The ABC’s of EDA vs. ECA in Ediscovery – It’s as easy as 1,2,3, right?
Now, now, now, I’m gonna teach you
Teach you, teach you
All about love, dear, all about love
Sit yourself down, take a seat
All you gotta do is repeat after me
Abc, easy as 123
Or simple as do, re, mi
Abc, 123, baby, you and me
Michael Jackson, my childhood heartthrob (and okay, maybe my adult heartthrob, too), tried to teach us the ABC’s of love back in the 1970’s. According to MJ, love was as easy as ABC….right.
Just as love is more complicated than ABC, so is ediscovery – especially when it comes to early assessment of your data….or is it early assessment of your case? EDA v. ECA — Why does it seem that these two terms are used interchangeably in ediscovery circles? Is there really an important difference?
Understanding the Differences
The difference between EDA (early data assessment) and ECA (early case assessment) is subtle, but critically important. Whereas ECA involves the entire legal matter—before discovery and beyond data analysis—EDA is a smaller subset, isolated to discovery activities. For example, ECA encompasses fact finding, venue research, liability analysis, damage assessment, adversary investigation, and litigation budget forecasting. EDA, on the other hand, aids in fact-finding and narrows the scope of important data early on. During the process of EDA, data is separated between critical and non-critical groupings, the number of key players is narrowed, key search terms are tested, and critical case arguments are identified. A robust EDA strategy (especially if data volumes are immense) usually involves ediscovery technology – a platform with searching, foldering, clustering, topic grouping, email threading, and maybe even predictive coding (or TAR) capabilities. However, EDA is more than a technology—it is a methodology that involves people, processes, and the right technology. By using EDA, organizations tasked with the production of documents are able to drastically narrow immense fields of potentially relevant information into smaller, refined clusters of pertinent data. That data can then be feasibly analyzed with test search terms and other input parameters.
So, even though one small letter distinguishes EDA from ECA, the differences are significant to ediscovery practitioners. Early assessment of data volumes in a matter is as critical to the case strategy as ABC and definitely more complicated than 123.