The Predictive Coding Technologies and Protocols Survey is a non-scientific survey designed to help provide a general understanding of the use of predictive coding technologies and protocols from data discovery and legal discovery professionals within the eDiscovery ecosystem.
A working knowledge of electronically stored information (ESI) is foundational for effective, efficient, and comprehensive planning and execution of all electronic discovery tasks. This short, non-all inclusive overview highlights key definitions, descriptions, and attributes related to ESI and may be beneficial for legal and information technology professionals as they consider ESI in the conduct of data and legal discovery.
This new legal technology salary mashup contains general salary data points gathered from an aggregation of publicly available technology and legal technology industry research, reports, and anecdotal evidence.
Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for software and services in the electronic discovery market for the years between 2017 and 2022.
The presented listing highlights key industry business moves by sharing the announcement date, acquired company, acquiring or investing company, and acquisition amount (if known) of significant eDiscovery-related mergers, acquisitions, and investments.
Based on a compilation of research from analyst firms and industry expert reports in the electronic discovery arena, the following “Top 100+Provider” list provides a short listing that may be useful in the consideration of eDiscovery providers. This listing is taken primarily from eDiscovery provider mentions in selected key formal industry reports and surveys published between August 2011 and today.
One of the biggest challenges facing information, business, and legal professionals is the ability to cohesively consider the elements of data discovery and legal discovery within a technology framework that is comprehensive enough to address critical discovery tasks throughout information and legal lifecycles yet concise enough to be realistically approached from an automation perspective.