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Automated Process of Electronic Discovery October 19, 2009. Coding & Scanning. Document Acquisition. 95% Settle. Review. Depositions. Complaint. Discovery Begins. Discovery Closes. Trial. Photocopy. Produce & Share. Electronic Discovery. Electronic Discovery Legal Issues.

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Automated process of electronic discovery october 19 2009
Automated Processof Electronic DiscoveryOctober 19, 2009

Coding &




95% Settle










Produce &


Electronic Discovery

Electronic discovery legal issues
Electronic Discovery Legal Issues

  • Chain of Custody/Data Integrity

    • “Chain of Custody”

      • Requires that “the one who offers real evidence…must account for the custody of the evidence from the moment in which it reaches his custody until the moment in which it is offered in evidence.” Black’s Law Dictionary, page 156 (6th ed. Abr. 1991)

    • Inexpert handling of electronic media (e.g., open, print, & scan) has serious drawbacks

      • Human error

      • Missing data or inadvertent changes

      • Time to produce

      • No detailed audits

Electronic discovery legal issues1
Electronic Discovery Legal Issues

  • Electronic Marginalia

    • Simple spreadsheets and word processing files contain an array of formatting elements including:

      • comments, headers, hidden rows/columns

    • Counsel should proactively ensure the process used provides at a minimum:

      • hidden rows and columns uncovered

      • comments exposed and converted

      • passwords broken

      • blank pages eliminated

Electronic discovery terms



Tape Restoration

Text Extraction



Data Culling

Electronic Discovery Terms

Electronic discovery process




Receive Data




Electronic Discovery Process

1 receive data

Identify locations of all data and prescribe systematic uniform collection of data

Media is sent in many formats




DAT Tape

Media is signed in and a strict chain of custody process begins

1 - Receive Data

2 index data
2 - Index Data uniform collection of data

  • Extract

  • Unzip

  • Index

  • Copy

  • Rename (uniform fashion – while maintaining data integrity)

  • Capture valuable info. (metadata)

  • Each file is examined to detect any changes to file extension – possible smoking gun/file

    • another reason why you cannot “just print them”

3 reduce the data set
3 - Reduce the Data Set uniform collection of data

  • De-duplication option

    • Our process ensures accuracy and integrity

      • MD5 Hash – “bit” level count

      • Bit Level most accurate!!

  • Filtering Data

    • Narrow by a specific “date range”

    • Uses metadata to eliminate files outside of the discoverable date range

4 keyword searching
4 - Keyword Searching uniform collection of data

  • Select keywords or phrases to narrow your search/discovery

  • Advanced searching using Boolean, proximity, etc.

  • Responsive files are flagged and continue through the process

  • Non-responsive files are still preserved

  • Saves Hours

  • Saves $s

5 convert the data

Full Text of files is extracted uniform collection of data

Hidden information is uncovered

rows, columns, changes (if enabled)

embedded comments exposed

“electronic marginalia”

Files converted to Tiff or PDF images

5 - Convert the Data

6 package the data
6 - Package the Data uniform collection of data

  • Batchload Application Begins

  • Images bundled and a customized load file is created for uploading to client document management system

    • e.g., Summation, Concordance, etc.

7 burn return
7 - Burn & Return uniform collection of data

  • Final (of several) quality checks performed

  • CDs Burned

  • Data Integrity still intact

  • CDs are shipped to client

  • Data remains on system

Key considerations

Automation = Integrity & Speed uniform collection of data

Provides Data Integrity – Chain of Custody – Cannot “Just Print Them Out”

Allows De-duping, Filtering, & Searching to Reduce Data Set

Uncovers Hidden & Meaningful Data

Examines all files for hidden file types

Hidden Rows/Columns Uncovered

Comments are Exposed

Metadata Uncovered & Searchable

Electronic Marginalia

Key Considerations

Discovery Pipeline show the legacy of each document.

The information starts by grouping in the Case Container list.

Case Documents are organized in Case Load Volumes.

Actual document history is tracked from initial collection to final evidence production.

Doc. details are linked.

Document review progress & status reports uniform collection of data

Each matter is given reports on its own home page.

Brief summary of document review status. “Executive Summary” overview.

Forecasting project completion dates and project progress are shown in %’s

Graphs are used to provide a visual aid to see your project’s “Big Picture” status.

  • Equivio Near-Duplication uniform collection of data

    • Reduce document review time by 15% to 20% - directly impacting the bottom line costs

Less Time

  • Near-Duping – Step 2

  • Assign near-dupe sets for coherent review to reviewers

  • Reviewers prioritize and review only the differences

  • Apply coding to entire near-dupe sets where appropriate

  • Near-Duping – Step 1

  • Group the near-duplicates

  • Identify the differences among the near-duplicates

  • The Problem:

  • No clear method to organize and allocate documents across reviewers

  • Documents are reviewed multiple times by different reviewers

  • High risk of different coding among similar documents

Less Errors

Less Cost

  • Equivio eMail Threads uniform collection of data

    • Reduce eMail review time by up to 70% - directly impacting the bottom line costs

Less Time

  • eMail Threads – Step 2

  • Build tree structure

  • Identify missing links

  • Suppress duplicates

  • Focus on inclusives

  • eMail Threads – Step 1

  • Group into eMail sets

  • The Problem:

  • No clear method to identify eMail threads, originals, replies

  • eMails are reviewed multiple times

  • Extremely difficult to identify where missing eMails exist

  • High risk of different coding among similar documents

Less Errors

Less Cost

  • Equivio eMail Threads uniform collection of data

    • Review “conversation threads”, identifying missing links

    • Review only differences

doeDiscovery’s compare function allows you to sort and de-dup each document set for coding.

Choose your criteria for the compare.

Select the action you want to use from a drop-down list.

Using EquivioTM as the basis for the custom compare functions increases its power.

The Compare Features in doeDiscovery… Help you find the pertinent data faster!

Summation Enterprise Enhancements de-dup each document set for coding.


Search within database for potentially privileged documents using key terms

Documents that match have a field populated with the term that is found


Allows sets of docs, grouped by either similarity or parent/child relationship, to be coded in one pass….time savings up to 30%


Ability to save advanced searches & data “snapshots”

Expand search based on similarity or parent/child relationship

Verify consistency of coding among similar docs

Create review sets using Equisets

Enable Transaction Level audit capabilities


Pipeline reports to be able to see real time status of your review