Skeptics no more building your legal team's confidence in using ipro analytics
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Purpose of this session is to provide some context, mainstream references, and other things to help you get people on board and when to apply it. Don’t get discouraged – change is hard. Keep going. If you take this back to your firm and can show success, you can change the behavior of others naturally – they will want to be able to do things as fast as you.
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Skeptics no more building your legal team's confidence in using ipro analytics
- 1. Skeptics No More: Building Your Legal Team's Confidence in Using Ipro Analytics
- 2. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 It is in the whole process of meeting and solving problems that life has meaning. Problems are the cutting edge that distinguishes between success and failure. Problems call forth our courage and our wisdom; indeed, they create our courage and our wisdom. It is only because of problems that we grow mentally and spiritually. It is through the pain of confronting and resolving problems that we learn. - M. Scott Peck
- 3. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Why can’t we stick with what we know? Keywords are a good starting step but they have to be paired with another approach in order to be beneficial. •Size and amount of data •Time involved in finding intentional deception in documents Time involved in comparing produced documents to your reviewed documents •Changes to our current language and communication styles •Code words/names •Current shorthand, acronyms, text language, emoji’s
- 4. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 an·a·lyt·ics /ˌanəˈlidiks/ information resulting from the systematic analysis of data or statistics
- 5. Private and Confidential – Copyright 2019 Is this Analytics?
- 6. Private and Confidential – Copyright 2019 Is this Analytics? We call this searching.
- 7. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Is this Analytics?
- 8. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Is this Analytics? We call this reporting.
- 10. Private and Confidential – Copyright 2019 How accurate is your OCR?
- 11. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Start Simple Introduce features depending on the data set and goals – don’t start with all at the same time. Organize it into more efficient groups of documents
- 12. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019
- 13. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Near Duplicates!
- 14. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Near Duplicates •Create consistent results in coding •Looking for variations in document versions or added/removed text•Review •Ensure consistent coding on responsiveness and privilege callsQuality Check •Find original Natives for image produced documents Production Received •Identify best copy of a document for depositions, trial, and witness kitsTrial Prep
- 15. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 If I asked you to put these into groups, would you do it by hand or take it to a coin machine?
- 16. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 So why do it with this?
- 17. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Clustering •Quick insight into “What we have” •Test search term inclusion/quality prior to Meet & Confer •Easily Segregate Junk data Pre-Review •Organize and Prioritize Review of Document Topics/Significant WordsReview •Identify where your search terms might have missed relevant dataQuality Check •Weed through documents produced to find key data Production Received
- 18. Private and Confidential – Copyright 2019 Give me all your Dimes... Bob Mary
- 19. Private and Confidential – Copyright 2019 Give me all your Dimes... Bob Mary Categories!
- 20. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Categories • Leverage key issue documents or text to find similarReview • Use previously coded data to find similar within incoming productions Production Received • Prep for depositions - understanding all documents discussing similar topics • Locate best example document for a fact or issue Trial Prep
- 21. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 TAR ®® ®
- 22. Presentation TitlePrivate and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Private and Confidential – Copyright 2019 Teach Me Your Ways Good Song, Give me more like this Meh, Not Interested, take it out of my repertoire Responsive Document, find similar ones and tag them too Not Responsive, go find others and pull them out as well
- 23. Private and Confidential – Copyright 2019 Key Takeaways If applied correctly, it can only help Don’t apply all features at once Use terminology to inspire confidence and understandi ng To start, just organize it into more efficient groups of documents No additional cost for using these features
- 24. Thank you for joining us!
Notes de l'éditeur
- Purpose of this session is to provide some context, mainstream references, and other things to help you get people on board and when to apply it. Don’t get discouraged – change is hard. Keep going. If you take this back to your firm and can show success, you can change the behavior of others naturally – they will want to be able to do things as fast as you.
- Krista – AI panel gave us some good stats on keywords being ineffective.
- People fear things they don’t understand Do you find pushback when you suggest using analytics? what are the fears? decrease costs, Improve Review Speeds Save time to allow you to take on more clients Decrease labor reduce errors and increase consistency in document review Any new feature isnt going to cut your review time in half, maybe, but not likely.
- Everyone used to ask this. You don’t hear it anymore. Its not the norm. Every single project, but had to identify : its based on your type of images, etc. Now its mainstream because what other options do you have? If you don’t use it, you’re at a disadvantage.
- How do we make it more mainstream relevant and understandable
- No one likes to eat a handful of M&Ms, mixed with Skittles and Reese’s. It will throw your taste buds off their game by constantly changing flavors that don’t go together. Near Duplicate detection is going to take your documents regardless of filetype, and use the text to assign them a score. MM’s or skittles or reeses? Do you want to pick up each one/review each document to determine what it is? Or use the analytics engine to make the piles for you? Separate out the candy type – makes it a similar document Separate out the candy type and color – similarity score is even higher Candy type, color and flavor (peanut butter vs chocolate) similarity score is highest. Comparison will show you how they are different, and how they are the same.
- Near Duplicate detection is going to take your documents regardless of filetype, and use the text to assign them a score. MM’s or skittles or reeses? Do you want to pick up each one/review each document to determine what it is? Or use the analytics engine to make the piles for you? Separate out the candy type – makes it a similar document Separate out the candy type and color – similarity score is even higher Candy type, color and flavor (peanut butter vs chocolate) similarity score is highest. Comparison will show you how they are different, and how they are the same.
- How else have you used it? War stories?
- Multiple aspects of a coin make it unique and distinguishable, it could be grouped into several categories. For coins it looks at all these things: Size of Coin, Thickness, Color, edges, Heads Image, Tails Image Clustering doesn’t just look at words, it looks at concepts, and key phrases
- How else have you used it? War stories?
- If I tell you to give me all your dimes Who wants to be Bob, who wants to be Mary? Bob is reviewing every document one by one to determine if it is what he needs. Mary’s are already grouped, she has to match one example, and she gets back a bunch.
- If I tell you to give me all your dimes Who wants to be Bob, who wants to be Mary? Bob is reviewing every document one by one to determine if it is what he needs. Mary’s are already grouped, she has to match one example, and she gets back a bunch.
- Give recommendations based on your habits or input.
- The more you listen, the more you shop, the more shows you watch, the smarter it gets, giving better recommendations and learning your tastes and your habits.
- How do we make it more mainstream relevant and understandable