Analysis, which takes place on hosted data, allows evaluation of content and context, including the identification of key patterns, topics, people, or discussions. In essence, the analysis phase is where your data and strategy intersect. Consequently, by whatever technology or approach you employ, the better your analysis the stronger your case. Analytic methodologies may be used for:
- Reducing data volume
- Zeroing in on documents of interest
- Understanding what the data contain to assist in the producer’s own strategic decisions
- Gauging the productivity of reviewers
- Helping in identifying other potential sources of responsive ESI
- Documenting what took place during collection and processing
The marketplace is awash with add-on systems offering “key integrations” designed to reduce, analyze, report and map your data, but the two most effective and worry-free tools remain 1) using the out-of-the-box search capabilities within your instance of Relativity and/or 2) applying advanced Relativity Analytics to your data.
Standard Analysis in Relativity
The ability to search and cull hosted data in Relativity’s out-of-the-box package is, in fact, highly adaptable and capable of capturing all relevant information pertaining to a matter. That’s right, you don’t need anything more than what is provided in Relativity to gather data and build a rock solid foundation for your side of a matter. The only actual requirements for the most effective application of Relativity’s search tools are 1) a predetermined case strategy and 2) a skilled Relativity user.
An adept Relativity user can perform a range of searches from filtering on fields and simple keyword searches to the development of complex queries to effortlessly reduce the amount of data for review while ensuring the case strategy remains in tact without missing relevant documents in the process. A standard instance of Relativity provides the following implements allowing proficient users to narrow searches for even the most finite results:
- Filters: When users enable the filters for an item list, users can set criteria on single or multiple fields so that only matching documents or items appear in the view. Filters query across the searchable set of documents in the active view to return your results. Relativity supports multiple filter types so that users can easily choose the best format for different field types.
- Keyword searches: With these searches, users can leverage the basic functionality for querying the index populated with data from extracted text fields. The keyword search engine supports the use of Boolean operators and wildcards.
- Saved searches: These searches provide users with the functionality to define and store queries for repeated use. With flexible settings, users can create a saved search based on any Relativity search engine, assign security permissions to it, and define specific columns to display your search results.
- dtSearches: Available on the Documents tab, users can use the advanced searching functionality to run queries with proximity, stemming, and fuzziness operators, as well as with basic features such as Boolean operators and wildcards.
Advanced Relativity Analytics Features
Relativity Analytics, on the other hand, adds a steroid boost to Relativity’s standard search capabilities by supporting conceptual searching of unstructured text. Unlike the out-of-the-box search capabilities of Relativity, Relativity Analytics does not require a predetermined strategy or even adept users.
Essentially, what analytics technology does is find themes, recurring words, phrases or concepts automatically, without any keyword input. It presents a highly navigable framework that enables lawyers to see what lawyers may not have considered (or had any reason to consider) at the beginning of the e-discovery process. Analytics can rapidly classify and sort data, as well as identify relevant documents that other searching techniques might overlook. You can use Relativity Analytics to perform the following searching techniques:
- Conceptual Term Searching: Returns documents that contain concepts similar to user’s search terms or phrases.
- Keyword Expansion: Returns keywords that conceptually match user’s search term.
- Categorization: Uses a set of example documents as the basis for categorizing other conceptually similar documents.
- Clustering: Identifies and groups conceptually similar documents in a workspace using an existing Relativity Analytics index. It does not require a training or example set of documents.
- Find Similar Documents: Uses the document currently displayed in the viewer to identify other conceptually correlated documents.
- Similar Document Detection: Identifies groups of highly correlated documents and displays them as related items in Relativity.
It is important to note, however, that these techniques organize material rather than reducing the number of documents. It is still up to the user to cull or properly code irrelevant documents prior to providing a set for attorney review. Additionally, given the physical limitations of reading and comprehension, better organization of the body of documents is not likely to account for reduced review rates unless decisions about individual documents can be applied to dozens or hundreds of similar items on a routine basis.
