Analytics vs. Technology Assisted Review (TAR)

Legal technology evolves quickly. It’s hard to keep up with all the advancements. Document review is one area where technology is moving at a rapid pace. The debate now is Advanced Analytics vs. Technology Assisted Review (TAR). Twenty years ago, document review meant reading piles of paper to find relevant documents. Today, the process is much smarter, but the goal remains the same. Electronic discovery (eDiscovery) has become more complex with the rise of Big Data.

Advanced Analytics includes several key functions. Near-duplicate detection and email threading are two popular ones. With near-duplicate detection, the software compares documents. If two documents are identical, it removes one based on their hash values. But if one document is a PDF and the other a Word file, the hash value won’t recognize them as duplicates. Analytics can detect this by recognizing the words in the document. This helps reviewers avoid reading the same email over and over with only a few word changes. Email threading allows reviewers to view an entire email chain at once and code it with one click. This saves time and reduces reviewer fatigue.

Another valuable feature is clustering. This organizes documents into related groups. For example, if a set of documents is about “fantasy football” but your case is about “electrical engineering,” you can prioritize the engineering-related documents. You can also batch documents based on reviewer expertise. If a reviewer specializes in economics, you can assign them stock market-related documents for efficiency and accuracy.

Concept searching is also a game changer. Instead of searching for keywords, you can search for concepts. For example, searching for “confidential information” will return documents related to privileged or hidden data. Analytics organizes and prioritizes data. TAR focuses on coding documents.

TAR works by having a reviewer code a sample set of documents for a specific issue. After the sample is coded, the software uses that sample to code the remaining documents. TAR software “learns” from the review process. As the reviewer codes more documents, the software improves and predicts how to code the rest of the data. The goal is to review enough documents to create a sample that enables the software to code the remaining documents accurately. TAR software includes fail-safes to alert the reviewer when this goal is met. This saves time and reduces costs.

So, what’s the difference between Advanced Analytics and TAR? Analytics help organize, prioritize, and sift through data. Reviewers no longer have to read irrelevant documents. Analytics do not carry the stigma of “a computer doing my thinking,” unlike TAR. However, both technologies enhance the review process.

eDiscovery is evolving rapidly, and the Analytics vs. TAR debate continues to grow. As technology advances, the question is how to best use it. Embracing TAR may start with using analytics. By doing so, you can reduce risks and improve client satisfaction. The choice between the two depends on your goals. Once you define your goals, you can select the best technology or method for your review process. To learn more about improving eDiscovery efficiency while lowering costs, contact us!