Early case assessment (ECA) is defined as the practice of estimating the risk to prosecute or defend a legal case. Organizations will often spend significant cost, time, and money on a case only to find they need to settle the case unfavorably after the cost or exposure becomes too burdensome. Much of that cost is directly related to e-discovery.
Organizations will often spend significant cost, time, and money on a case only to find they need to settle the case unfavorably after the cost or exposure becomes too burdensome. Much of that cost is directly related to e-discovery. In a broader sense, ECA can be thought of as the process of gaining insights about the strength of legal positions, and potentially relevant issues, witnesses, custodians and evidence as soon as a legal matter surfaces. In some instances, it has been described as “conducting discovery prior to a formal discovery context.”
More than 90 percent of all cases settle prior to trial. In federal cases, this number is closer to 98% . Therefore, discovery has become a de facto form of dispute resolution in U.S. courts. That is why the early phases of discovery are arguably the most important of any legal battle or dispute. This is when you identify whether or not your side will likely be able to proceed with an action or if it will be more cost-effective to pursue a settlement or alternative resolution.
The initial phase of discovery is the time when you will have to determine the evidence that will need to be collected, the custodians who will be interviewed, and the cost, complexity and challenges your team will face. Early case assessment provides a foundation for creating overall case strategies. Considerations include identifying the trigger event of litigation, the facts of the case, value of the damages, the capability of your opponent, your judge’s sophistication in e-discovery and history of imposition of sanctions, date ranges, keywords, case merit, risk analysis and many other considerations.
We will address the basics of early case assessment in this chapter, but first, let’s discuss two important recent trends which are changing the practice of ECA.
Traditional linear review is giving way to an analytics-first workflow. That means legal teams can increasingly rely on software to understand case data early through analysis and intelligent culling, rather than old-fashioned manual review, where documents had to be reviewed one at a time.
For example, the use of visual analytic technology is helping lawyers find relevant information by illustrating relationships and communications patterns. Clustering technology helps group data into categories so that legal teams can identify where relevant information might be found. In Logikcull, culling filters that are akin to those used by Amazon.com are use to whittle down large amounts of data, so, for example, users can hone in on just emails to and from certain domains, or just files above a certain size. This is similar to how online shoppers search for products by identifying a size, a make, a brand and so forth, so they are not looking at thousands of available options one at a time, but a targeted subset instead.
In terms of visual analytics, Logikcull automatically provides “infographics” that create a high-level timeline showing when important communications have been created and sent.
In addition, analytic tools can identify how custodians are talking about their work, which words are important, and what unexpected terms might be useful search topics. This information can help create a simple word list from a collection of emails and documents that can be used to find smoking gun documents and evidence later.
Some of the cost drivers are: volume, scope, timing, risk tolerance, and the level of open communication with the opposition.
In order to control costs, consider the principles of proportionality to set a budget proportional to the case. The producing party should calculate the maximum amount of money appropriate to spend proportional to the potential financial awards or cost containment likely to come from the case.
The following practices will not apply to all ECA efforts, but these are all best practices to manage the ECA process and avoid legal pitfalls:
The next factor that is changing how ECA is employed is changes to the Federal Rules of Civil Procedure on proportionality. As mentioned in Chapter 1 , Federal Rules of Civil Procedure 26(b)(1) was rewritten to limit discovery to that which is “proportional to the needs of the case” and provided five factors for courts to consider.
The old rule 26(b)(2)(C)(iii) was clear that a court could limit discovery when the burden outweighed benefit. However, new Rule 26(b)(1), implemented by the December 1, 2015 amendments , takes the factors in these old requirements and puts them at the heart of any discussion about the scope of discovery.
In addition, Rule 26(b)(1) eliminates the phrase “reasonably calculated to lead to the discovery of admissible evidence.” This means that requesting parties can no longer assume they can obtain discovery of virtually anything that’s “reasonably calculated” to be helpful in litigation. According to the rules, the factors parties must address as they relate to proportionality include the:
In sum, under the new rules, lawyers now have to provide more details and more information in order to make a claim that discovery is proportional to the needs of a case. This means lawyers must dispense with vague, broad claims or objections and rigorously vet proportionality factors in order to control the scope of discovery.
In practical terms, this may entail providing:
Given the exploding volumes of information subject to discovery, ECA tools are essential for providing insight into the data. Technology allows legal teams and investigators to filter, search and perform different types of analysis prior to fully processing data.
Some ECA tools ingest native files and the metadata associated with the native files and thus do not require processing to permit analysis. Other “full service” solutions, like Logikcull, have the ECA function built into their core framework.
Choose technology that works with your data collections. For example, a project that contains numerous spreadsheets would likely drive a decision to use a review platform appropriate for reviewing spreadsheets, or a tool that can natively ingest and analyze all types of common files.
One important trend to be aware of is the rise of in-place ECA applications. In traditional ECA, litigators must first collect all potentially relevant ESI at the outset of a legal matter and try to assess its potential relevance. This is disruptive to business operations and captures a lot of duplicative and useless data. In-place ECA applications assess the available data as it is on the network, leaving it in place during the initial review. This allows for more targeted and thoughtful collections if data is deemed relevant, and less business disruption.
Careful analysis and review will allow parties to identify and eliminate inessential data so that it doesn’t become a burden later on. Tactics for eliminating useless data, collectively termed “defensible deletion,” include:
Of course, your team should always document and record the use of these technologies so that if opposing counsel questions why potential evidence was deleted, documentation exists which can support your actions. Legal Intelligence tools like Logikcull automatically create audit logs of all user actions that have been taken within the system so they are defensibly recorded.
Early data assessment is similar to ECA, and the two terms are often used interchangeably. Early data assessment allows you to know what your data looks like before you process it, and gives insights into the scope of the project and its costs.
Often, the most important part of this process is the selection and testing of keywords. Keywords are the words or phrases you will use to search data collections and identify potentially relevant documents, whether they are applied as basic searches or in a more predictive or analytical context.
In its most basic application, early data assessment may mean searching your available data collections to see if potentially relevant documents hit on selected keywords. If no documents are found, refine your search until better results are found. However, if the results continue to be disappointing, it may also indicate that there is no evidence to support the claims in a case, or that rudimentary keyword searches are insufficient to properly assess the available data.
Once accurate keywords have been developed and you have a bearing on the scope of the potentially relevant information, take the time to count your potential custodians and estimate the volume of evidence for each. This is also the best point to evaluate how much time and effort a discovery project will take, as time periods can be different for each custodian and source.
The Early Case Assessment process is iterative, meaning actions must be repeated until your team feels it has a complete picture of the digital landscape. The total universe of potentially collectible ESI will usually be defined during the process of formulating the internal preservation directive/litigation hold. The universe usually consists of four main categories of data locations: Individual employee files, department/group files, enterprise databases, and, potentially, backup media. Not all data identified for preservation needs to be collected right away. Some data may never need to be collected. Collecting all data that has been preserved may unnecessarily inflate costs and overwhelm the legal team with irrelevant data. Once the timing of collection from a data location has been decided, the legal team must assess what level of forensic defensibility should be employed for the collection.
Important steps include:
The ability to defensibly remove immaterial information from business and IT systems is largely dependent on corporate policy. Most organizations have record management policies which describe actions to be taken by the client to preserve data, especially in the case of a legal event. However, even the best record management policies fail if no one enforces or tracks the progress. For example, companies often fail to ensure that potentially relevant information is not improperly deleted or re-purposed when employees leave the organization.
Consult records management policies which determine how long different classes of information should be retained before deletion. In some cases, you can meet with Information Governance Committees, risk management, compliance, legal, I.T. and various individuals who oversee data management.
When identifying potentially relevant ESI, use data maps or data surveys of IT systems, conduct periodic surveys of an organization’s digital landscape and document who has access to which records and where the records reside. Data maps list the IT systems used by employees, onsite and offsite, and paper document storage locations, including backup media that may contain responsive documents or data.
When surveying the organization’s IT systems and data, it is essential to determine:
Bute always remember that while organization charts and data maps are valuable resources, data maps can get out of date very quickly. Ensure a data map is verified with the IT staff you interview.