As you know, the wheels never stop turning here at Everlaw.  Our latest release includes content searching improvements, comprehensive case history, a new help interface, faster image loading, and improvements to our Reviewer QA feature.  Here are additional details about the headliners.

More Powerful Searching

No more stop words means nothing is stopping your search. Terms like ‘AT&T’ are no longer stripped of symbols like ‘&’ or ‘%’, and can now be used in content searches. You can also search for phrases that include generic articles and pronouns.

More Help!

We have made significant improvements to our help. We now have more videos than ever before, in an easily searchable new interface.

Case History

Admins, you no longer have to wonder about the history of your case. You can now view past uploads, user additions, settings changes, and more!

Stay tuned for more amazing features from Everlaw; the next release is going to be huge!

The post Everlaw’s September Release: More Powerful Searching, Case History, and More! appeared first on The Everlaw Blog.

Last year, we kicked off Everlaw’s ediscovery redesign with a new logo. Since then, I’ve been working with everyone on the team to revamp our look—and to reflect it in our software and marketing materials. We’ve been focusing on not only our visual presence, but on the usability of our product.

We were specifically looking to address pain points identified by users and by our team. For example:

• Inconsistent and outdated icons
• Confusing or cluttered interfaces
• Unproductive homepage
• Lack of optimization for small or extra-large screens
• Vague language

Creating a New Visual Language

I started the redesign by creating a new visual language that we could use across our product. I began by creating color swatches, which are harmonious color palettes with specific purposes to be used across our brand. They can significantly improve a user’s understanding of an interface and enhance its interactivity. We use these new colors, fonts, and illustration styles in our ediscovery tool, emails, and print collateral.

Creating a Style Guide

However, colors and fonts are just the beginning: next is the shape and experience of the platform. Starting from scratch, I redefined every interface element used consistently across the product. Every button, box, pop-up, form, and card is now consciously styled, based on its context and function. For example, dashed borders represent buttons that can create a new element, and drop shadows are only applied to elements that overlap others to signify depth. To make sure our icons look crisp at any size, I manually created over 150 custom SVG icons.

Improving the User Experience

Just as important as interface elements is what happens when you interact with them: how will the user know what’s clickable, or how will the user know that their batch process is actually working? Our ediscovery tool now has much more fluid and obvious animations that help users understand their actions.

Card User Experience

A bold interface element that makes up our new homepage are cards: boxes that hold your individual searches, binders, outlines, and tasks. We decided to create these color-coded cards after I brainstormed heavily with the team to address the lack of organization and priority in the old homepage. After much discussion, we felt that cards were the right choice because they take chunks of content and organize them in a way that’s easy to sort through, modify, and use. Because many of our users have lengthy lists of binders and would be frustrated by vertical columns, we also created an “expanded view” that hides other columns and alphabetizes the cards. On the previous homepage, you had to scroll through a table list to resume a binder or search, but with cards, it’s very easy to hide irrelevant content by using the fast and smooth Filter Box.

We also decided users should be able to “favorite” cards, and designated the leftmost column for them on the homepage. By letting you aggregate the cards most relevant to you, those cards will be right there when you log in to immediately resume your review or outline. You get to choose what’s important to your workflow.

Creating Prototypes

Prototyping is a crucial part of the design process because it’s a quick way to show exactly what you’re thinking. Changes can be made and undone rapidly to explore lots of options. As a result, it is much easier to stay unattached to ideas that might not be the best solution for our users.

Therefore, after receiving feedback on the visual language and style guide, I applied them and created prototypes. I used Sketch to recreate every platform page and compiled a clickable prototype using InVision. In total, I made over fifty pages and states to interact with. For animations, I used Adobe AfterEffects to get my ideas across. With these tools, I was able to solicit helpful feedback and start great conversations with every member of the team.

Engineering the New Design

Our engineers translated the mockups into a working site for everyone at Everlaw to test rigorously, which took a tremendous and amazing amount of engineering effort. We examined every corner of the product and made changes accordingly. At every step of the way, we kept asking ourselves, “Will this actually benefit the user? Does this solve the problem?” I sat right next to the engineers to work closely with them and answer any questions while they implemented the new user interface. This collaborative process helped us revisit old problems and brainstorm new ideas on the spot.

The new look is now live! Though the big redesign is complete, we’re still working hard to keep improving our platform with our users in mind. Our pride in the fresh platform we’ve delivered to kick off the new year has only encouraged us to keep innovating. We always enjoy receiving feedback on how Everlaw can continue to push ediscovery forward!

The post Starting Fresh: Everlaw’s Redesign Process appeared first on The Everlaw Blog.

Think of any software or app you use daily. I bet you know a few tricks or hacks to speed things up. For example, I lock header rows to the top of Excel or Google worksheets before scrolling through data sets, and my inbox auto-highlights emails sent from specific senders. Document review is no different: once you know a tool well, you know shortcuts to use.

When we’re coding our ediscovery tool, we try to make it as intuitive as possible. You shouldn’t have to use it for a year to find some hidden trick or workaround that’ll get you the data you want. Want proof? Here are 5 commonly-asked document review actions, along with how Everlaw handles them:

1) How can I construct custodian searches?

If your document set includes a “custodian” metadata field, you can search for it on the main search page. Just drag the green “metadata” term to the middle of the query builder, and choose the “custodian” field. For the second parameter, choose which custodian you want to pull up documents for; it will even autocomplete as you type! Then, run your search. That’s it!

2) How can I pull up all of the documents in a case?

There are two ways to pull up all documents within a case:

1. If you started on a case after 12/17/2015, a permanent “All Documents” card will appear in your search column on the homepage. Clicking the card will bring up a results table with all the documents in your case.

2. You can also create a search that will return all the documents within a case. Simply add the green Bates search term into the query builder, choose “Any Prefix” for the first parameter, and input “1-” in the second parameter. Then, run your search!

3) How can I export large groups of case documents?

You can easily export documents, messages, Binders, and Chronology entries out of Everlaw by using the Export button on the right side of the toolbar. Depending on what you are choosing to export, you will be provided with different options, like desired file format. You can tell when your export is complete in the “Batches and Exports” column on the homepage. Once it’s ready, just click on that card to download your export!

4) How does document deduplication work?

In a document search, duplicate results are automatically hidden from view, except for duplicates that have been rated hot or warm, or coded with any code. You can choose to include duplicates in your search results by clicking the “Include duplicates” box below the query builder.

You can also see duplicates or near duplicates of a specific document during review. In the review window, the panel on the left side displays duplicates and near duplicates down to 95% similarity for a given document. This can help identify different versions of the same document, for example.

5) How can I redact text?

If your case is in production mode (a case-wide setting), redaction features are automatically enabled. There are two primary redaction methods:

1. An area redaction performed using the redaction tool, and
2. Text redaction performed using the text selection tool.

To redact an area, toggle the redaction tool (identified by a black highlighter), click and drag it over an area of your document image/PDF. All text underneath the highlighted area will be redacted when produced.

To use text redaction, click the “text selection mode” icon in PDF view, and select the text you’d like to redact. Once text is selected, a menu with three options will pop up:

1. You can redact a single instance of the text that you selected by clicking on the redaction tool.
2. You can redact all instances of that text in the entire document by clicking on the the redaction marker with the green checkmark. The number of instances of the selected text will be displayed on the icon in yellow.
3. You can add the selected text as a hit highlight, which will highlight all instances of the text, but will not redact it.

Curious how other things work in Everlaw? Just ask in the comments or on Twitter!

Bonus reward for those of you who read this far: email love@everlaw.com or tweet @everlaw, and we’ll mail you a free Everlaw water bottle. Hydration, mmmm!

The post 5 Common Ediscovery Questions appeared first on The Everlaw Blog.

A new release is live! We’re adding to the enhancements we recently made to metadata, to further improve your review. All of these updates are born of user requests: you told us what would make your work easier, and we implemented it. We told you we were listening!

With this release, you can now:

1) Break apart compound documents into units (Unitization)

At times, you may receive productions in a less-than-ideal state. Separate documents may be lumped together into one big compound file, making it impossible to work with them. Today, we’re releasing logical unitization: this feature makes it easy to break up a production into individual “units” or documents. Two things that make it great:

1. It reduces workload: Because this feature is built directly into our platform, there’s no need to have giant productions processed separately before being loaded in for review: our platform does it all.
2. It streamlines workflow: Instead of fighting opposing counsel for a better production format, now you can dive straight into review and unitize as you go.

2) Search for highlighted documents by color

You can already search for documents in your corpus with highlights on them: by who created the highlight or when the highlighting was done. Now, you can also search by the color of those highlights! No more manually searching for a file that has a pink highlight!

3) Spot grouped documents more easily in search results

In search results, we already show search hits and their related documents – like other emails in a thread or child documents. However, you told us that italics weren’t easy to spot immediately, especially after a few hours of review. Now, documents related to your search results are shown in a different color text and their row numbers show their relation – making it easy to spot them at a glance.

4) Print a single page from the review window

You can already print a document from your review window. But what if you only want to print a single page of your document? You told us that this would be helpful, so now you can. No need to mess with printer settings or waste paper!

5) Maintain zoom when switching between natives

When you’re reviewing a certain type of native, you may want to zoom in or out for all files of that type. In the past, your screen would stay zoomed when you moved from page to page in one native document, but it would revert to 100% when you moved on to the next native. Now, you don’t have to re-set your zoom settings on each subsequent native file, saving time and removing frustration!

6) Display end bates in search results

Begin bates is already a default column in your search results table. You told us that it would be nice to see “End Bates” as a column, rather than having to calculate it based on Begin Bates and the number of pages in the document. Now, it’s easy to add End Bates as a column in your search results table.

7) Search for partial Social Security Numbers

We already provide an easy way to search for numbers that resemble Social Security numbers across your document set. You can set up a case-wide highlight for numbers in the format ###-##-####, to make it easy to review and redact them. You told us that you also want to be able to find Social Security numbers when they are already partially-hidden for privacy reasons: in the format XXX-XX-####. Now, you can easily find these partial SSNs, so you can fully redact them as well.

Try out all of these enhancements, and let us know what you think, at feedback@everlaw.com! We’re always eager to make your ediscovery experience even better, with changes both big and small.

The post Unitization and Other User-Requested Features appeared first on The Everlaw Blog.

This weekend, we released 8 new features that will enhance your ediscovery search, assignments, and user management experience. They are smaller changes, all aimed at making document review easier in a big way. You can now:

1) Retain data when you delete a reviewer

Members of a review team may come and go. Ensuring that a team member’s departure doesn’t affect the continuity of a review project can be a headache. Now, that process is a lot more manageable with our new data sharing tool. When you remove a user from a case, you can now choose to share the review items and retain the review product associated with that user.

2) Share coding presets

Coding presets, the “macros” that allow you to apply multiple codes at once, are now shareable! You can share them with entire groups of users, all users in a particular organization, or individual users. This capability can help you standardize review across a team or an entire case.

3) See hit estimates as you build a query

More information while you’re still building a search can help you review more efficiently and effectively. This is why our ediscovery platform already shows real-time search result previews – and why we are releasing a new feature that shows estimates of the number of documents matching a query. When adding a metadata criterion to your search, you will now see the number of items matching that value in your database. This feature can help you ballpark assignment volumes or project review timing.

4) Search for null metadata values

Already, you can easily locate documents with a particular value for a given metadata field. If you want to find documents that do not have any value for a field, you can now search for that too by selecting the “no value” parameter. For example, you might want to pull up all documents in your database that do not have a specified custodian.

5) Use de-duplicated “file” field names

We’ve added “Filename” and “File Path” as two new canonical fields. This normalizes all conceptually-related fields under the single “Filename” or “File Path” field, making it easier for you to search across these attributes.

6) Quickly open a document family

The left-hand panel of the review window already shows you document families. During review, you may need to work with a document family in more depth. Instead of having to build a new search to retrieve that document family, you can now simply click the magnifying glass icon to open the family in a new results table.

7) See the percent of unassigned docs already reviewed

To make assigning documents even easier, you now have more information on unassigned documents in an assignment group. You can now clearly see what percentage of these unassigned docs have already been reviewed. This is useful because reviewed, unassigned documents cannot be assigned to reviewers within your assignment group.

8) Message all users in an assignment group

Need to quickly communicate with all reviewers associated with an assignment group? Instead of adding each person individually, there is now a new “message all assignees” option in your in-platform messaging tool.

We hope these new features give you an even better ediscovery experience. As always, let us know what you think at feedback@everlaw.com!

The post New Features Bring More Ease-of-Use appeared first on The Everlaw Blog.

We’re excited to announce a new round of updates to our litigation tool: one major new feature and five smaller ones.

Introducing Search Term Reports

Search term reports are a significant new offering that will allow administrators to perform early case assessment (ECA), make informed staffing decisions more quickly, and triage review. This feature provides a higher-level case overview across multiple searches, a big benefit for teams with sophisticated workflows.

How Do They Work?

Search term reports allow you to define a searchable set and then add multiple search terms for that set. You can then immediately see how many documents match your different searches, providing you with vital intelligence about your case. Using this information, you can plan for document review: arrange for the staffing you’ll need, understand your potential workflow, and anticipate the likely timeline. This early case assessment will improve efficiency and increase predictability for your cases.

In addition to search term reports, we are bringing you a handful of smaller improvements to make your experience better. You can now:

1) See attachments in email threading

You can now immediately see attachments when looking at email threads in the results table. This gives you a truer sense of the components of the thread and makes it much easier to review families of documents. You can review the entire email thread and its attachments, and code them simultaneously within the context panel of the review window. By removing extra steps in your workflow, this will mean faster and more efficient review.

2) Find and redact credit card numbers

When creating custom highlights and persistent highlights you can now select “credit card numbers” as one of the default search terms. This is an efficient way to easily find credit card numbers throughout your document set, for potential redaction.

3) Use more intuitive case settings and analytics interfaces

Your case settings and analytics pages look better than ever! The updated interface makes it easier and more intuitive to add users, set up a coding sheet, and view case analytics.

4) Use a shortcut to duplicate a search term

In the search interface, you can now use keyboard shortcut “d” to duplicate the search term you just applied. It’s a simple way to search across multiple terms within the same search term category. You can see some other keyboard shortcuts here.

5) Search by “has format”

The current search term “has native” will now be relabeled as “has format.” The new search term has been expanded and enhanced to encompass a variety of different formats: native, image, text, and pdf. You can now search by any of these formats by selecting “has format” on the search screen and then choosing one of the four options from the dropdown menu. With a more granular way to find and filter documents, you can better refine your search set and speed up your review.

We hope these new features give you an even better ediscovery experience. As always, let us know what you think at feedback@everlaw.com!

The post Introducing Search Term Reports appeared first on The Everlaw Blog.

Minimizing document review cost is a priority for most firms and clients. Reducing cost comes down to reducing hours, or even minutes, of review time. In Everlaw’s litigation platform, every feature is designed to allow you to get things done faster.

One way to do this is with Everlaw’s highlighting tools. They allow you to quickly identify useful terms across your document set. There are three types of highlights:

1. Search: Highlights the terms that you’ve searched for. This type of highlight shows you why each document is in your search results by identifying the terms that made it responsive to the search.
2. Persistent: Highlights the term in all documents in your case, for all users. This type of global highlight (set up by admins) makes it easy to redact privileged information, identify patterns, and ensure intra-reviewer consistency.
3. Custom: Highlights a term within the document you’re currently viewing, just for you. This type of local highlight helps ensure you don’t miss an instance during review.

Today, we are putting the spotlight on custom highlights.

What Are Custom Highlights?

Custom highlights are a convenient way to search for a term in an individual document. They can help you find contingent, important information for that document and take action on that information. Unlike persistent highlights, a custom highlight can be created by any user on the case. Like persistent highlights, you can have multiple custom highlights for each document and, if you pin a custom highlight, you will be able to view that highlight throughout your documents. These will not be seen by others who view those same documents.

Why Do You Need Custom Highlights?

• They allow you to find every single instance of a term in your document, without intensive manual searching or fear of missing one.
• They make it easy to redact all occurrences of a word or phrase in a document, so you can keep confidential or privileged information protected.
• They allow you to quickly jump to the important information in long documents.

How Do You Set Up Custom Highlights?

You can set up custom highlights in a specific document from the review window. Go to any document. Then:

1. On the right side of the review window, find the header that says “Custom Hits,” underneath the “All Hits” heading.
2. Click into the box or field that says “Search Document,” and then type in the word or phrase that you want to locate within the document.
   • Common patterns, such as social security numbers or phone numbers, will automatically appear in the drop-down menu as options.
   • You can also use advanced search syntax, such as fuzzy, wildcard, or proximity search.
3. Press Enter. You will now see a new line with your selected term under “Custom Hits” and the term will be highlighted throughout the document.
4. To apply your custom highlight to the other documents in your case, you can “pin” that highlight. Click the custom hit, which reveals a pin icon. When you pin the hit, the pin icon changes from light grey to dark grey. If you don’t pin a custom highlight, then it will be removed when you navigate to the next document. To unpin a custom highlight, click the pin icon again so that it’s no longer bolded.

You can also perform redactions on custom highlights by clicking the redact icon underneath your term. You can either redact the one instance of a term that you have selected, or all instances of that word or phrase in the document.

When entering multiple custom highlights, they will be highlighted in different colors – both throughout the document and in the key in the right panel. Color bands representing the highlights will also appear in the document jump bar (in text and image views). The jump bar is a navigation tool which allows you to jump from one page to another within the document.

Any questions? Let us know at feedback@everlaw.com, or review the detailed documentation within the Everlaw platform. You can also check up on past feature deep dives here.

The post Speed Up Document Review with Custom Highlights appeared first on The Everlaw Blog.

To support investigative reporting, we’re giving news organizations free access to Everlaw’s ediscovery software. From whistleblower dumps to FOIA requests, journalists need to analyze large data sets quickly and accurately. More and more reporters are trawling documents—whether emails, text messages, or files—to uncover the stories within. This is much the same process attorneys go through while building a case. Finding key documents and weaving them into a story—it’s what Everlaw was designed for.

While ediscovery software isn’t new, until recently it’s been hard to use except for those with specialized training. Plus, users often needed to ship the data to a third party for uploading—a non-starter for reporters on tight deadlines. Not to mention the technology was slow and at times inaccurate —hardly what you need when you’re not sure what you’re looking for.

Everlaw solves all of these problems. Because we’ve made usability a top priority, our platform is easy to use, so you don’t need extensive training on ediscovery. We’ve just launched a self-serve upload feature, so you can load data anytime, from anywhere. Our blink-speed search means you can quickly trace interesting leads to explore what the data is saying. Our prediction engine can even suggest documents to review. PDFs are no obstacle. Everlaw handles them, and other common document formats, as easily as text.

Once you’ve executed a search, a combination of built-in and user-defined filters allow you to slice and parse large data sets quickly and easily from any browser or tablet. With our Storybuilder feature, you can easily insert key documents into chronologies, story outlines or interview questions, all within Everlaw’s secure platform. Attorneys use this to build their case or prepare depositions. The same can apply to journalists building their story and crafting follow-up interviews.

And your documents will be well protected. Everlaw’s SOC 2 Type 2 certification assures the security, availability and confidentiality of your data. Users can also activate two-factor authentication, which requires validation from a mobile device or secured email account in addition to your password. It’s an additional level of security to keep your sensitive data private. 

Everlaw for Journalists is available for free to qualifying news organizations. For further information and terms, please reach out to journalists@everlaw.com to arrange a demo with a member of our team.

The post Ediscovery Software for Investigative Reporting appeared first on The Everlaw Blog.

It’s been a couple of months now since we launched Self-Serve Uploads and Productions, our biggest update of 2016. Since then, we’ve had a lot to say about the new features. (Short version: they’re really easy, really convenient, and really, really fast). And we recently got back from Legalweek where we demoed Uploads and Productions until we could demo no more.

It’s time to yield the floor. And who better to yield to than someone who is actually using Uploads and Productions in the field? We caught up with Mike Dunn, director of legal services at our partner Elevate, to hear how uploads and production used to be conducted, and why there was so much room for improvement.

Let’s start with definitions. What exactly are uploads and productions, and what makes them so tricky?

Processing is the discrete phase of ediscovery where you take the metadata and text of your raw files and structure them into a database. It can be quite complex. First of all, you have to make a lot of decisions about what to process without knowing what’s in the data. Legacy products make you run through several steps and wait days to find out what you’re about to process. Everlaw accelerates the “process” of processing and allows you to upload sample sets and share this information seamlessly right away. On top of that, legacy products often have problems with password-protected documents, or documents with strange file types that aren’t business records. These can make error handling very time-consuming and difficult. Everlaw has basically created workflows that automate error-handling, which makes the entire processing phase a lot less complicated.

The same is true on the production side. When coding documents as privileged or irrelevant, it’s always been someone’s job to say, “OK let’s make sure we haven’t produced anything privileged.” And it’s easy to make a mistake. In the latest release, Everlaw has automated a lot of these processes. It absolutely will not allow you to put anything coded ‘privileged’ into a production set. That’s extremely helpful. It makes quality checking and everything else so much easier.

I imagine packaging and adhering to particular protocols can be pretty labor intensive too.

Yes, it’s very labor intensive to keep checking an Excel or Word document that has the specs for this particular project and this particular counsel. With Everlaw, that’s all automated and savable within the platform. No matter who picks up the case to manage production, they can work from a preset standard. They can change what they need to, but they have everything right there on the platform. They can see what was done last time and what the overall workflow should be, and execute through a simple wizard.

So as an ediscovery service provider, what does this self-serve functionality do for you? What are the big advantages?

The main advantage is how it breaks down the complexity and the black-box process that have always plagued the ediscovery market. More intuitive interfaces and processes give counsel much more transparency into what’s going on. They can also level the playing field. With Everlaw you don’t need an advanced competency in ediscovery basics to really build your case. It allows us as a provider to focus on more valuable services and highly leveraged areas of expertise. It eliminates the need to have a fully built-out team dealing with the minutiae of ediscovery.

What do you think are the strongest use cases for the self-serve capability?

I think there are a couple of very compelling use cases. The first involves cases with extremely quick turnarounds, where you have a relatively short time to collect, review and produce documents. Having the parameters for uploads and productions saved in the platform as well as baked-in analytics, makes it very, very easy to make decisions and quickly move through the case.

The other opportunity that jumps out is for sensitive smaller cases where you’re doing an internal investigation and don’t want to expose what you believe is possibly going on to outside counsel or a broad internal team. You can have a very small team investigate what’s happening without a lot of emails and reports flying around. Everything, including email communication with the team is right in the platform. You can collect a couple custodians’ email boxes, upload them, review them and send the output back with reports to just a small team that needs to review it very very quickly. That allows faster decision-making within a corporate legal department as well.

How much do you hear about security from your clients? Are they concerned about moving data around through different processes or shipping data outside the platform?

Absolutely. We work with large corporations on very sensitive data. If they’re going to bring in any provider—whether it’s ediscovery or IT or anything else—to augment what they’re doing internally, security is a must. They won’t even look at somebody who doesn’t take security very, very seriously.

Everlaw has helped us by leveraging the public utility cloud, which is trusted by government organizations. Even the DOJ has a cloud-first initiative. By using Everlaw’s advanced infrastructure, we don’t have to put a bunch of time and resources into building a cloud environment that matches our clients’ specifications. We we can rely on Everlaw, and AWS, to have extremely effective, well-thought-out security and response plans. We’ve been through the security process several times with Everlaw and our clients, and they’re very satisfied with how that all plays out.

What’s the biggest challenge when it’s time to produce documents?

Traditionally the biggest challenge has been time and effort. The team doing the production usually needs 48 hours—for good reason. But litigators have good reasons of their own that make a 48-hour process impossible. Inevitably, the production team winds up working under a tight deadline, and needs to be extremely careful it doesn’t release privileged information, or information that’s irrelevant but damaging to the client. So typically you have a robust QC process that involves a lot of manual steps. That’s always been our biggest challenge: how we can be sure our clients produce only what they want under the deadlines they need.

One way Everlaw has addressed that challenge is by building in a lot of the automation for QC. We know every time there’s a redaction, it will be applied by the technology as it was written, and we’ll be able to QC it very quickly. It will line up everything for us, and can just click through and put some eyes on it. Before we had to do a lot of sampling to ensure things worked the way they should have. Now, with the added automation, it’s very easy to do QC fast and efficiently.

Most ediscovery service providers make money from processing, but you don’t. Why is that?

Traditionally providers had to make money from it. Both processing and productions required large investments in infrastructure and people. There was a business case for it. You were building a service to execute certain processes. It took X amount of money to build the service. You needed to make a margin as a business, so you built accordingly.

But with Everlaw, we don’t have to charge individually for items like processing and production. We can leverage Everlaw’s infrastructure for whatever we need whenever we need it. That public utility cloud allows us to scale up and do massive processing jobs very quickly. Same thing with productions. Scale-up is near instantaneous, so we don’t have an infrastructure investment we need to recoup.

Everlaw’s automation also simplifies of the human side of the process. We don’t have to invest massively in people to execute these routine processes, so we don’t have to recoup anything from these processes. We can focus on filling high-value services to our clients. We don’t have to recoup investment on the lower value services.

That’s great. Thanks for taking the time to talk us.

My pleasure.

The post Uploads, Productions, and Real-World Practice: a Conversation with Elevate appeared first on The Everlaw Blog.

With the goal of optimizing your ediscovery experience, we’re happy to announce a new round of improvements to Everlaw based on feedback from you!

Admin Features

1) Grouped Persistent Highlights

You can now categorize persistent highlights to make it easier for you to navigate, distinguish, and make sense of all the content hits that appear in a document. Categorized highlights will share the same color when rendered in the review window and will be listed together in the hit highlighting panel. For example, you can group all the relevant people in one category, entities in another, and relevant jargon in a third.

In the review window, you can navigate highlights by group or individual highlights.

2) Redaction Stamping

As a case admin, you might want your reviewers to apply stamps to redactions in order to note why a redaction has been applied. You can now create case-specific redaction stamps that reviewers can add with the click of a mouse. Applied stamps will be overlaid on top of redactions in the review window. You can also allow reviewers to apply custom stamps.

This feature is integrated with productions: custom redaction stamps will be printed on top of redactions in produced documents, overriding any default text that might be specified in the production protocol.

3) Document Download History

With this release, document download history is tracked at the individual user level, allowing admins to easily audit who performed exports out of a database, when the export occurred, and which documents were involved.

General Features

4) Search Interface Improvements

Our list of search terms has grown over the years. In order to simplify the interface, we’ve added the ability to collapse categories of search terms. For ease of initial use, we’ve identified the most used Document and Review search terms, which will appear by default at the top of the list. Now, you can also customize which terms are always visible with a simple drag and drop.

5) Children Count

When scanning a grouped results table, you may want to know how many children documents are in a particular group. Previously, you would need to expand the group and manually count the children. Now, the number of children is displayed next to the parent in a grouped table, even before expansion.

6) Performance Improvements

Users will be pleased to hear that we’ve improved the speed of our batch tasks (batch coding, adding docs to binders, etc). With the recent changes, batch tasks will be 2-3x faster!

You can also expect faster loading when accessing the homepage, assignments page, and messaging when you have large dynamic assignments. PDF viewing speeds have also improved.

7) Loadfile Exports

For additional flexibility in your productions, the loadfile-only download will now contain all of the included loadfile types, rather than only the .dat loadfile.

8) Email Login

Have you ever had trouble logging in because you forgot your username? Well, worry no more as you can now log in with either your username or your email address.

We hope these new features improve your ediscovery work. As always, send us your thoughts at feedback@everlaw.com!

The post Grouped highlights, redaction stamping, and more appeared first on The Everlaw Blog.

Document review can be a lengthy and involved process, with complex searches and multiple attorneys assigned to review potentially responsive data. Attorneys can rightfully ask, after diligently reviewing their search term reports and predictive coding hits, just when are we done with document review?

The answer is not as simple as when every email is read.

We can look at a recent case in the news to determine what the courts have to say on the matter. In the 2017 Davine v. Golub Corp case, the Defendants were given specific guidance on when their document review was complete. Stated in the order granting a motion to compel the production of email communications from 20 opt-in Plaintiffs was the following:

Defendants are entitled to rely on their predictive coding model for purposes of identifying relevant responsive documents, and may cease their review of the documents identified as possibly relevant when they made a good faith determination that the burden of continuing the review outweighs the benefit in terms of identifying relevant documents.¹

Here is the $64,000 question: When does the burden of continuing document review outweigh the benefit of identifying relevant documents?

Case law states that the standard for producing electronically stored information is one of reasonableness, not perfection.² Moreover, parties must conduct a reasonable search when responding to discovery requests.³

Discovery depends on attorneys acting in good faith and meeting their professional obligations to “reasonably and diligently search for and produce responsive documents.”

How Much ESI is There to Review? Analyze the Case

One method for lawyers to determine if they have conducted a reasonable search is using Case Analytics. The attorneys handling the case can know exactly how many records are in the case, how many have been viewed, and how many have been coded. There is also an estimate of the number of months it will take to complete document review.  

How Effective Are the Lawyers at Document Review?

Attorneys using predictive coding can focus their efforts on the ESI that is likely relevant to the case. Nevertheless, that still takes time to review. Case managers can gauge document review effectiveness with review time by Attorney, Review Progress, Rating Trends, and Reviewer Pace.

The amount of time spent on document review is billed directly to the client (unless the law firm is not charging the client based on the billable hour). The progress made in number of records reviewed in an hour shows how effective lawyers are being with their time for identifying what is responsive to a case.

If ongoing document review is yielding a large number of responsive records, then there is a strong argument for it being time well spent. However, if substantial time is being spent identifying non-responsive records, then relevant records have turned into a needle in a haystack. This is the moment when lawyers can ask with all seriousness, does continuing document review make economic sense? If a large amount of responsive records have been identified, and now hours are being spent to find random relevant records with marginal value to the case, the answer is no. Alternatively, if the relevant ESI can make or break the case, then continuing review is worthwhile.

In Search of Reasonableness

It is unreasonable for litigants to review every email, spreadsheet, and document collected for a lawsuit to determine relevance. The issue of when ESI review is completed is a simple one with complex analysis: Has the party conducted a reasonable search for relevant electronically stored information?

Predictive coding helps focus on the ESI that is relevant to a case, so attorneys can spend their energy on what is proportional to the needs of the case and not mindlessly clicking irrelevant and non-responsive on email messages. A party can then determine that the burden of document review outweighs any rogue relevant information when the time spent conducting reviewing is yielding the occasional relevant record in an ocean of irrelevant information.

¹Davine v. Golub Corp., No. 3:14-cv-30136-MGM, 2017 U.S. Dist. LEXIS 18109, at *3 (D. Mass. Feb. 8, 2017).
²See, Chen-Oster v. Goldman, Sachs & Co., 285 F.R.D. 294, 306 (S.D.N.Y. 2012), citing The Sedona Conference, The Sedona Conference Database Principles: Addressing the Preservation and Production of Databases and Database Information in Civil Litigation, March 2011 Public Comment Version, at 32.
³Bird v. Wells Fargo Bank, 2017 U.S. Dist. LEXIS 113455, at *11-12 (E.D. Cal. July 20, 2017), citing See Reinsdorf v. Skechers U.S.A., Inc., 296 F.R.D. 604, 615 (C.D. Cal. 2013).

The post With Millions of Documents Collected, When Has a Producing Party Completed Document Review? appeared first on The Everlaw Blog.

Proportionality in any case is a balancing of interests—whether the burden or expense of the proposed discovery outweighs its likely benefit. A basic question to ask of all discovery, is how does the requested information help resolve the claims in the case? What is the cost of acquiring the information? What is the value of the case? Knowing the answers to these questions can help a Court decide whether to order discovery, modify requests for production, or limit discovery.

Measuring the Scope of Discovery

In a 2017 lawsuit over alleged violations of the Americans with Disabilities Act, the Defendants, the Cheesecake Factory, Inc., brought a motion to compel discovery responses pertaining to the Plaintiff’s employment history going back over a decade from both the Plaintiff and third-parties.¹ The Plaintiffs produced discovery pertaining to prior employment dating back to 2004. The Plaintiffs’ claims in the lawsuit included back pay estimated at $480 (plus interest), compensatory damages for emotional distress, and punitive damages.

The Plaintiffs argued that a mere $480 in back pay was not proportional to the Defendants seeking discovery from the Plaintiffs’ former employers for over a decade. On its face, simply looking at $480 in back pay does not sound proportional to the expense of retrieving discovery going back a decade. However, the Court pointed out that the discovery was not for the claim of back pay, but was related to the other claims and defenses in the case. As such, evidence of how the Plaintiff interacted with prior employers concerning his disabilities was both relevant and proportional to the ADA claims in the lawsuit. As this case suggests, low damages on one claim cannot be used as a shield from discovery of information related to other claims.

However, in this case, the Court held that the Defendants’ discovery requests were overbroad for other reasons: the requests were not limited to employment records; and the requests were in excess of ten years.

Discovery Strategy for Individual Plaintiffs

Litigation can get expensive fast. Larger parties may be able to comfortably absorb litigation costs, but it can be challenging for individuals in employment, civil rights, or family law cases. One strategy to control litigation expenses is to focus specifically on the data that is relevant and proportional.

In a case such as the above, it is very possible a similar plaintiff could have responsive employment information in personal email messages. It is also highly probable that an individual using a webmail account could have email going back ten years. Not many individuals have personal data retention and destruction policies. They just don’t delete email or randomly delete messages after they are read.

One collection strategy for a case involving a plaintiff with webmail is for the collection expert to do a targeted email collection for emails from prior employers based on domain names. This would collect messages and attachments that should be both relevant and proportional to the case. However, it might not get all responsive emails, as there could be work related emails sent between people who do not have email accounts from their employers.

Another option is to do a collection and then exclude what is obviously irrelevant, such as news alerts, newsletters, or marketing emails. Other addresses could be used as a basis for inclusion or exclusion within a collection.

As the relevant and proportional dataset is exported to Everlaw for review, similar search strategies can be used for reviewing for responsiveness. In the above case, the Plaintiff identified data ranges during which he was employed at specific companies. A search could be created within the dates of employment with content searches for specific keywords, such as company names, “performance reviews,” or similar terms.

The same search strategy could also be done to find all messages from anyone with the domain address of a specific employer.

Many companies alert employees of paychecks with email pay stubs and electronic W2s. Searches can be created to identify this information in a variety of ways, from content searches for EINs to specific searches for payment.

Cases such as the above could seem onerous to an individual party. However, with analysis of what is truly needed to prove the claims or defenses, along with strategic searches, document review can be focused on relevant and proportional ESI. Determining the scope of discovery, such as relevant dates, specific individuals, email addresses, and the factual allegations of the lawsuit, can empower lawyers to find what they need to vindicate their client’s claims.

¹EEOC v. The Cheesecake Factory, Inc., 2017 U.S. Dist. LEXIS 144391 (W.D. Wash. Sep. 6, 2017).

The post Proportionality Before Dessert appeared first on The Everlaw Blog.

The easiest data to search and review is the kind we’re all used to. Words on a page, flowing naturally from left to right, top to bottom. We compose our search, our document appears, we read it, we take notes. Simple.

But as we know, modern data takes many forms. There are chats, social media posts, audio iMessages, voicemails. The list goes on. How do we find the important nuggets of information hidden in a voicemail? Listen to each one? Ignore them and hope the information comes out somewhere else? In an age where Google can send us transcriptions of our Google Voice messages in moments, there must be a better way in ediscovery.

Audio Transcription

We bring to you audio transcription. Our latest feature has undergone months of user testing and iterating before reaching its final state. We’ve tested multiple speech transcription technologies, optimizing for conversational speech like a phone call, a meeting, or a deposition. (It must be said: transcription is a field of active research, and the technology is not yet perfect, but we’ve provided what’s available within the current limits of the technology.)

With this release, you can view an automatic transcript of suitable audio files directly in the review window as you listen to it in the native view. You can take notes on particular timestamps throughout the transcript, navigating back to those notes later in your review. You can access notes with timestamps from the results table. You can even use custom hits to highlight particular text in your transcript.

If “clean energy” is a custom hit you’ve set up for your case, you will immediately be directed to any audio or video files where clean energy is mentioned and the text will be highlighted in the transcript.

Here are some additional details about how the transcription tool works:

• You can start playback or drag the playhead to a new position, which will cause the relevant part of the transcript to scroll into view automatically.
  
• As playback continues, the transcript will scroll to keep the relevant segment in view.
  
• If you manually scroll the transcript while the media is playing, the transcript will become “unsynced” from the audio. Simply click the Sync button in the transcription header to re-sync the transcript to the player.
  

If your dataset on Everlaw already includes native uploads, we’ve already transcribed all the appropriate audio in them. Check out the transcripts today! We will also be adding support for pre-processed document transcription in an upcoming release.

And There’s More

A couple more features we added into this release, just in time for the holidays:

Bookmark pages in StoryBuilder Outlines: You will now be able to easily bookmark a page of a multi-page document within a StoryBuilder Outline. You can navigate directly to bookmarked pages, as well as export PDFs with only bookmarked pages. You can also add multiple bookmarks from a single document to an Outline.

Upload documents from cloud-based apps or via direct links: With this release, upload directly from cloud-storage apps to Everlaw. When navigating to the New Upload page, you will see icons along the bottom that will direct you to popular, cloud-based storage apps: Box.com, Google Drive, Dropbox, OneDrive, and Office 365 SharePoint. You will be asked to log in to your application, after which you can select specific files from that app’s site.

Google Vault support: Everlaw’s upload tool can now support the ingestion of Google Vault files.

Adding documents to projects is now 5x faster: Because we know every second matters.

And that’s it! Please take our new features for a spin and let us know what you think at feedback@everlaw.com.

The post Audio Transcription to Ease Your Review appeared first on The Everlaw Blog.

We at Everlaw are proud to announce three major updates to our ediscovery platform, and just in time for our trip to Legalweek New York.

Data Visualizer

Everlaw’s latest update includes our brand new Data Visualizer, a tool that allows any user to create interactive visualizations from any set of documents, bringing to life information about document dates, metadata, contents, formats, review activity, and predicted relevance.

Viewing your dataset by date, for example, will give you a visualization of the spread of documents created (or modified, accessed, etc.) over a particular period of time.

Viewing your dataset by document type will inform you of the nature of the dataset. Is this mostly email you’re dealing with, or is there a large portion of chat files you’ll need to wade through?

It’s all there for you with Data Visualizer.

Predictive Coding System Upgrade

Long-time users will notice our predictive coding system has a fresh new look. But in addition to the visuals, the new system underwent a major upgrade under the hood. The system now makes it easier to create and train new, more defensible prediction models, provides more guidance on how best to make use of a model’s predictions, and makes the evaluation of a model’s performance both richer and more interactive.

Easier model creation

We’ve changed the way prediction models are created. In the past, you specified the relevant and irrelevant criteria for a model. Now, you’ll set the criteria for documents considered “reviewed”—or those within the universe of documents you’d like to be considered for training. You can then specify the subset of those documents which are “relevant.” All documents that are “reviewed” and not “relevant” will be considered “irrelevant” for the model.

Update the model on demand

Now, you’ll be able to initiate an immediate update of your model from the predictive coding page at the click of a button.

Performance metrics at any relevance boundary

Select any relevance boundary and see the precision, recall, and F1 values at that boundary. This allows you to easily select a body of documents based on your desired precision and recall levels. For example, if you want to ensure that all responsive documents are captured by your search, you may choose to increase your recall cutoff score, ensuring that you cast the widest net possible. On the other hand, if you’re only interested in seeing documents that the model predicts are likely to be hot, you may increase your precision cutoff score, ensuring that you only see documents that are likely to be relevant to your case.

New Homepage Organization Tools

This release brings the introduction of a much-anticipated feature: homepage folders! Organize your personal dashboard just the way you’d like by creating folders that can store multiple cards. And while you’re at it, share folder contents with your colleagues while also managing card permissions.

Check out these feature along with a few more:

• Searching against a prior search
  
• Support of Google Drive files
  
• Sorting by billable size
  
• Interface changes to the organization admin dashboard
  

More details on all of these additions can be found here. Please try them out on your next project!

The post Now Introducting Data Visualizer and a Predictive Coding System Upgrade appeared first on The Everlaw Blog.

This is the tenth post in our weekly ediscovery series covering our ediscovery chapter of a legal informatics textbook. In this series, we’ve covered the ediscovery basics, core technical ediscovery concepts, the technologies powering ediscovery (encryption, machine learning, transcoding, etc.); and we’ll soon get to the future of ediscovery. You can also download the ebook in full. 

Today we’ll dive into something you may all use, but not be familiar with the technical details of—optical character recognition, or “OCR.”

Optical Character Recognition

Even more basic than recognizing text in audio and video files is the task of recognizing text in images. It’s not unheard of, for instance, for emails to be produced in ediscovery as TIFFs without either embedded text or accompanying text files. In those situations, making the text searchable with accurate optical character recognition (OCR) is the only solution.

OCR is a complex process. Because OCR engines must deal with a wide variety of inputs—including everything from scanned receipts to photos of book pages—they commonly perform a number of pre-processing steps to normalize inbound data. This includes deskewing (aligning the page to a perfectly vertical or horizontal plane), removing lines and spots, and analyzing the layout of the page and structure of the text.

With pre-processing complete, the task of recognizing characters begins. There are two primary approaches: pattern matching and feature extraction. The former compares each character, pixel-by-pixel, with a library of stored character images to look for a match. The latter is more modern and, predictably, uses machine learning to develop a more nuanced understanding of the features defining the text and the wider document. This yields accuracy of up to 99%.

Over time, it is likely that the tools used for OCR will merge with those used for machine translation and transcription, as providers aim to consolidate and harmonize their machine learning approaches. Indeed, Microsoft and Google both offer on-demand OCR services as part of their computer vision tools for recognizing people, places, objects, and other elements beyond merely the text within a given image. Regardless of how it is packaged, however, OCR is likely to decline in importance over time as written materials make up less of the data in litigation.

The next post in our series will expound on the importance of a user-grade customer experience in any modern ediscovery platform.

The post Why Optical Character Recognition (OCR) Matters in Ediscovery appeared first on The Everlaw Blog.

There are many ways to achieve a focused dataset for review using both search terms and predictive coding.

Magistrate Judge Katherine Parker issued a detailed opinion on the use of predictive coding in a recent discrimination case . In Winfield v. City of N.Y., the Plaintiffs provided 665 additional search terms to be applied to the Defendant’s review database. The supplemental searches would have added 90,000 more records and cost approximately $248,000 to review. The Defendants agreed to run the additional searches, but stated they would use predictive coding to narrow the data set for review. The Court had actually been the first to recommend the use of predictive coding to help expedite discovery review.¹

Coupling the use of search terms with predictive coding in virtually any well-planned workflow can help attorneys conduct document review that is proportional to the needs of the case.

Challenging Predictive Coding

In the Winfield v. City of N.Y. case, the Plaintiffs were concerned about the reliability of the Defendants’ predictive coding workflow, claiming that the Defendants had a narrow view of what was responsive and had over-designated documents as privileged and non-responsive. The Plaintiffs’ arguments were based upon two documents that were withheld, but for which the extracted text was inadvertently produced with placeholder images. The Plaintiffs claimed the withheld records were relevant and should have been produced.

In deciding the Plaintiff’s challenge to the Defendants’ predictive coding workflow, Judge Parker provided a detailed summary of discovery production cases, ultimately finding for the Defendants. First, he found, producing parties are in the best position to “evaluate the procedures, methodologies, and technologies appropriate for preserving and producing their own electronically stored information.”² Moreover, courts have not traditionally “micro-managed parties’ internal review processes,” because 1) attorneys are officers of the court who are expected to comply with Rules 26 and 34 in connection with their search, collection, review and production of documents, including ESI;” and 2) to avoid putting attorneys in the position where they may end up disclosing work production, litigation tactics, and trial strategy.³

The Court further explained that while perfection is not required in producing discovery, a producing party must take “reasonable steps to identify and produce relevant documents.”⁴ This means parties cannot put in “half hearted and ineffective efforts to identify and produce relevant documents.”⁵

Based on the above principles from prior cases, Judge Parker stated:

“…This Court is of the view that there is nothing so exceptional about ESI production that should cause courts to insert themselves as super-managers of the parties’ internal review processes, including training of TAR software, or to permit discovery about such process, in the absence of evidence of good cause such as a showing of gross negligence in the review and production process, the failure to produce relevant specific documents known to exist or that are likely to exist, or other malfeasance.”

The Court rejected the Plaintiffs’ arguments that the Defendants’ training of the predictive coding system was either grossly negligent or unreasonable. The Defendants were thus ordered to conduct their supplemental review with the blended workflow of search terms and predictive coding.

Bow Tie Thoughts

Magistrate Judge Katherine Parker’s opinion on predictive coding was a thorough one, covering both the law and how the Defendants trained the predictive coding system. From the context of the opinion, the predictive coding system seems to have made use of “simple passive learning”—with the discussion of training a seed set—rather than “continuous active learning.” Either technology would be better than manually reviewing 90,000 records; however, one advantage of continuous active learning is that all review decisions automatically train the engine, which continuously updates the rankings for predictions.

The Defendants’ workflow, which involved applying search terms before using predictive coding to focus in on documents to review, is common among firms conducting document review. The prospective search terms applied to a dataset with “or” searches could have a staggering number of hits, likely with a large number of false-positive records for review. Using predictive coding to assist attorneys in maximizing their time for review and producing data is one option for review.

Other options for using search terms would be to develop search strings, so that keywords are not the sole basis for identifying a record for review. One example of such a string is proximity searching between specific keywords. Another could be a search of email messages between specific individuals, over a set timeframe, with specific content searches in the messages. There are many ways to achieve a focused dataset for review with search terms. Coupling the use of search terms with predictive coding can help attorneys conduct document review that is proportional to the needs of the case.

Judge Parker’s opinion provides guidance regarding options for challenging a party’s discovery methodology: evidence of good cause showing gross negligence in review, failure to produce relevant specific documents known to exist, or other malfeasance. This is a high bar, but not an impossible one to meet. Successful challenges would require showing a production was incomplete, contained a large volume of documents that were clearly irrelevant, or was extremely small. Such challenges would be highly fact-specific in order to demonstrate either gross negligence or malfeasance.

¹Winfield v. City of N.Y., 2017 U.S. Dist. LEXIS 194413 (S.D.N.Y. Nov. 27, 2017).
²Hyles v. New York City, 2016 U.S. Dist. LEXIS 100390, 2016 WL 4077114, at *3 (S.D.N.Y. Aug. 1, 2016) (citing Principle 6 of the Sedona Conference).
³See generally Disability Rights Council of Greater Wash. v. Wash. Metro. Transit Auth., 242 F.R.D. 139, 142-43 (D.D.C. 2007).
⁴HM Elecs., Inc. v. R.F. Techs., Inc., 2015 U.S. Dist. LEXIS 104100, 2015 WL 4714908, at *12 (S.D. Cal. Aug. 7, 2015), vacated in part on other grounds, 171 F. Supp. 3d 1020 (S.D. Cal. 2016).
⁵Bratka v. Anheuser-Busch Co., Inc., 164 F.R.D. 448, 463 (S.D. Ohio 1995)).

The post How to Make Use of Predictive Coding and Search Terms When Producing Discovery appeared first on The Everlaw Blog.

Magistrate Judge Sallie Kim issued a discovery opinion that should be included in ediscovery chapters of Civil Procedure textbooks. In a security class action against Twitter, Judge Kim methodically issued orders covering whether to include custodians in the scope of discovery, application of the Stored Communications Act, and proximity search terms.¹

Should the Co-Founder and CEO Be Included as a Custodian?

The parties had agreed on 25 custodians; however, there was disagreement on whether to include Jack Dorsey, the CEO and co-founder of Twitter. Dorsey was the CEO from 2007 to 2008, was Chair of the Board of Directors, and CEO again in 2015. Dorsey was the individual who “came clean” on Twitter’s metrics that went to the issues of misleading investors.

The Defendants argued that searching Dorsey’s email was premature, which the Court rejected. Moreover, the Court also rejected the argument that Dorsey had limited involvement, since he was the one who admitted Twitter’s actual metrics. Even though other custodians might have responsive information, it would not justify eliminating Dorsey as a custodian. As such, the Court ordered the inclusion of Dorsey’s files to be searched.

Direct Messages Are Protected by the Stored Communication Act

The Court found that the Stored Communication Act (SCA) prohibited the production of Direct Messages sent within Twitter, other than between the two individuals who were parties to the litigation. The Court treated Twitter as a separate entity from the individuals who had Direct Messages within Twitter, because Twitter did not require its employees to use Direct Messages. The SCA prohibits the production of third-party electronic communications from electronic communication service providers (ECS). As Twitter is an ECS under the SCA, the Court adjudicated Twitter separately from users with privacy rights under the SCA.

Wildcard and Proximity Searches

The parties had one disagreement over a search term: “engag*”. This is a “wildcard” search in many review applications that would generate hits for the every word beginning with “engage.” The Defendants warned this term could have false-positive results that would include anything from “engagement parties” to political engagement.

The Defendants argued the term “engag*” needed to be within the proximity of five other keywords; the Plaintiffs countered that was too limiting. The Court acknowledged there was not a perfect solution and ordered the term “engag*” be searched within 10 words of other search terms.

Bow Tie Thoughts

The facts of a lawsuit matter in determining which custodians need to be included in the scope of discovery, reasonable search methodologies, and whether a party has actual control of data in the case. This case logically addressed and resolved all of these issues.

Proximity searches should be discussed  at Rule 26(f) conferences between the two parties. Parties frequently list hundreds of search terms for use, not taking into consideration that hundreds of “OR” searches could generate anywhere from tens of thousands to millions of hits. Searching for these terms within a certain distance of other terms, or used by specific individuals, can focus search hits on those that are reasonable for review.

Consider the following example: An “OR” search for the terms “gas,” “interest,” and “earnings.”  There are 276,597 hits.

This is an excessive amount of records to review from OR searches. However, if the search is refined to finding records that contain “gas” within twenty words of the phrase “interest earnings,” the number of hits drops to two.

Any party responding to discovery requests must demonstrate whether they had a reasonable process to find responsive data. Are two search hits reasonable? Yes, if the two hits prove the case. In most cases, however, two hits are not reasonable, and the proximity search should be expanded out to validate if there are any other possibly responsive hits.

Search terms should be tested, experimented with, and validated. This requires leveraging proximity searches and other advanced search strategies to find responsive data, instead of merely relying on lengthy “OR” searches. Thinking in big-picture terms can help legal teams avoid swimming in thousands of search hits and focus on finding the discovery that supports their case.

¹Shenwick v. Twitter, Inc., 2018 U.S. Dist. LEXIS 22676 (N.D. Cal. Feb. 7, 2018).

The post Who’s a Custodian, What’s Protected, and What Counts as Reasonable? Judge Sallie Kim’s Opinion on Shenwick v. Twitter appeared first on The Everlaw Blog.

What makes discovery relevant to a case? How can a court determine a time period that is both relevant and proportional to the needs of a case? A lawsuit involving a fire at a manufacturing plant turned the heat up on these issues.

In Ball Corp. v. Air Tech of Michigan, the Defendant was hired as an independent contractor to clean the Plaintiff’s industrial bake oven. A fire occurred allegedly eight hours after the Defendant cleaned the oven in the ductwork. The Defendants were sued for breach of contract and negligence for the alleged failure to clean the oven properly.¹

The Defendants claimed the Plaintiffs had an inadequate discovery production, improper objections, and a deficient privilege log. Magistrate Judge Andrew Rodovich reviewed each challenge in turn.

Determining the Scope for a Supplemental Production

The Defendants sought discovery for the service and maintenance activity on the oven for the ten years prior to the fire. The Plaintiffs objected to the ten-year time period and produced discovery covering only one year. The Defendants argued that the ten-year time period was relevant to establish a pattern of neglect that may have caused or contributed to the fire. According to deposition testimony by one of the Plaintiffs’ employees, the oven was cleaned once every six months and ductwork once a year. The operating manual stated the oven and ductwork should have been cleaned monthly.

The Court held that the Defendants’ request for discovery for ten years was relevant to the claims of the case. Moreover, any burden was outweighed by the value the information could provide under the proportionality analysis of Rule 26(b)(2), as the Plaintiffs had alleged $12 million in losses for the fire.

Privilege Log

The Defendants challenged the privilege log as having cryptic descriptions and the requesting party could not assess the Plaintiffs’ privilege claim.

Pursuant to Federal Rule of Civil Procedure 26(b)(5)(A), a privilege log for withheld discovery must 1) expressly make the privilege claim; and 2) describe the nature of the documents, communications, or tangible things not produced or disclosed—and do so in a manner that, without revealing information itself privileged or protected, will enable other parties to assess the claim.

The Court held that the Plaintiffs’ Privilege Log contained general descriptions that were deficient under the Rules. The Plaintiff was ordered to supplement their privilege log to comply with 26(b)(5)(A)(ii) that included the following information:

1. Name and job title or capacity of the author(s)/originator(s);
   
2. Names of all person(s) who received the document or a copy of it and their affiliation (if any) with the producing party;
   
3. General description of the document by type (e.g., letter, memorandum, report);
   
4. Date of the document; and
   
5. General description of the subject matter of the document.
   

The requirements stated for the new privilege log could all be created in Everlaw. Below are strategies for review in similar cases and creating a privilege log.

Reviewing Ten Years of Documents

Collecting ten years of email and related documents can pose challenges, because data might have been destroyed pursuant to a document retention policy. If the data does still exist going back a decade, it likely is in different forms of email, as companies update their email systems over time.

In the above case, the discovery at issue was the claim the oven was cleaned once every six months and ductwork once a year. A possible search strategy is to define the date range of the search (starting in 2007 or 2008 to the date of the fire). If there is a maintenance form that documents when the oven and ductwork were cleaned, there could be a search for that specific type of record. Depending on how the company does business, it is possible maintenance forms could be electronic, such as an Excel spreadsheet where work is logged, or a printed checklist with handwritten notes. If handwritten, those documents would need to be scanned in for review. The printed text can be made searchable, depending on the quality of the scan; however, handwritten notes would need to be read manually.

Searching ten years of email can have multiple custodians over that period of time. One strategy is to start with the defined date range with content searches for “oven” or “ductwork” within 10 to 20 words from “clean.” The term “oven” alone could have false-positive hits, given the nature of the Plaintiff’s business, so proximity searches are advisable. There could be other terms as well that could be developed after conducting interviews with custodians on terms of art used to describe cleaning the oven and ductwork.

Preparing a Privilege Log in Compliance with the Federal Rules of Civil Procedure

The producing party had specific requirements to meet from the Court order for their privilege log to comply with the Federal Rules of Civil Procedure. Half of the ordered information could be meet with the metadata of the records. The other fields would need to be user editable fields customized to meet the Court order.

Have a Discovery Checklist

Cases like Ball Corp. v. Air Tech of Michigan have discovery obligations seen in almost every lawsuit:

• What is the scope of discovery?
• What needs to be searched to respond to discovery?
• What are the privileges at issue?

One way to avoid any missteps in discovery is to have a defined checklist for attorneys to think about these issues at the beginning of a case. If a party knows the relevant privileges in case, document review strategies can be tailored to find privileged communications or documents. If editable user fields need to be customized to comply with the requirements of a court order, those fields can be created at the beginning of document review, so reviewing attorneys can provide the necessary information for the privilege log.

¹Ball Corp. v. Air Tech of Mich., 2018 U.S. Dist. LEXIS 19949 (N.D. Ind. Feb. 7, 2018).

The post Where’s the Fire? Attorneys Consider Issues of Relevance, Scope, and Privilege in Discovery appeared first on The Everlaw Blog.

If lawsuits are about people who have experienced a wrong, requests for production are one of the most important tools in Civil Procedure to get at the “truth” of what happened in a case. In Jones v. Standard Consulting & Standard Testing & Eng’g Co., a gender and age discrimination case, the Plaintiff was terminated by the Defendant and requested discovery on all employees who were supervised by the Defendant. The Plaintiff’s theory was that the Defendant treated female employees differently with regard to work hours. The Court agreed the Plaintiff had alleged a pattern of discrimination by the Defendant, and ordered a supplemental production that required the Defendant to review the files of 50 employees.¹

Using Predictive Coding to Find Responsive Discovery

There are a few tested strategies a producing party can use to identify the responsive discovery in their data. A producing party in a similar situation to the above defendant could leverage both search terms and predictive coding to find responsive discovery. The likely responsive information would be in termination documentation of employees, work schedules, and possible communications with female employees to show discrimination or retaliation.

1. Prior to starting document review, issue coding and a prediction model should be created for responsive discovery.
   
2. With the codes and the predictive coding model in place, the model can be trained by reviewing a small subset of documents.
   
3. Other reviewing attorneys can focus on termination notices given to employees through searches or even search term reports. Responsive records can be coded accordingly.
   

The above strategies can help identify responsive information for the review team to train the predictive coding system. Once a prediction model has had a sufficient amount of data reviewed, it can predict documents for attorney review.

Document Review with Predictive Coding

Courts recognize that responding parties are best suited to determine the most effective methodologies for producing discovery.² The issue, if there is a challenge to a production, is whether it is inadequate or manifestly unreasonable.

For attorneys conducting review with predictive coding, the goal is to have a reasonable process to comply with Federal Rule of Civil Procedure Rule 26(g). This requires validating the data that is being reviewed. For example, attorneys should conduct review of data that is predicted to be irrelevant, to confirm it is indeed irrelevant. Everlaw allows admins to check the historical performance of the prediction model, by comparing it to a set of “hold out” data that is used for validation. This allows attorneys to see how scores change over time, providing insight on the performance of the prediction model and review.

Document review attorneys should provide feedback on the documents they are reviewing. If the documents are very similar, lowering the relevancy scale to allow more documents for review could be advisable. This is to ensure there is not confirmation basis and ensure the results are not overly narrow for review.

Know What You are Looking For

The most effective way to respond to discovery requests is to know what you are looking for. This means knowing what data must be found for discovery, claims, or defenses in a lawsuit, in order to have a defensible discovery strategy. If a lawsuit involves discrimination over work schedules, then searches must be created to find that information. At the end of the day, lawsuits are about people. What happened to them? What are the relevant facts? Knowing the answers to those questions can help lawyers develop searches and predictive coding models to identify responsive information.

¹Jones v. Standard Consulting & Standard Testing & Eng’g Co., 2018 U.S. Dist. LEXIS 23835, at *6-7 (W.D. Okla. Feb. 14, 2018).
²See, Mortg. Resolution Servicing v. JPMorgan Chase Bank, N.A., 2017 U.S. Dist. LEXIS 78217, at *6 (S.D.N.Y. May 18, 2017), citing The Sedona Conference, The Sedona Principles: Second Edition, Best Practices Recommendations & Principles for Addressing Electronic Document Production, at ii princ. 6 (2007), http://www.thesedonaconference.org.

The post Predictive Coding Strategies to Identify Patterns of Discrimination appeared first on The Everlaw Blog.

Mark your calendars… on Wednesday, April 18th at 10:00am PDT, we’re hosting a webinar. This time, our VP of Security and Compliance Lisa Hawke will team up with CEO AJ Shankar to bring you:

“Bringing Dark Data to Light: Ethical Considerations and the Technology You Need Today.”

Potentially exculpatory photos in a social media feed. Critical corporate memos written in a language you don’t understand. The recording of a game-changing voicemail. Increasingly, the information you need to succeed in ediscovery is not created in a searchable format. Why is it important to know about different technologies that create and understand this “dark data?” How can you access dark data? Join us for a lively exploration of the sources of dark data, the best techniques for illuminating it, and the importance of technology competence for attorneys.

This session will explore…

• Specific examples of dark data, including:
  
  • Audio or video files (phone calls, meeting recordings)
    
  • Images (patent applications, schematics)
    
  • Foreign language (emails and more)
    
• Technology competence and risk mitigation for attorneys
  

Register today! If you can’t make the session time, register anyway and the recording will be available after the session.

The post April 18 Webinar: Bringing Dark Data to Light appeared first on The Everlaw Blog.