What is the Process of eDiscovery?
eDiscovery is the electronic aspect of identifying, collecting, and producing electronically stored information (ESI). This is in response to a request for the production of digital evidence in civil litigation, dispute resolution or an investigation. The initial phase requires parties involved in a dispute to actively provide relevant information and evidence related to the case.
What is Metadata Analysis?
The processes and technologies involved in e-Discovery are usually complex due to the volume of electronic data produced and stored. Additionally, unlike hardcopy evidence, electronic documents are more dynamic and often contain metadata. Metadata is the information generated within a piece of electronic data and is ‘data about data’. It exists within every digital item stored on physical devices and can include the user who created it. Alongside the creation date, history, and document software used to create it.
Data of this kind often tells the rest of the story about the document and, therefore, is often a key focus of e-Discovery. Preserving the original metadata of electronically stored information is crucial in order to maintain evidence continuity. And, to avoid the opposing party critiquing the validity of the evidence during litigation.
What is the eDiscovery Process?
From the issuance of a formal legal matter to the presentation of digital evidence in court, the process actively engages in eDiscovery. Both parties involved identify ESI and place any relevant electronic or hard-copy documents under legal hold to prevent modification, deletion, or destruction. They then identify, collect, forensically extract, index, and host any data associated with the matter in a secure environment.
Why do we use Document Review Platforms?
Here at CYFOR Legal we use eDiscovery technology such as a document review platform so that document reviewers can analyse the data and ascertain its relevance to the legal matter. Applying e-Discovery methods such as Technology Assisted Review (T.A.R), predictive coding, keyword filtering, de-duplication, and email threading actively enables the assimilation of vast amounts of information, culling any non-relevant data, considerably reducing the dataset, and leaving only relevant information.
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