How To Reduce Ediscovery Expenses During Review

It’s impossible to overlook the transformative effects that widespread digitization continues to have on the landscape of legal work—in particular, our relationship to information. However,  the evolution of technology and its impact on legal work are creating a significant financial strain on legal teams and organizations. It’s not uncommon, for example, for ediscovery costs to make up the majority of litigation costs, with 75% of those funds used on review-related tasks. As the amount of discoverable information grows, ediscovery expenses can increase apace, unless properly controlled.

Before legal teams address the complexity of modern data, they first need to confront the increasingly high volume of electronic information that’s sure to be associated with almost any case. While each matter is different, teams often have an obligation to review anywhere from hundreds to millions of documents, which can be a considerable challenge for even the most capable and sizable operations.

Four Strategies to Reduce Ediscovery Review Costs

To reduce the volume of documents requiring review, administrators and their teams can rely on the following strategies:

1. Reduce Review Costs at the Onset Through Early Case Assessment

Early case assessment, or ECA, refers to certain tools and methods used by legal teams to reduce data volume before the reviewal process even begins. By gaining critical insights into the nature of the data, identifying and eliminating duplicate documents, and optimizing the ability to search for relevant information using keywords, legal teams can simultaneously cut costs on data storage and make the actual review process more efficient. 

Traditionally, ECA tools utilize an “in-out” strategy. First, data goes into a processing tool for extraction, deduplication, and deNISTing (a specific method for eliminating superfluous data based on file type). The “in-out” strategy allows legal teams to search through text and metadata more easily based on keywords and, ultimately, to cull or pull out any documents that likely won’t be useful to the case. Once the entire ECA process is complete, the culled data will be stored separately so that it reduces the volume of documents in the review space but can still be accessed as needed. 

Ediscovery platforms, such as Everlaw, build on the traditional ECA strategy in two critical ways. 

First, in addition to text and metadata, our processing tool also extracts the images and native files associated with documents. Images and native files often provide much more intuitive insights into the data contained in the ECA space, reducing the level of technical know-how necessary to navigate the data pool or understand the complexities associated with metadata. 

Secondly, instead of housing culled documents in a siloed location, Everlaw’s ECA space is directly linked to the review space so that anyone with access to both can move easily between the two, promoting and demoting documents efficiently based on their immediate usefulness.

11 Ways to Reduce Ediscovery Expenses
In this white paper, we detail 11 practical and distinct techniques that can help legal professionals cut document review costs. In particular, we focus on three core areas: reviewing fewer documents, optimizing review processes for efficiency, and reducing risks with built-in automation.

2. Utilize Passive Learning Tools Through Data Visualization and Concept Clustering 

Taking advantage of passive learning technologies is another reliable way to reduce the number of documents requiring review. While the phrase “passive learning” is an umbrella term that can refer to a variety of digital solutions, for our purposes it simply refers to any method or tool that can provide an overview of documents without the need for any user input. For example, we utilize two specific methods to help legal teams passively identify irrelevant data and focus only on the documents that matter: data visualization and concept clustering

Data visualization can be used to produce an illustration of text and metadata once the documents have been collected and processed. The data is visualized along several key dimensions, including dates, file types, custodians, subject lines, and correspondence fields. 

This allows legal teams to identify certain patterns in the data pool, such as recurring dates, subject lines, and recipients, without needing to manually review the data. Additionally, any irrelevant data can be promptly removed from the review set, and the process can even be automated to prevent similarly irrelevant data from showing up later on. 

When utilized to its full potential, data visualization can save legal teams hours, or even days, on first-level review work. 

Document clustering, or “concept clustering,” helps legal teams identify patterns that aren’t as easily revealed through the simple visualization of metadata. Through the use of modern algorithms, large document collections can be parsed and automatically sorted into “clusters” based on higher-level themes found in the data’s text and metadata. 

For example, one cluster might contain documents related to environmental research, whereas another might be dedicated to office management. 

Through this process, legal teams can easily identify both relevant and irrelevant themes and prioritize the corresponding data accordingly. 

3. Determine Key Terms Using Search Term Reports

By the time documents have been collected, parties often have an initial sense of which words or phrases will be central to a case. Once the passive learning tools clarify the nature of the data, legal professionals can narrow down document review obligations by creating a series of dynamic search term reports. These reports help legal teams zero in on which terms bring back a disproportionate number of documents and where legal professionals should focus during review. 

Teams that have the ability to produce thorough, far-reaching search term reports in a timely manner can end up with a significant advantage, and in some cases, the results can lead to a renegotiation of terms to avoid overly burdensome review obligations. 

With platforms like Everlaw, search term reports are automated and can be updated with the click of a button to include new terms as the case progresses, significantly reducing the time administrators spend on managing new searches. The use of automation allows our reports to yield results instantaneously, neatly organized based on hits per term, or based on a more targeted approach, such as specific hit counts related to an individual document.

4. Identify the Most Inclusive Emails

Since emails are a primary form of communication today, they often contain invaluable information that can help legal teams develop a case. While a single email has the entirety of the historical conversation (in the form of a “thread”) embedded in it, many of the individual messages contained in the thread will be of little to no use. 

By identifying the “most inclusive” email within each thread (i.e., the message containing the most relevant information), legal teams can eliminate the need to read each message more than once, reducing the volume of emails necessary to review by upwards of 30%.

With ediscovery platforms, legal teams can automate searches to only include the most inclusive emails from the outset of the process while maintaining the ability to review the less inclusive documents at a later time if necessary. Even when conversations branch off into separate threads—which happens frequently—users can customize to identify and organize multiple related “thread groups” at no additional cost. 

Turning Data From a Burden to an Advantage

Today’s legal teams aren’t powerless in the face of rising data sizes.

With the proper tools and well-executed strategies, litigators can not only control costs, they can turn otherwise overwhelming amounts of data into a strategic advantage, uncovering key documents, illuminating previously unseen connections, and developing an understanding of their case that otherwise would have been nearly impossible just years ago.