E-discovery seismic shifts have become so common that we are often quick to forget what we haven't yet learned.

What I mean is that the e-discovery technical revolution throws so many new tools at us that we can fixate on them before we have fully understood and adopted previous, effective process improvements. Keyword searching is a classic example. With all the rush to the varieties of “predictive coding,” “computer assisted review” and machine learning, we have failed to get keyword searching right before promptly bidding it an abrupt “adieu.”

Of course, we can't really say goodbye to keyword searching; it remains a critical part of e-discovery. Just the same, keyword searching hadn't reached its fully deployed development before the e-discovery world moved on to what George Socha of Electronic Discovery Reference Model (EDRM) calls “the almost irresistible allure of the next bright and shiny ornament.”

What did we miss in getting keyword searching right before moving on? The story starts with the “good-old days” of document production before electronic discovery. Back then, after the receipt of a request for production, a round of motion practice would ensue over “general objections” that the document production requests were ambiguous and impossible to understand, overbroad and burdensome, and  irrelevant and not calculated to lead to the discovery of admissible evidence. Once these objections were resolved (and following some scant discussions about what documents should be included), the client would send the responsive paper documents to retained counsel. Retained counsel would then review, categorize, redact, stamp, number and deliver the documents to the opposition, with a modest photocopying bill attached.

This model followed retained counsel into the e-discovery era. The only problem is that the documents changed from plain-old paper to electronically stored information (ESI), giving rise to a host of complications—the new “see-it-only-on-a-screen” (ah, horrors) digital data didn't lend itself to the old procedures. Leaving aside for the moment the “structured data” in databases, most ESI is disorganized and voluminous, often saved and stored in a careless, chaotic manner that would have gotten any file clerk fired with extreme prejudice in the good old days. For example, email arrives, day in and day out, one after another. Hundreds of posts pile up in the inbox and outbox, week after week. Some of them may be placed in folders, and some “rules” may be applied, but the vast majority of potentially relevant emails sit in email boxes, mixed in with mostly non-relevant email. Other emails find their ways into multiple folders in multiple locations, heedless of their common subjects. In this new environment, retained counsel have to collect entire email mailboxes and folders, which then need to be laboriously searched for relevant email.

And in case you weren't already enjoying the process, here's where the fun and frustration begins. Search terms invariably identify a large number of non-responsive documents (poor precision) and miss a large number of responsive documents (poor recall). So while you're digging through a hundred folders, you have to wonder if countless documents in them have anything to do with the case as well as what important document you may have missed that somehow ended up in the “Cat Pictures” folder.

How did we make keyword searches better? The answer is multifaceted. Some efficacious solutions had already been deployed before we became transfixed, ogling the new big thing. For example, Boolean searching has been available to help exclude misidentified documents, while “near” and “within” search capabilities have helped capture phrases. There's also fuzzy logic already at your disposal to help identify misspelling, and stemming to catch words with the same core. In short, keyword search has been there, nicely functioning as an iterative process of refinement rather than a one-shot game of good guessing. Search terms have evolved into developed “search strings” and “search expressions.” Help was on the way and had arrived.

But before we mastered all of these utilities, predictive coding arrived on the heels of some interesting studies, which suggested that even though we thought keyword practice was improving, we still had a long way to go to beat the latest machine-learning algorithms. After all, “Watson” won at Jeopardy. In a flash, the e-discovery world was chasing the new “predictive coding” technologies as if they were robo-heroes and keyword searching a limping dinosaur. Don't get me wrong! These are wonderfully powerful technologies that have been increasingly deployed in significant cases, and they promise even more excitement for the everyday case in the near future. But they are only a few technologies in the big e-discovery toolbox, shiny and new as they may be.

Not to sound curmudgeonly, but what ever happened to that old war horse, the keyword search? Remember, we said that keyword search practice was really improving. Unfortunately, it got sidetracked just as the real capstone to key word searching was emerging: verification by statistical random sampling. As Craig Ball and others have often noted, you just can't look wide-eyed at the search results, you have to test them. Or as Reagan would have said, “Trust but verify.”

We know, we know: “But attorneys never did that in the good old days. Why now?”

E-discovery is different. We could only look at paper (one sheet at a time) in the good old days, and there really wasn't much of it compared to the now-normal gigabytes and terabytes of litigation data. Now, with all that data to sift through, we can only deploy random sampling rather than a brute physical audit. And because we are engaged in searching data (a.k.a “information retrieval”), with random sampling you can actually know with reasonable, legally defensible confidence that the responsive documents have been produced. Indeed, without random sampling, you cannot know with reasonable confidence if you are turning over non-responsive documents. And better yet, random sampling is immensely cost effective—due to the magic of statistical science, the number of randomly selected documents (in ESI speak, “files”) that need to be reviewed to obtain the desired confidence level tops out quickly and does not increase as the volume of data increases.

So let's complete the arrested development of keyword searching. We need the final chapter in its storied development. Statistically sample both your produced and non-produced documents. You might have gotten it right with a stab in the dark (which is, ahem, statistically unlikely), but do you want to take that chance? With statistical sampling, you can be reasonably confident that your productions are legally defensible. And then you can sleep easily at night. While you're at it, make sure the opposition statistically samples the culled documents they are not producing so you can be reasonably confident that you got all the goodies.

And you can even keep playing with the new predictive coding technologies, which, we note with some irony, depend on statistical sampling! We'll visit that in the next article.

E-discovery seismic shifts have become so common that we are often quick to forget what we haven't yet learned.

What I mean is that the e-discovery technical revolution throws so many new tools at us that we can fixate on them before we have fully understood and adopted previous, effective process improvements. Keyword searching is a classic example. With all the rush to the varieties of “predictive coding,” “computer assisted review” and machine learning, we have failed to get keyword searching right before promptly bidding it an abrupt “adieu.”

Of course, we can't really say goodbye to keyword searching; it remains a critical part of e-discovery. Just the same, keyword searching hadn't reached its fully deployed development before the e-discovery world moved on to what George Socha of Electronic Discovery Reference Model (EDRM) calls “the almost irresistible allure of the next bright and shiny ornament.”

What did we miss in getting keyword searching right before moving on? The story starts with the “good-old days” of document production before electronic discovery. Back then, after the receipt of a request for production, a round of motion practice would ensue over “general objections” that the document production requests were ambiguous and impossible to understand, overbroad and burdensome, and  irrelevant and not calculated to lead to the discovery of admissible evidence. Once these objections were resolved (and following some scant discussions about what documents should be included), the client would send the responsive paper documents to retained counsel. Retained counsel would then review, categorize, redact, stamp, number and deliver the documents to the opposition, with a modest photocopying bill attached.

This model followed retained counsel into the e-discovery era. The only problem is that the documents changed from plain-old paper to electronically stored information (ESI), giving rise to a host of complications—the new “see-it-only-on-a-screen” (ah, horrors) digital data didn't lend itself to the old procedures. Leaving aside for the moment the “structured data” in databases, most ESI is disorganized and voluminous, often saved and stored in a careless, chaotic manner that would have gotten any file clerk fired with extreme prejudice in the good old days. For example, email arrives, day in and day out, one after another. Hundreds of posts pile up in the inbox and outbox, week after week. Some of them may be placed in folders, and some “rules” may be applied, but the vast majority of potentially relevant emails sit in email boxes, mixed in with mostly non-relevant email. Other emails find their ways into multiple folders in multiple locations, heedless of their common subjects. In this new environment, retained counsel have to collect entire email mailboxes and folders, which then need to be laboriously searched for relevant email.

And in case you weren't already enjoying the process, here's where the fun and frustration begins. Search terms invariably identify a large number of non-responsive documents (poor precision) and miss a large number of responsive documents (poor recall). So while you're digging through a hundred folders, you have to wonder if countless documents in them have anything to do with the case as well as what important document you may have missed that somehow ended up in the “Cat Pictures” folder.

How did we make keyword searches better? The answer is multifaceted. Some efficacious solutions had already been deployed before we became transfixed, ogling the new big thing. For example, Boolean searching has been available to help exclude misidentified documents, while “near” and “within” search capabilities have helped capture phrases. There's also fuzzy logic already at your disposal to help identify misspelling, and stemming to catch words with the same core. In short, keyword search has been there, nicely functioning as an iterative process of refinement rather than a one-shot game of good guessing. Search terms have evolved into developed “search strings” and “search expressions.” Help was on the way and had arrived.

But before we mastered all of these utilities, predictive coding arrived on the heels of some interesting studies, which suggested that even though we thought keyword practice was improving, we still had a long way to go to beat the latest machine-learning algorithms. After all, “Watson” won at Jeopardy. In a flash, the e-discovery world was chasing the new “predictive coding” technologies as if they were robo-heroes and keyword searching a limping dinosaur. Don't get me wrong! These are wonderfully powerful technologies that have been increasingly deployed in significant cases, and they promise even more excitement for the everyday case in the near future. But they are only a few technologies in the big e-discovery toolbox, shiny and new as they may be.

Not to sound curmudgeonly, but what ever happened to that old war horse, the keyword search? Remember, we said that keyword search practice was really improving. Unfortunately, it got sidetracked just as the real capstone to key word searching was emerging: verification by statistical random sampling. As Craig Ball and others have often noted, you just can't look wide-eyed at the search results, you have to test them. Or as Reagan would have said, “Trust but verify.”

We know, we know: “But attorneys never did that in the good old days. Why now?”

E-discovery is different. We could only look at paper (one sheet at a time) in the good old days, and there really wasn't much of it compared to the now-normal gigabytes and terabytes of litigation data. Now, with all that data to sift through, we can only deploy random sampling rather than a brute physical audit. And because we are engaged in searching data (a.k.a “information retrieval”), with random sampling you can actually know with reasonable, legally defensible confidence that the responsive documents have been produced. Indeed, without random sampling, you cannot know with reasonable confidence if you are turning over non-responsive documents. And better yet, random sampling is immensely cost effective—due to the magic of statistical science, the number of randomly selected documents (in ESI speak, “files”) that need to be reviewed to obtain the desired confidence level tops out quickly and does not increase as the volume of data increases.

So let's complete the arrested development of keyword searching. We need the final chapter in its storied development. Statistically sample both your produced and non-produced documents. You might have gotten it right with a stab in the dark (which is, ahem, statistically unlikely), but do you want to take that chance? With statistical sampling, you can be reasonably confident that your productions are legally defensible. And then you can sleep easily at night. While you're at it, make sure the opposition statistically samples the culled documents they are not producing so you can be reasonably confident that you got all the goodies.

And you can even keep playing with the new predictive coding technologies, which, we note with some irony, depend on statistical sampling! We'll visit that in the next article.