Washington DC User Group

Transcription

1 Washington DC User Group Analytics Workflows for Smaller Cases and QC Workflows

2 The D.C. Steering Committee Kristin Thompson Williams & Connolly Margaret Havinga - Williams & Connolly Richard Addison - Finnegan Kate Bauer - Steptoe & Johnson

3 How does a user group work? You, the community, drive the user group s topics and conversation kcura is here to moderate the conversation The more you put into the meetings the more you get out Surveys will go out at the end of each meeting. Please fill them out!!

4 Who are you?

5 Analytics for smaller cases

6 Relativity Analytics features Structured threading Textual Near Duplicate ID Language identification Conceptual Concept searching Categorization Clustering Keyword expansion

7 Features by use case Use Case Narrowing the review set Feature threading Foreign language identification

8 Features by use case Use Case Narrowing the review set Quality control Feature Near duplicate identification Cluster visualization

9 Features by use case Use Case Narrowing the review set Quality control Investigation Feature Keyword expansion Concept search

10 Features by use case Use Case Narrowing the review set Quality control Investigation Organizing large sets of data Feature Clustering Categorization

11 Common Objection: Analytics is best only for the largest cases. Messaging with analytics in e-discovery has focused on large case wins. There are a number of uses for most cases. For example: Batching Reviewing conceptually related documents increases review speed Reviewing threads together reduces inconsistencies Production prep Analytics can help to avoid mistakenly producing privileged docs Keyword sampling Find potentially relevant documents that keywords miss Threading Only review inclusives and reduce the volume of

12 Threading

13 2/24/99 11:25 a.m. 4/29/99 6:45 p.m. 4/30/99 9:03 p.m. 4/30/99 7:00 p.m. 4/30/99 7:22 p.m. 4/30/99 10:24 p.m. 5/1/99 12:57 a.m.? Barry Pearce Bob Crane & Jeff Harbert Maria Nartey Richard Sage & Mark Elliott

14 Threading What is it? Identifies and arranges s that were part of a single thread or conversation. What is it used for? Allows you to: Easily see the order of each in a thread. See which s are inclusive (i.e. have unique content). Identify duplicate spares (i.e. s with the same content). How will it help me? Sort and organize s by thread for more intuitive review. Saves time if only reviewing the non-duplicative inclusive s.

15 Near Duplicate

16 Can you spot the difference? Version A Version B

17 Textual Near Duplicate Identification What is it? Identifies documents with highly similar text and places them into relational groups. What is it used for? Allows you to: Use near dupe groups in searching or filtering. Conflict check coding decisions amongst near dupes prior to production. How will it help me? Saves time by identifying very similar documents prior to the start of review. You can also use the near dupe groups for review and QC.

18 Best Practices and Considerations Run instead of or in place of threading Use with Compare function Not meant to eliminate items but as prioritization and grouping Include Numbers? Use for QC, comparison of datasets

19 Conceptual Analytics

20 What is Conceptual Analytics? Relativity Analytics indexes use a mathematical approach to indexing documents. Terminology is understood based on language usage in your documents. No outside word lists Dictionaries, thesauri, ontologies, etc. Language-agnostic Term co-occurrence, not term location

21 Analytics Index: Best Practices and Considerations Minimum text (>0 KB) Maximum text (<30,000 KB) Repeated Content Identification Run repeated content identification on a 100k document random sample for large cases Default settings best for small cases Change minimum number of occurrences to 0.5% or 0.4% of number of documents being analyzed Repeated Content Filter Review filters before adding to analytics profile

22 Keyword Expansion

23 Keyword Expansion What is it? Uses the concept space to allow users to submit terms and returns conceptually related words What is it used for? Investigating the language of the workspace using known keywords How will it help me? Allows you to find code words Assists in expanding the keyword list Familiarize yourself with the language of the case. Also useful to determine the quality of the index!

24 Best Practices and Considerations Concept or term submission Copy to dtsearch

25 Clustering

26 keyterm Custodian #1 Custodian #2 filetype Custodian #3 date range Custodian #4

27 Cluster Browser

28 Heat Maps Show You Where Your Data Lives FIND YOUR COUNTY Choose a state KEY Unemployment Rate More than 13% % 7-9.9% 0-6.9%

29 Heat map in Cluster Visualization Heat Map 5 Workflows to enhance review with cluster visualization

30 Clustering What is it? Use the power of the conceptual index to identify groups of conceptually related documents. What is it used for? This can be used as a tool for investigation, analysis, review, or QC. How will it help me? Investigate a large unknown dataset Cull out non-relevant documents quickly Speed up a linear review by batching conceptually related documents together

31 Best Practices and Considerations Cluster sub group Cluster all documents Batch by cluster QC with Clusters

32 Real World Challenges

33 One More Thing

34 Mobile Beta Program New ipad app separate from Relativity Binders Mobile-friendly design for saved searches, views, document lists, and coding layouts as they exist in Relativity All coding on mobile is synchronized to and viewable in Relativity

35 Join the Beta! Experience the new app before it becomes publicly available. Explore new features added every month. Guide the development of the product a direct-line to Product Management. MobileBeta@kcura.com to participate.

36 In the Future Administer Relativity from your smartphone Case strategy and construction Reporting dashboards Support for additional devices and operating systems

37 Thanks, DC!