Searching for Bates numbers in Everlaw

Everlaw provides you a great deal of flexibility when you need to look up documents by a Bates number. For too many years, when being asked to locate documents that use a Bates number that is neither the PRODBEG or PRODEND number, I have had to set a search to cover a range of documents with a Bates number 100 - 1000 numbers less than and greater than the number cited in a report or other document. Everlaw's 'Page search' check box configures the search so that the document within which the number is used will be returned.

As is shown in this example, there's a helpful list of Bates prefixes to select from. (Why have I worked in so many Relativity databases lately which have split the production numbers across multiple metadata fields??). It's not necessary to use the correct number of leading zeroes. So here when we search for page 100 from the PRODTRAIN production, it returns the document with the beginning Bates number PRODTRAIN-000096 and the ending Bates number PRODTRAIN-000103.

If you click on the link for Advanced settings, you'll get a window in which you enter multiple Bates numbers, and here the 'Page Search' checkbox is available as well. Not that much different from running a search for multiple Bates numbers in Relativity, but here you have the option to upload a text file with multiple Bates numbers (each listed on a different line).

Everlaw also makes it possible to run a search through all entries in the email From, To, Cc, and Bcc fields in the Parties field, or all entries in the To, Cc, and Bcc fields in the Recipients field.

Note the lightning bolt icon next to these fields. This is an indication that multiple email metadata fields have been aggregated so that it's only necessary to run one search for each name.

Similarly, the 'All Text Fields' field allows for a search through all text metadata fields, and includes an option to search through document contents at the same time:

RAND Study on Electronic Discovery Costs

A RAND study, The Cost of Producing Electronic Documents in Civil Lawsuits, provides some insight into how much businesses are spending on electronic discovery.

It confirms that Review continues to be the most expensive stage of electronic discovery, and that most of the spending goes for work performed by outside counsel.

RAND concludes that the highest number of documents that a person can review in an hour is about 100, and it's not likely that this rate can be improved upon. Email threading; clustering; and near-duplicate detection will not necessarily allow for major improvements in review time.

The study also emphasizes the inconsistent decisions made by human reviewers. "In one case, for example, seven teams of attorneys, all trained in a similar manner and given the same instructions, examined 28,000 documents organized into 12,000 clusters to judge whether the clusters were responsive to the facts of the case. Results showed that the teams agreed only 23 to 54 percent of the time, depending on the pair of teams being compared.". It recommends predictive coding as an alternative.

Overview of Email in Viewpoint

Elite Discovery's Viewpoint platform has a relationship analyzer which can provide metrics on email data. In the analyzer, a list of domains is shown in the left pane, and a list of individuals is shown on the right. The respective tables will show how many outgoing and ingoing emails are available for each domain and individual.

Clicking on an individual name will pull up another pane which will have multiple tabs listing all of the email addresses associated with the individual; the email addresses of the individuals that this selected individual has exchanged messages with; and a line graph plotting the number of emails including the selected individual across a date range.

The analyzer will also create a circular schematic which shows how emails are exchanged between domains and individuals. Each domain is designated with a color on the outer rim of the circle. Hovering over the inner rim of the circle will make captions pop up showing a specific email address. Lines across the center of the circle visualize communications between different domains and users.

The relations map has blocks for each domain which are sized to reflect their proportion of emails in the system. It's possible to drill down to an individual email level with similarly proportioned blocks. Color coding is used to show how most of the emails for an individual have been coded for relevancy.

It's possible to QC the coding decisions by right clicking an individual block in the chart, and selecting 'Build View', an option which is available on the other tabs as well.