St. Louis User Group - Amazon Web Services · St. Louis User Group Relativity Bag of Tricks: ... – Report data to show ... • Dashboards to show progress of documents set to review

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St. Louis User Group

Relativity Bag of Tricks: Solving Common Problems in Unique Ways


• Introductions

• Tricks from the Relativity bag

– User experiences and tips

– Let’s have a conversation

– Ask questions

– Contribute your stories

Agenda


• QC Protocols for contract reviews (and in house too)

• Effective communication between the case team and the review team

• Hints to manage your project in Relativity

• Best practices to keep your stakeholders informed with reporting tools

• Quick wins when using Relativity Assisted Review

Bag of Tricks


• Create a view for Privilege QC

• Encourage reviewers to do their own QC

• Use a second level review layout

• Take advantage of built in Sampling

QC workflows


View: Privilege QC


View: Self QC


QC Layout


QC: Create a Good Layout


QC: Create a Good Report to Track Results


Sampling for Success



QC: Use Sampling to Your Benefit

1 2


QC: Save

the Results


Effective communication between the case team and the review team

• Processing detail (summary)-

– De-duplication

– Exceptions

– Other issues- Missing files, OCR

failures

• QC searches-

– Responsive Set

– Family Conflicts

– Needs Redaction

– Tech Assist

Layouts-Document MetadataHas hidden data and exceptions

Reports-Batch progress- updateRAR-updateSTRs (key terms and privilege)- iterative

Security views-Layout mockup

Preview for case team


• Establish the big picture

• Prepare for new workspace and data

• How do you handle new data

• Tips to conduct the review

• Readying a solid production

Hints to manage your Relativity project


Keeping Stakeholders Informed

• Where in the process are you?

• Who are the stakeholders?

• What do they want to know?


Processing

• Client: wants to know what they gave you and how much it is going to cost

– report showing data by custodian

– total size/cost

• Legal Team: needs data to support their discovery efforts

– Volume at start

– Volume deduped

– Volume left out from culling parameters


Pre-Review

• Partner: wants to know how much there is to review and how long it will take.

– Report on average review times for that much data

– Report data to show whether, in this particular case, analytics will help save review

time/money

• Associate: wants to know best place to start review

– Reports on search terms, concepts, domains


Review

• Dashboards to show progress of documents set to review will keep everyone up to date to the

minute. Can help forecast how much longer it will take.

• BUT…make sure to have a dashboard that shows status of QC as well, so there are no

surprises.


Production

• QC dashboards from review will help alert Lit Support that a production is imminent, even if the

legal team does not communicate well!


Post-Production

• Fact Manager can help the team keep up to date on what useful (or not useful) information has

been located in the documents.


• Time to PLAN

• Train the Reviewers

• Rounds of judgmental sampling

• End of the Game – dealing with the NRs

Quick wins using Relativity Assisted Review


RAR Case Checklist Meets Criteria

Minimum of 50,000 records with text

Concept-rich files, e.g. emails, word documents, limited excel files, or graphic

images

Issue or privilege coding in separate field not part of Assisted Review workflow

Is this previously reviewed data? If so, how were families reviewed?

Expected timelines given

Expected process given

Number of reviewers

Level of reviewers

Level of accuracy

PLANNING - Assisted Review Checklist*

- Does this dataset meet the criteria?

See https://help.kcura.com/9.4/Content/Recipes/Checklists/RAR_checklist.htm

https://help.kcura.com/9.4/Content/Recipes/Checklists/RAR_checklist.htm


• Selecting good example documents

– Quality of the example is independent of responsiveness – it’s a second dimension

– Some documents may be highly responsive, yet undesirable as example documents for a

computer-assisted review project

– Four Corners Doctrine

• Applying the Use as Example field

• Using the Excerpt Text feature

Train the Reviewers


• Hot Documents

• Important Custodian

• Keyword Search Hits

• Previously Coded Documents

• Clustering

PROJECT – Judgmental Sampling Options


Options

• Save until last (still reviewing all)

• One last sample

• Keyword searches for hot documents

END GAME: Uncategorized/Non Responsive Documents


Thanks for attending!