1. Beyond Predictive Coding – The True Power of Analytics

2. Panelists: Hal Marcus, eDiscovery Attorney working with Product Marketing team, Recommind Susan Stone, Senior Solutions Consultant, Advanced Discovery Moderator: Stephen Dooley, Senior Manager - Electronic Discovery & Litigation Support, Sullivan & Cromwell LLP Introductions

3. Researching your Data Clustering and Organizing Email Analysis QC and Redactions Categorization Overview

4. Researching Your Data

5. Search with text from anywhere: Excerpt from Wikipedia News Media Articles Documents from client Concept Searching

6. Concept Browsing

7. Phrase Analysis

8. Use for internal investigations, especially with fraud components Apply when keyword searches are not yielding much information Target documents of interest and then categorize to find additional documents Researching your Data - Recap

9. Clustering/Organizing

10. Clustering Review and filter by like concepts

11. Organizing with Smart Filters Filter and analyze. Then save into “Review Universes”

12. Group documents intelligently based on a range of criteria Overlay search, metadata, analytics Organize for priority treatment and/or discrete review workflows Clustering and Organizing - Recap

13. Email Analysis

14. Email Threading

15. “End of Branch” Email

16. Hypergraph Email Analysis

17. Reduce the volume of review substantially Ensure consistency in coding (view and/or bulk code as desired) Identify possibly missing components Email Analysis – Recap

18. QC and Redactions

19. Identify Textual Near Duplicates

20. Textual near Duplicate Compare

21. Consistent Coding

22. Smart Redactions

23. Global Smart Redactions

24. Textual Near Duplicates Identify near duplicates Review changes Provide consistent coding Smart Redactions Identify PII, PCI, PHI Redacts regular expressions Redact full phrases/sentences Use colors and reasons for QC QC and Redactions – Recap

25. Categorization

26. Coding just 5 documents able to categorize hundreds

27. Docs with priv search terms Docs suggested by machine learning Relevant and privileged 2nd: Use relevant priv docs to train for privilege - and find anything missed by keyword searches Total document corpus Help identify potentially privileged docs 1st: Prioritize your review of docs hitting on priv search terms

28. Use Clustering to identify materials provided by opposition Use categorization on documents from your review set to identify hot materials from opposition documents. Where machine learning overlaps with search and/or other analytics, you have key documents to review Categorization – Recap