San Diego User Group June 28, 2016

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1 San Diego User Group June 28, 2016 Analytics What is it and how can I use it?

2 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 out!! 2015 kcura. All rights reserved.

3 Where is Analytics used? Google Analytics Netflix Twitter Pandora itunes Genius Intelligence agencies High frequency trading National security

4 Relativity Analytics Adoption

5 246 Customers with Analytics Subscriptions

6 Current RA Subscription Mix by Client Type Corporation Government Law Firm Litigation Service Provider Subscription No Subscription

7 over 1.6 Billion documents analyzed

8 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10 May-10 Jul-10 Sep-10 Nov-10 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 Sep-13 Nov-13 Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jul-15 Sep-15 Nov-15 Jan-16 Mar-16 Cumulative GBs Indexed through Analytics 1,200,000 1,100,000 1,000, , , , , , , , , , ,125,561

9 Analytics Average Monthly GB s Indexed By Year 60,000 56,609 50,000 40,000 35,924 30,000 20,000 10, ,532 12,342 7,393 1,033 3,

10 Trend: Using Technology to the Fullest Sedona Principles: Number 11 use of electronic tools and processes, such as data sampling, searching, or the use of selection criteria, to identify data reasonably likely to contain relevant information. Commentary to Principle 11 - possible to use technology to search for concepts, which can be based on ontologies, taxonomies, or data clustering approaches, for example kcura. All rights reserved.

11 Trend: Openness and Transparency Sedona Conference Cooperation Proclamation: Leading jurists, trial attorneys, corporate counsel, government lawyers, and others are signing onto The Cooperation Proclamation. By doing so, they are pledging to Reverse the legal culture of adversarial discovery that is driving up costs and delaying justice Help create toolkits of model case management techniques and resources for the Bench, inside counsel, and outside counsel to facilitate proportionality and cooperation in discovery Help create a network of trained electronic discovery mediators available to parties in state and federal courts nationwide, regardless of technical sophistication, financial resources, or the size of the matter kcura. All rights reserved.

12 Articulating the Value of Relativity Analytics

13 Analytics toolbox Conceptual Structured Categorization Clusters Keyword expansion Concept searching Find Similar threading Near duplication Language identification Repeated Content Identifier

14 Articulating Value Use Cases Audiences identify with use cases over features More memorable for your internal client More natural method of selling

15 Use Case Features Use Case Narrowing the review set Feature threading Foreign language identification

16 Use Case Features Use Case Narrowing the review set Quality control Feature Near duplicate detection Cluster visualization

17 Use Case Features Use Case Narrowing the review set Quality Control Investigation Feature Keyword expansion

18 Use Case Features Use Case Narrowing the review set Investigation Quality control Organizing large sets of data Feature Clustering Categorization

19 Common Objections

20 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 a majority of cases. For example: Batching - Reviewing conceptually related documents increases review speed Production prep Analytics can help to avoid mistakenly producing privileged docs Keyword sampling Address keyword issues to help identify other potentially relevant documents Threading Only review inclusives and reduce the volume of

21 Analytics is too expensive threading will save clients money on almost every case Other workflows that can help save your client money: Batching by Clusters Categorization Relativity Assisted Review Confidential - kcura 2015

22 Analytics is not defensible Da Silva Moore, et al. v. Publicis Groupe, No. 11 Civ (ALC)(AJP), 2012 WL (S.D.N.Y. Feb. 24, 2012). First court to explicitly approve the use of TAR in e-discovery What the Bar should take away from this Opinion is that computer-assisted review is an available tool and should be seriously considered for use in large-data-volume cases where it may save the producing party (or both parties) significant amounts of legal fees in document review. Counsel no longer have to worry about being the first or guinea pig for judicial acceptance of computer-assisted review. Confidential - kcura 2015

23 Analytics is not defensible Dynamo Holdings Ltd. P ship v. Comm r of Internal Revenue, Nos , (T.C. Sept. 17, 2014). Tax court petitioner sought permission to use predictive coding First time the tax court sanctioned the use of TAR Court recognized that it is widely accepted and does not cause an undue burden Confidential - kcura 2015

24 Analytics is not defensible Bridgestone Americas, Inc. v. Int. Bus. Machs. Corp., No. 3: (M.D. Tenn. July 22, 2014). Defendant objected to use of predictive coding because it would change the original case management order Screening of search terms already completed Rule 26 requires discovery be tailored by the court to be as efficient and cost effective as possible. Magistrate allowed plaintiff to switch horses in midstream Openness and transparency are critical and expected Confidential - kcura 2015

25 Analytics is not defensible Progressive Cas. Ins. Co. v. Delaney, No. 11 CV 00678, 2014 WL (D. Nev. July 18, 2014). Production to the FDIC by Progressive. Keyword culling was done to narrow population from 2 million to 565,000 documents that hit on key words. Progressive used TAR without defendant s agreement and without leave of court. Court rejected the use of TAR because Progressive failed to inform. No discovery about discovery exceeds the scope of Rule 26 a reasonable search is required Progressive ordered to produce all 565,000, but permitted to apply a privilege filter Confidential - kcura 2015

26 Analytics in the legal spectrum Sedona Principles Number 11 use of electronic tools and processes, such as data sampling, searching, or the use of selection criteria, to identify data reasonably likely to contain relevant information. Commentary to Principle 11 - possible to use technology to search for concepts, which can be based on ontologies, taxonomies, or data clustering approaches, for example.

27 Analytics Every Day Uses Finisar Corporation v. Nistica, Inc., No. 13-CV (N.D. Cal. Dec. 12, 2014). Model order for e-discovery searches were required on specific custodians Order that all be produced. Analytics allows review of all , but efficiently

28 Analytics Every Day Uses New Mexico State Inv. Council v. Bland, D-101-CV , 2014 WL (N.M. 1st Dist. Feb. 12, 2014). Intervening plaintiffs objected to the settlement Law firm conducted document review using predictive coding, concept grouping, near-duplication detection and threading Used advanced linear review...utilized advanced machine learning tools that enabled the reviewers on the document analysis teams to work more efficiently with the documents and identify potentially relevant information with greater accuracy than standard linear review, resulting in a prompt settlement with four defendants.

29 Structured Analytics

30 Threading

31 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

32 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.

33

34 Near Duplicate

35 Can you spot the difference? Version A Version B

36 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.

37 Language Identification

38 Language Identification What is it? Determines a document s primary language and up to 2 secondary languages. What is it used for? Allows you to see how many languages are present in your collection, and the percentages of each language by document. How will it help me? Easily filters documents by language and batch out files to native speakers for review. Determines if translation is needed.

39 Conceptual Analytics

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

41 Document Preprocessing The legal department of a large financial services company a long-standing client of kcura reached out to the company for help with a seemingly straightforward project: e-discovery related to an ongoing investigation. The federal government was demanding documents be turned over quickly, meaning the client had a short time frame to produce relevant information. The client initially identified 100,000 documents belonging to three custodians, and a team of contract attorneys set to work. After reviewers started coding the original data set, however, the government agency s lawyers requested documents from a fourth custodian, who they believed was crucial to the case. Nearly all of the original 100,000 documents had been reviewed, and suddenly a fourth custodian shows up with 200,000 documents, said Adam. Bringing in this single custodian tripled the size of the case. kcura needed to develop a plan of attack for their client, and there was no time to waste. A tight schedule for 100,000 documents now had to accommodate 300,000. Given those numbers, kcura and their client quickly determined that Relativity Assisted Review would be the best fit.

42 Document Preprocessing The legal department of a large financial services company a long-standing client of kcura reached out to the company for help with a seemingly straightforward project: e-discovery related to an ongoing investigation. The federal government was demanding documents be turned over quickly, meaning the client had a short time frame to produce relevant information. The client initially identified 100,000 documents belonging to three custodians, and a team of contract attorneys set to work. After reviewers started coding the original data set, however, the government agency s lawyers requested documents from a fourth custodian, who they believed was crucial to the case. Nearly all of the original 100,000 documents had been reviewed, and suddenly a fourth custodian shows up with 200,000 documents, said Adam. Bringing in this single custodian tripled the size of the case. kcura needed to develop a plan of attack for their client, and there was no time to waste. A tight schedule for 100,000 documents now had to accommodate 300,000. Given those numbers, kcura and their client quickly determined that Relativity Assisted Review would be the best fit. legal department relevant information client initially identified custodian tripled size case kcura needed develop plan attack client waste tight schedule documents accommodate kcura client quickly determined relativity assisted review best fit documents belonging custodians team contract attorneys set work reviewers started coding original data large financial services company company help seemingly straightforward project e-discovery related ongoing investigation federal government demanding documents turned quickly meaning client short frame produce long-standing set government agency s lawyers requested documents fourth custodian believed crucial case original documents reviewed suddenly fourth custodian shows documents adam bringing single client kcura reached

43 Value of Concept Search Avoids term mismatch issues Pop vs. soda Football vs. soccer Avoids intentionally confusing use of language Code words Finds documents even if exact language differs Misspellings Synonyms

44 Keyword Expansion

45

46 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.

47 Concept Searching

48 Example of a Good Concept Search On January 2, 1997, Mr. Allchin wrote concerning "IE and Windows" that Microsoft needed to begin "leveraging Windows from a marketing perspective" if it was to defeat Netscape. Allchin complained that without leveraging Windows from a marketing standpoint: "We do not use our strength which is that we have an installed base of Windows and we have a strong OEM shipment channel for Windows." Allchin emphasized: "I am convinced we have to use Windows this is the one thing they don't have.... We have to be competitive with features, but we need something more Windows integration. If you agree that Windows is a huge asset, then it follows quickly that we are not investing sufficiently in finding ways to tie IE and Windows together." Using Microsoft's code name, Memphis, for the next version of Windows, Allchin concluded that, "Memphis must be a simple upgrade, but most importantly it must be a killer on OEM shipments so that Netscape never gets a chance on these systems." (MS ) Source: Complaint: U.S. v. Microsoft Corp., 2007

49 Example of a Good Concept Search This is the relevant portion to use for the search

50 Clustering

51 Custodian Name Custodian Name Custodian Name Custodian Name

52 Cluster Browser

53 What is Cluster Visualization? A component of Relativity Analytics that displays your data as a map of conceptually similar documents, allowing you to: Overlay metadata and coding information on top of clusters to uncover patterns and trends in your data Work faster by getting you to the most important documents first Identify potential coding inconsistencies Investigate and explore large, new data sets View relationships between groups of documents*

54 The main components of Cluster Visualization. The Stuff You Need to Know

55 Cluster Visualization Main Components Circle Pack Visualization Nearby Cluster Visualization Filters Heat Map Document List

56 Circle Pack Visualization

57

58 Edit this footer with presentation name

59 Circle Pack - Each Top Level Cluster Visually Represented

60 Circle Pack - Parent and Child Hierarchy Displayed

61 Circle Pack - Zoom in on Sub-clusters

62 Circle Pack - Drill Down Another Level

63 5 Workflows to enhance review with cluster visualization Nearby Cluster Visualization

64 5 Workflows to enhance review with cluster visualization

65 5 Workflows to enhance review with cluster visualization

66 Building the Nearby Clusters View CIRCLE PACK VIEW NEARBY CLUSTERS VIEW

67 Filters Use filters to select the data you want to display in the Heat Map

68 What s a heat map?

69 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%

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

71 Document List Document List 5 Workflows to enhance review with cluster visualization

72 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

73 Real World Challenges

74 Challenge #1 You have a discovery deadline quickly approaching. Challenge You were on #1 target for your deadline until you were just dropped with 100 GB of data to review. How will you get through this data in time for your deadline?

75 Challenge #1 Solution You have a discovery deadline quickly approaching. You were on target for your deadline until you were just dropped with 100 GB of data to review. How will you get through this data in time for your deadline? 1. Use your existing coded documents as examples in a categorization set. 2. Create a search to find documents categorized as Responsive with a rank higher than Batch these out to second level review. 4. Create a search to find documents categorized as Not Responsive and rank greater than 80, or where they are not categorized. 5. Batch these out to first level review. The key is prioritization.

76 Challenge #2 Your attorney received 5 paragraphs from a subject matter expert depicting potential conversations Challenge among three #1people that corporate counsel believes to be important. How will you find these types of conversations between these three custodians?

77 Challenge #2 Solution Your attorney received 5 paragraphs from a subject matter expert depicting potential conversations among three people that corporate counsel believes to be important. How will you find these types of conversations between these three custodians? 1. Create a search to find these three custodians documents. 2. Create a categorization set. 3. Add each paragraph as an example, using Text Excerpts. 4. Run categorization against the documents in Search #1.

78 Challenge #3 You need to QC your production to make sure Challenge #1 no privileged documents go out the door. How will you speed up this process to be as efficient as possible?

79 Challenge #3 Solution You need to QC your production to make sure no privileged documents go out the door. How will you speed up this process to be as efficient as possible? 1. Run Textual Near Duplicate Identification against all documents. 2. Run a search using metadata fields to find privileged documents. 3. Include Textual near duplicates on the search and filter down to find inconsistencies.

80 Challenge #4 You ve already coded your own documents, and you just Challenge received a production #1 from the opposing counsel. You ve been data dumped! How will you find the relevant documents that you need?

81 Challenge #4 Solution (Option A) You ve already coded your own documents, and you just received a production from the opposing counsel. You ve been data dumped! How will you find the relevant documents that you need? Option A: 1. Cluster the received production data. 2. Evaluate the clusters to see if there is any junk that can be quickly eliminated from review.

82 Challenge #4 Solution (Option B) You ve already coded your own documents, and you just received a production from the opposing counsel. You ve been data dumped! How will you find the relevant documents that you need? Option B: 1. Use your existing documents as examples in a new categorization set. a. Designation, Issues, Privilege, Hot Docs, etc. b. Use the production as the Documents to be Categorized search 2. Cluster the received production documents. 3. Use Pivot and compare each cluster with the categorized issues.

83 Scenario 1 - What would you do? During an ongoing insurance matter, you receive an opposing production on a Friday afternoon. The documents must be reviewed by Monday. How can you meet the aggressive deadline over the weekend?

84 Scenario 2 - What would you do? You received paragraphs from a subject matter expert depicting potential conversations between 3 people that corporate counsel believe to be important. How will you find these types of conversations between these 3 custodians? How will you set up the review once you find them?

85 Scenario 3 - What would you do? You need to come up with a good list of privilege terms in order to do a proper screen and to set up a highlight set.

86 Scenario 4 - What would you do? Your document set contains unknown but relevant terms. You have identified some but know more exist. What can you do to organize the case data more accurately and improve the effectiveness of the document review?

87 Scenario 5 - What would you do? You ve just loaded your documents and prior to formal review, a senior attorney wants to explore a bit and see what you have (or what you ve been provided by the other side)

88 Scenario 6 - What would you do? Opposition is claiming that your production consists of mostly junk responsive documents. What can you do to a) ensure that you found all the smoking guns, and b) how do you present your findings defensibly?

89

90 Join us in Chicago for our annual user conference, Relativity Fest. This year s show will be our biggest and best one yet. If you ve never attended before, here s everything you need to know: WHAT IS IT? kcura s annual user conference for Relativity, featuring four full days of educational content and unique networking opportunities now in its seventh year. WHO ATTENDS? More than 1,000 Relativity users from service providers, law firms, corporations, and government agencies, along with e-discovery thought leaders and the kcura team. WHY SHOULD I GO? It s a great opportunity to network with the Relativity community, meet analysts and judges, hear about e-discovery trends from industry leaders, and chat with the kcura team all while learning how to make the most out of your Relativity experience and absorbing real-world takeaways you can implement as soon as you get home. Many organizations send several members of their team to learn together and share ideas in real time. HOW DO I SIGN UP? Visit relativityfest.com/register.

91 Questions?

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