Using technology to improve ECA outcomes

Businesses are creating data every day, from emails, word documents and spreadsheets, to social media posts. According to IBM, over 2.5 quintillion bytes of data are created every day. All this data is posing many challenges for businesses, including how to quickly and easily access and review mountains of data in the event of an investigation or litigation.

Making this even more burdensome is that data is generated from and stored on a variety of sources, many of which add to the complexity of retrieving data. Our mobile, ‘always on’ work lives mean that employees have relevant information on their mobile phones (both personal and office), in chat rooms, on shared and hard drives, and in the Cloud.

Since legal teams want to evaluate the merits of any litigation as quickly as possible, getting to the crux of the case is often very time sensitive. However, the discovery process can be extremely time consuming and costly. This is why many firms are now using early case assessment (ECA) to help determine whether the time and cost of litigation is worthwhile at a very early stage. Leveraging technology to make the ECA process more efficient can help companies deploy resources more proficiently, saving time and money on both eDiscovery costs and, potentially, unnecessary litigation.

“Litigation is time, money, and resource hungry. A framework to assess early resolution can help you get an early and accurate picture of your case.” Matthew Grant, Epiq

Early case assessment

ECA is an important part of a legal team’s toolkit. It allows you to get a feel for the fundamentals of the case, even with large volumes of data, in a well-organised and cost-effective manner. By employing a robust ECA process, you can reduce the time and money spent preparing for litigation.

ECA is exactly what its name implies – it is a process whereby lawyers take a first look at the information surrounding a case to try to establish not just the key issues in the matter, but how much relevant data there is, where it is stored, and what eDiscovery costs will look like if you proceed to litigate.

Beyond gaining and understanding of cost implications, ECA provides a way to help formulate a litigation strategy (or a path to settlement).

How does it work?

Like a full eDiscovery project, ECA begins with data collection, and its efficacy hinges on the quality of that data collection. The first step, as with any in-depth eDiscovery exercise, is to collect the data in a defensible way that ensures, among other things, that key metadata is preserved. Self-collection may seem tempting, but it requires the right tools and the right training to ensure it is done correctly. If data is not collected properly, even for an exercise as quick and dirty as ECA, it can cost time and money in the long run as data will need to be recollected or run the risk of not being admitted into evidence.

Using (or at least getting advice from) a third-party provider with expertise handling a wide array of data types from a wide variety of sources helps to ensure that you collect information in an efficient, effective, and defensible manner, reducing the risk of spoliation or the need for recollection.

For example – some email archiving tools use ‘stubs’ to reduce inbox sizes and storage costs. A stub contains only the subject line and a small sample of the email’s content, with the full content and attachments stored in another location. If you simply copy the inboxes of the relevant parties in the matter, you may end up with stubs rather than full emails. Further, the stubbing process may have altered the email’s metadata, resulting in changes to key information such as the date the email was sent or received.

New advances in ECA

The advent of technology-assisted review (TAR) brought with it many advances in the world of eDiscovery. TAR became widely used as a tool for ECA, but it came with flaws. A traditional TAR exercise often meant an expensive, time-consuming up-front review to train the TAR system before you can begin your analysis.

More recently, continuous active learning (aka CAL or TAR 2.0) has improved traditional predictive coding outcomes. This version of TAR gets to the key data faster because it requires only a few sample documents in order to begin identifying relevant data. CAL systems push the most relevant documents to the front of a review queue where the legal team tag them for relevance. The lawyers’ decisions are fed back into the tool, which learns from them, and iteratively refines its ability to identify the most important documents in the document population.

The results are that relevant documents are identified and reviewed earlier, faster, and with fewer document reviewers. The abundance of early information allows counsel to get to the most important documents quickly, which can help in the overall strategy of the litigation.

Seeing into the data

Next-generation analytics tools do more than just predictive coding. Along with standard features such as document categorisation and clustering, they have additional capabilities that can give insight into the data’s content. Communications analysis, for example, can show who is talking to whom, and about what, during particular periods of interest in a matter. Sentiment analysis can provide insight into the tone of emails and documents – pressure, fear, suspicion.

The ability to understand your data in more detail than ever before, and more quickly than before, helps you more effectively assess the facts of your case.

ECA in action

A large global financial services firm needed to identify whether a case was worth pursuing via litigation. Using the concept-searching feature of their analytics tool, the legal team was able to quickly identify the matter’s key documents and, as a result, flesh out their understanding of the key issues in the dispute. This approach was combined with CAL to iteratively push documents identified by concept-searching to the legal team, who reviewed the documents and fed relevancy decisions back into the review tool. Using this ECA strategy, the team was able to advise the client on the case’s merits and the recommended strategy much sooner and more effectively than using a traditional approach.

In another case, a large Japanese company needed to identify key documents across several multi-language document populations in three countries. Using CAL, the legal team was able to access and evaluate the documents quickly, and advise the client on the recommended case strategy. This reduced the overall cost of the project significantly and enabled the clients to make fully informed decisions they might otherwise not have been able to make.

Bringing it all together

Litigation is time, money, and resource hungry. A framework to assess early resolution using next-generation technology can help you get an early and accurate picture of your case and its merits, helping you identify pathways to resolution, and save significant costs.