Basics of E-Discovery

What Is Early Case Assessment?

Early case assessment (ECA) is the process of attempting to quickly surface key electronically stored information (ESI), paper documents, and other potential evidence early on in a legal matter. The data gathered during early case assessment is then used to help estimate risk and guide case strategy, such as decisions to go to trial or settle.

An effective ECA strategy seeks answers to questions such as:

• What are the key issues in the legal matter?
• What keywords and search terms are relevant to those issues?
• How much potentially relevant data do I have?
• What are the potential e-discovery costs related to the complaint?
• What is my overall legal liability?
• Are there any key documents that must be accounted for?

When you have answers to these questions, it's much easier to determine whether to seek an early settlement, proceed to trial, or attempt to adjust e-discovery parameters so they are less costly and burdensome.

It's important to note that ECA comprises much more than simply examining potentially relevant electronically stored information (ESI), a component of ECA sometimes referred to as early data assessment (EDA). ECA is comprehensive evaluation of legal liability and potential costs at the outset of a case. In addition to looking at the relevant data, ECA will include comparing matters against similar past matters, decisions about what counsel to retain, and looking at previous court rulings to assess the viability of a matter.

Seeing as this is an e-discovery guide, we are going to focus here on the data elements of ECA. Keep in mind, e-discovery costs typically consume by far the biggest chunk of the litigation budget. Thus, e-discovery considerations figure very prominently into the overall ECA equation.

You'll notice that ECA is not recognized as an actual stage in the Electronic Discovery Reference Model (EDRM). That's because ECA usually spans multiple e-discovery stages and includes both process-centric (left side of EDRM) and data-centric (right side of EDRM) activities. Another way to think about it is that ECA activities run concurrent to other e-discovery processes, such as identification, preservation, and collection.

What does ECA Involve?

It's one thing to determine that a case is big and involves a lot of data and custodians, and will surely cost a lot of money. Most lawyers can make that assessment just by looking at the basic facts of the legal matter. It's another thing to attach real figures and data to those projections. The more precise the estimates, the more confidently you can set the right litigation course. So, how do you get there? There are a variety of things that go into an effective ECA process, including:

Custodian Interviews

Communicating directly with custodians (employees in possession of potentially relevant ESI) is a good way to get an idea of matter scope. Interviews help you determine who is involved in the legal matter, where and how much data is involved, and whether there are any especially important documents that could ultimately swing the litigation one way or the other. You likely won't come away from custodian interviews with any hard numbers. But you should have a great starting point from which to proceed to a more detailed analysis.

Data Analytics

A major component of ECA, data analytics turn raw data (think thousands of documents) into useful information. It goes without saying that legal teams don't have the luxury to go through every single document in formulating their case strategy. Instead, they can use data analytics to generate reports that capture data volumes, custodian names, date ranges, email domains, key concepts, and other insights into large data sets. Analytics answer quantitative questions (how much stuff is out there?) as well as qualitative ones (is there a smoking gun?). The data analysis and reports can be used by counsel to make informed decisions regarding the best course of action.

Data Sampling

Another way you can analyze large data sets is through data sampling. Data sampling assumes that by examining a representative subset of documents, you are able to draw accurate conclusions about the entire data population. In the context of e-discovery, that translates to examining a small set of documents for relevancy in order to determine how many relevant documents might exist across the entire data corpus. Accurate data sampling addresses concerns over sample sizes, confidence rates, and margins of error that we won't cover here. . What's important to remember is that when done effectively, data sampling can be a very effective e-discovery tool.

Reporting

Reports help you make sense of everything. They make it easier to digest key figures, trends, and other actionable information. For example, a report might reveal that e-discovery costs and burdens far outweigh the monetary value of the case, a fact that might be missed looking at the data at a more granular level. Plus, reports are easy to share and export-friendly--helpful when you need to turn them over to a judge or opposing party. They also provide a historical record of activities for after-the-fact review and analysis.

ECA Best Practices

An effective ECA process is dependent on a variety of factors that stretch from one end of an e-discovery project to the other. Holes in the e-discovery workflow or areas of inefficiency directly impact the efficacy of your ECA activities. Some best practices to keep in mind that will directly impact the ECA process include:

Create a defensible deletion strategy

A big part of ECA involves proactively interacting with the potentially relevant evidence of the legal matter. That's a more feasible task when there is less data to sift through in the first place. Your ECA process will be much more efficient and effective if you have a defensible deletion strategy in place that limits the amount of redundant, obsolete, and trivial data stored across your enterprise. Learn more about defensible deletion by reading the recent article, "Legal Considerations for Defensible Deletion Practices."

Involve IT early and often

Just because ECA revolves around case strategy doesn't mean IT doesn't have an important role to play. In order to perform the necessary data analysis, legal has to understand the data environment, (e,g., how much data resides on certain systems and how easily data can be accessed). That's IT's bread and butter. We even created a checklist dedicated to IT reporting for legal which covers key ECA considerations.

Developing strong preservation procedures

We have an entire section dedicated to preservation, but it's applicable here as well. In order to perform the necessary ECA data analysis you have to be confident that potentially relevant data is being adequately preserved. If there are holes in your preservation process any delay in going out and collecting could result in spoliation and render your ECA efforts futile.

Communicate and cooperate with opposing counsel

FRCP 26(f) “meet and confer" conferences provide a good model for limiting over-collections and cost overruns. Once you've gathered a preliminary view of the evidence during your ECA activities, it's usually a good idea to meet with opposing counsel to discuss what was found and what was not, disclose any anticipated technological hurdles, and get a sense of where the other side may be willing to compromise. Even if your discussions don't go anywhere, the judge will appreciate your efforts to cooperate! 

Collect in phases

As discussed earlier, one of the goals of in-place ECA is to limit the amount of data that is collected. One way to accomplish this is adopting a phased collection approach. Early collection efforts should be focused around the most relevant items, such as contracts, email, ESI most at risk of being lost, CRM systems, instant messaging, and other data sources. Going through this initial process of pursuing "low-hanging fruit" will often be sufficient as you examine the other potentially relevant evidence and determine what else might need to be collected.

ECA Tools

Because ECA is so intertwined with other e-discovery phases, we could list a bevy of tools here from legal hold automation systems to managed review platforms that all play a role in ECA. However, we'll focus on a few specific technologies that get to the heart of ECA activities.

Automated Interview Systems

We described the importance of custodian interviews earlier. Legal teams often rely on email to send questionnaires and spreadsheets to conduct custodian interviews and record responses, or they may rely on free online survey applications. This is not ideal. Online, generic survey applications can be somewhat restrictive in the design of the interview and are typically unable to automate follow-up communications, such as reminder emails, which are necessary to achieve desired response rates. They also require the manual transfer of the interview responses into a format that can be easily accessed, revisited, and compared alongside other survey responses. A better option is a dedicated interview application that has built-in workflows to ensure all steps in the process are performed in a logical, automated sequence with reporting that allows legal teams to quickly collect information and easily act on it.

Pre-Collection Analytics

In-place ECA relies on the ability to analyze data in its native environment. While they aren't as ubiquitous as some e-discovery technologies, pre-collection analytics tools crawl data sources and deliver basic insights like document volumes and can also perform more advanced searching and filtering to really hone in on relevant content. These technologies also allow you to flag and label documents, which helps support collaboration.

Artificial Intelligence (AI)

Even those who work on the periphery of e-discovery have likely heard of predictive coding and other AI applications.. Unfortunately, many have pigeon-holed these cutting edge technologies as strictly e-discovery review tools, when in fact predictive analytics can be used prior to review, even in advance of document collection. Rather than relying on specific search methodologies, predictive technologies and deep learning technologies apply advanced algorithms to categorize responsive documents with a level of efficiency that exceeds even what humans are able to accomplish reviewing documents manually. This ability is based on the system's ability to learn what qualifies as a responsive or non-responsive document for a particular case and apply that learning to an entire document set.

As we've emphasized throughout this section, ECA isn't an isolated e-discovery stage, but rather a process that spans across the full e-discovery lifecycle. Nowhere is the impact of an effective ECA process more evident than in the collection phase, which happens to be the next section of the guide.