand well enough to
defensibly delete.
Data we understandwell enough to retain.
Data we don’t have enough information
about to make a decision either way.
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Stage 1: Collection
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Begin a new collection (40,380 documents)
Exclude the date range you don’t need (-6,012)
Ignore sources and folders you don’t need (-1,700)
Collect the remaining data for further analysis (=32,668)
Create a report to capture your decisions
Stage 1: Collection
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Stage 2: Processing
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Begin processing your collection (32,668 documents)
Exclude records by NIST, file type, and email domain (-71)
Exclude records by email domain (-2,478)
Process the remaining data for further analysis (=30,119)
Create a report to capture your decisions
Stage 2: Processing
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Stage 3: Analytics
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Index your workspace (30,119 documents)
Run clustering to sort into similar concepts
Focus only on sample sets with categorization
Find similar documents based on key records
Create a report to capture your decisions
Stage 3: Analytics
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Results
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© 2015 kCura. All rights reserved.
40,380 documents down to
30,119 documents.
Simple collection and processing workflows alone removed
25.4% of our original data set.
Results Before Analytics
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How do you think the proposed revisions to FRCP 37(e) will affect evolving IG policies?
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Which department in an organization should take ownership of IG policies and procedures?
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What’s a common mistake made by organizations first trying a data remediation project, and how can it
be avoided?
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Open Q&Akcura.com/relativity/igresources | iginitiative.com | [email protected] | Find us at LegalTech!
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Thanks for attending!You’ll receive a follow-up email soon.
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