Best Practices to Avoid Missing Key Evidence in Large Document Reviews

Size: px

Start display at page:

Download "Best Practices to Avoid Missing Key Evidence in Large Document Reviews"

Jesse Andrews
6 years ago
Views:

1 Best Practices to Avoid Missing Key Evidence in Large Document Reviews How Proper or Improper Search, Processing and Indexing can Make or Break Your Case Erin Derby, ACEDS Lexbe

2 ediscovery Webinar Series About Lexbe We are an Austin, TX based ediscovery software and services provider, specializing in serving small & medium-sized law firms and organizations. We provide: Cloud-based DIY ediscovery processing & document review software High-speed ESI document processing and data conversion services Experienced ediscovery specialists and expert consultants Cost-effective ediscovery Secure, easy-to-use and a great review tool for consideration A powerful litigation document management service Lexbe Sales sales@lexbe.com (800) x22

3 ediscovery Webinar Series About our Webinars Webinars take place monthly and cover a variety of relevant e-discovery topics If you have technical issues or questions, please webinars@lexbe.com Lexbe webinars are available for viewing (streaming video), and downloadable as a PDF Presentation or an MP3 podcast. This Webinar and a complete listing of other ondemand webinars is part of the: Lexbe ediscovery Webinar Series For notices of future live and on-demand webinars as part of this series please us at webinars@lexbe.com or: Follow us on LinkedIN

ediscovery Webinar Series Erin Derby bio ediscovery Specialist at Lexbe, specializing in working with clients handling ediscovery in complex litigation, requiring a high level of precision and

4 ediscovery Webinar Series Erin Derby bio ediscovery Specialist at Lexbe, specializing in working with clients handling ediscovery in complex litigation, requiring a high level of precision and expertise. Certified by the Association of Certified E-Discovery Specialists (ACEDS). Erin is also a Litigation Paralegal with 10 years experience with both plaintiff and defense law firms in a variety of practice areas. Erin Derby (512) ederby@lexbe.com

5 Avoid Missing Key Evidence in Large Document Reviews Agenda Overview Modern Search and ediscovery Indexing Technologies Basic and Advanced Search Options in Use Today Search Indexing 'Gotchas' Pitfalls of Relying Solely on Image-Based OCR Indexing Why Native Extraction Alone is Ineffective Complexities of Working with Foreign Language & Translated Text Optimal Search with a Concatenated Indexing Approach Takeaways

6 Best Practices: ediscovery Search Use of Keyword Search in Discovery Early Stage Culling - Reduce amount of ESI for review by using keywords, date ranges, custodians to cull document collections. Keyword-Based Responsive and Privilege Review Construct search queries to return documents that are likely to be responsive and/or confidential. ID Documents for Depo Prep - Find and assign key documents related to specific case participants to prepare for depositions. ID of Key Docs for Trial - Find and mark key case documents. Code documents that will be needed for trial.

7 Best Practices: ediscovery Search Construct Quality Searches Start with Request for Production or Subpoena - Translate the demands of the RFP/SDT into a keyword search strategy. Interview Custodians - Ask key case participants/data custodians about their ESI. Use their insights and their terminology to find obscure key documents. Include Jargon - Seek out industry or company sub-culture specific terms and abbreviation/acronyms you may not be familiar with. Included Misspellings - Include misspelled versions of keywords or (use fuzzy search settings or Boolean limiters) in your search string to account for s or other words with typos.

8 Best Practices: ediscovery Search Pros of Keyword Searching Fast - Keyword search is very fast compared with other document search methodologies. Inexpensive - Good results are obtained at little cost compared with manual review or other computer-assisted methodologies. Quality - Search can deliver high quality results, particularly if keyword terms are carefully developed and tested. Avoids Manual Review Errors/Inconsistencies - Search results are computer generated and avoid known human review errors that can result from fatigue and inadequate training or lack of focus.

9 Best Practices: ediscovery Search Cons of Keyword Searching Search Can be Over or Under-Inclusive - Search terms can bring back too many junk results or miss good results. These are known as false positives and false negatives. Difficulty of Creating Good Search Terms - Constructing good search terms takes design time, testing, iterations and analysis. Non-Searchable Text - Search results are only as good as the underlying searchable text. ESI collections and review tools can miss text that a human reviewer might catch for a variety of reasons. Some file types cannot be indexed - There is little consistency in what files can be indexed across databases.

10 Advanced ediscovery Search Using Search Limiters Search Limiters Reduce False Positives (Noise) Filter Out Unneeded File Types. Some file types are unlikely to lead to useful information and can be excluded. Use Boolean Modifiers to Limit Overly Expansive Searches. Boolean modifiers can reduce the number of documents returned from a query while increasing the relevance of those files. Exclude certain words or combinations and specify word order.

11 Advanced ediscovery Search Using Search Expanders Basic Boolean Operators: AND: returns results including both terms OR : looking for at least one of a list of terms NOT : exclude terms you don t want ( ) : can be used to separate OR statements from the rest of the Boolean string PRE/n : First search term does not precede the second term by more than n words Wildcard Characters: * replaces a letter in your search term,! allows for stemming search within a Boolean query

12 Advanced ediscovery Search Finding Personally Identifiable Information Search for PII Social Security Numbers Search Options Use Boolean option and wildcard '==' as a sequence of 09 digits: 1- Wildcard "=== == ====" (spaces) 2- Wildcard "=========" (w/ spaces) 3- Wildcard "===-==-====" (dashes) 4- Search "SSN" 5- Search "Social Security Number" The same logic can be used for credit card numbers, birthdates, phone numbers, or any other PII needing to be redacted from documents prior to production or Court filings.

13 Advanced ediscovery Search Finding Personally Identifiable Information Search for PII Phone Numbers === === ====

14 Advanced ediscovery Search Test Keyword Searching Results Look at Results Returned. Searching without review and testing results in low quality results. Sample and Look for Ways to Limit Search. Create new queries that reduce false positives. More new keywords. Viewing search results prompt the discovery of additional keywords that could be used to expand or reduce search queries. Fuzzy and Concept Search. Find new keywords by searching and returning synonyms and near identical words. Keyword searching becomes an iterative process.

15 Advanced ediscovery Search Ways to Improve Searchability Understand Data Collection. Understand composition of collection, file types, special issues. Not everything is ready to be indexed/searched. Some data will need processing. Understand Software Used for Processing & Search - Indexing tools create database entries for files by opening, scanning, and storing specific content in fields, often based on the data s location within the document, e.g. header data fields and body content fields. If indexing tools can t open a file or see specific forms of data within the file, it will not be able to index it and the data will not be searchable.

16 Search and ediscovery Indexing Technologies What happens when you load documents to a processing &/or review platform attachment extraction & parent association Time Zone Offset for s Metadata extraction & fielding Custodian Assignment MD5 hash code generation Native text extraction System file identification & DeNIST OCR of images Indexing of extracted & OCRed text Archive/Container Expansion File Repair Deduplication

17 Search and ediscovery Indexing Technologies What is an index and why is it important? The purpose of storing to an index is to optimize speed and performance in finding relevant documents for a search query. Without an index, the search engine would scan every document in the corpus, which would require considerable time and computing power. Modern ediscovery engines are able to parse out noise or stop words, which greatly increases the search speed.

Search and ediscovery Indexing Technologies OCR vs Text-based How OCR Works An OCR index receives each document as though it were a scanned image.

18 Search and ediscovery Indexing Technologies OCR vs Text-based How OCR Works An OCR index receives each document as though it were a scanned image. The image is run through a virtual print driver and inspected for alphanumeric text. Any identified text is then lifted and indexed, making it searchable in your review platform. OCR Software distinguishes light and dark patterns to determine characters that match alphanumeric symbols. Not necessarily 100% accurate. Depends on the quality of the image, layout, fonts, spacing, etc. Advanced search practices can help mitigate accuracy issues.

19 Search and ediscovery Indexing Technologies OCR vs Text-based Optical Character Recognition (OCR) Imaged Based: Search results are generated from the image of documents. Native files ( , attachments, spreadsheets, etc.) are converted to a paginated image file and then OCR is applied to make the text searchable. (ex. TIFF production with no extracted text). Conversion software uses a print-driver approach to virtually image what would have been physically printed Data that could be missed - Headers/footers/notes, comments and revisions, highlighted text, hidden sheets or text, print selections, applied filters

20 Search and ediscovery Indexing Technologies OCR vs Text-based Text-based Index Text is extracted from a native document Any text typed into the document is indexed- this includes; hidden fields, comments, hidden sheets, BCC fields, etc. Text would have to be typed into the document(s) not appear on an embedded image (a picture of a Stop sign would not add the word Stop to the index) Data that could be missed - Non-text files (ex. scanned documents) and embedded text, objects, or visuals will not be indexed. Different native extraction methods can also vary in their ability to recognize certain types of text.

21 Search Indexing Gotchas OCR Index vs Text-based Index OCR Index Pros: Embedded Images Charts Budgets Scanned Documents Cons: Hidden Fields Excel hidden cells Comments Tracked Changes BCC Field Text-Based Index Pros: Hidden Fields Excel Comments Tracked Changes BCC Field Cons: Embedded Images Charts Budgets Scanned Documents These indexes are each powerful, but the strength of one is the weakness of the other. Which do you choose?

22 OCR Index vs Text-based Index Hidden Sheets- OCR Con / Text-based Pro SEARCH Joe Connor Searching Joe Connor would not return this document as a hit if searching in a typical OCR index.

23 OCR Index vs Text-based Index Hidden Sheets- OCR Con / Text-based Pro The native document shows the hidden sheet where Joe Connor appears

based index does yield that same document as a hit.

24 OCR Index vs Text-based Index Hidden Sheets- OCR Con / Text-based Pro Searching Joe Connor in a Text based index does yield that same document as a hit. OCR Indexes miss hidden fields/ pages like this one.

Text Based Index All of the text that appears in the PowerPoint

25 OCR Index vs Text-based Index Embedded Images- Text-based Con / OCR Pro SEARCH NW Alum Smelters PowerPoint Slide with Embedded Image Text Based Index All of the text that appears in the PowerPoint slide is on an embeded image and so is not readable by a Text-based index.

26 OCR Index vs Text-based Index Embedded Images- Text-based Con / OCR Pro The OCR index is able to lift the text off the document. Above we see the hit returned when NW Alum Smelters is searched in an OCR Index. Extracted Text-based indexes miss embedded or attached images.

27 Metadata Index Invisible fields with important information Metadata is field-level file information used in review and often delivered with a production as part of a load file or retrieved from Native Files. Metadata is information about your data and can produce very valuable evidence. Metadata Field Name Use in Review DocOriginalTitle The original title of a document Date/Time Sent, Date/Time Received Show when s sent and received Sender, Recipients, CC and Bcc Show who sent and received s Date Last Modified Usually best date field for files collected normally (without a forensic collection) File Extension / File Type Show type and quantity of ESI produced

28 Foreign Language and Translated Text Complexities of Translating Documents Choices to make when you have foreign language documents: Machine Translation (not perfect, but more affordable) Human Translation (can be very good, but expensive) Hire foreign language staff for search (can be very good, but expensive) Create foreign language search terms and have only responsive documents translated (not perfect, but more affordable) You find yourself asking: What might I miss? vs How much money am I wasting Further complicating the matter: Tying translated documents to original for production often has to be done manually.

29 Concatenated Index Combines 2 or more Indexes Blends the OCR and Text indexes into a single searchable database Regardless of whether the text appears in a hidden field (Text based) or an embedded image (OCR based) your document will be responsive to search Lexbe s uber index seamlessly blends the OCR and Text-based Index. Additionally includes the translation and metadata indexes into its Uber Index Apply advanced Boolean search to pull text from either OCR or Text-based and get better results Translated documents can be searched in either language and all of the search expanders can be applied (fuzzy, concept, etc.)

30 Concatenated Index Blending all available Indexes Having only one index would miss information on a document like this. A concatenated index would pull all of the text, on the embedded image and in the comments, into a single searchable database.

31 Uber Index Lexbe Document Viewer All of these tabs allow you to easily toggle between different views of the document

32 Uber Index Multi-Language Search Results A Horse is searched, this document is identified as a hit with caballo being identified as a translated term.

33 Uber Index Translation Tab The Lexbe Document viewer shows both the translated and original version in the doc viewer for easily toggling.

34 Evaluating your Platform Index Considerations Many platforms offer a single index, OCR or Text What might you miss? Some platforms offer both, but you have to search separately More time spent searching Metadata can contain crucial evidence Ensure that your platform receives and indexes metadata in a way that is searchable Foreign language documents add another level of complexity Does the platform offer auto language detect? Will the platform pull the original and translated version together?

35 Avoid Missing Key Evidence in Large Document Reviews Summary The quality of your search index can directly affect the outcome of your case Foreign language documents can add a level of complexity, especially if your platform is not set up to receive translated documents properly The Lexbe Uber Index helps ensure that you do not miss any evidence by capturing all text, embedded on an image (OCR) or typed (Text-based), as well as pulling in metadata and translated text into a single searchable index.

36 Uber Index Lexbe s Concatenated Index Four Critical Data Sources Feed the Lexbe Uber IndexSM Resulting in the Industry s Most Complete Index and Search Platform.

37 Thank You Thank You For Attending We ll be making the following available to webinar attendees: A recorded streaming version MP3 podcast PDF Please let us know if you have any questions or comments about this webinar or suggestions for future topics. This webinar is part of the Lexbe ediscovery Webinar Series. For notices of future live and on-demand webinars as part of this series please us at webinars@lexbe.com or Follow us on LinkedIN. Presenter: Erin Derby ederby@lexbe.com (512) Moderator: Frank Krafka fkrafka@lexbe.com (512) Webinar Questions: webinars@lexbe.com

A Lawyer s Guide to ediscovery Processing

A Lawyer s Guide to ediscovery Processing What You Should Know to Competently Handle Your Case Christian DeTrude Lexbe ediscovery Webinar Series About our Webinars If you have technical issues or questions,