Sky Discovery will help arrange and power through your data faster with a complete suite of text analytics features, including categorisation, key word expansion, concept searching, clustering, include similar documents and computer-assisted review. Analytics and predictive coding is revolutionary technology completely disrupting the legal review space.
Key word expansion
Identifying the correct key words to search on is a critical part of any case. Through keyword expansion, text analytics gives you investigative power to widen searches and pull in more relevant documents sooner in the review process by teasing relevant terms out of key documents.
Search on keywords of your choice from any document, and from those terms text analytics can provide a list of terms that are conceptually related based on the unique language in your data set.
By finding related terms from other documents, you can discover unexpected or hidden words, such as project code names and company or industry jargon, and ensure you aren’t overlooking anything important to your case.
Words that hold very similar meanings, such as “cold” and “frigid,” or words with multiple meanings, such as “leaves,” can skew results of traditional keyword searches and slow down the review process. Concept searching is another text analytics feature that helps overcome obstacles in standard searching techniques.
Concept searching goes beyond keywords to find documents based on ideas rather than specific terms. This allows you to identify important documents and follow an investigatory pattern, locating relevant documents even without knowledge of the specific terms, phrases, jargon, or code words that may be used in other documents.
Categorisation allows subject matter experts to automatically group unreviewed documents into categories they define themselves with the issues coded in a small manually reviewed set. You can also use categorisation to determine if documents are most likely to be responsive or non-responsive
Include similar documents
As a valuable QA tool before sending out a discovery, this feature can assist locating privilege documents and prevent them from being inadvertently disclosed. Relativity can learn the concept of a privilege document by analysing the human identified privileged documents, and then reviewing proposed discovery. Analytics will identify suspected privileged documents can then be manually reviewed before disclosure.
Text analytics helps get the most important groups of documents to review teams as soon as possible and batch documents by conceptual similarity for faster, more consistent coding. With a feature called clustering, you can organise and prioritise your review much earlier in a case.
Clustering automatically identifies and groups documents with similar concepts. It labels those groups by the most prevalent ideas in each one and visually represents how the groups relate to one another. Unlike a concept search, the user provides no input as to what they’re looking for—there’s no need for subject matter experts to identify example documents.
Computer-assisted review helps you accelerate your review process by amplifying your team’s efforts across any substantial document set. Text analytics (categorisation) is one of the three key elements of computer-assisted review, which also includes statistical validation and, most importantly, actual humans.
In computer-assisted review, senior reviewers code documents in the system in the form of seed sets, and the system applies their decisions to the rest of the document universe through an iterative workflow managed by the review team. The end result is a less costly and tedious eDiscovery experience.