IBM Brings Analytics to eDiscovery
eDiscovery solution services all aim to save customers and companies time and money. Due to the non-linear structure of eDiscovery, there are so many ways to go about making the process, whether it be pre-processing, data collection, filtering, review or post-processing phases, more pleasant for the user.

IBM (news, site) is focused on the analytics part of eDiscovery. Today they announced new IBM eDiscovery software with advanced analytics features. The goal is to help clients not just find information, but have them understand it better.

Users will find new features in IBM InfoSphere eDiscovery Manager and IBM InfoSphere eDiscovery Analyzer, among them:

Discover Your Organizational Agility

The idea is that by accelerating “organizational agility” -- a fancy eDiscovery term that refers to the efficiency (or lack thereof) with which an organization can respond to change -- workflows can streamline the integration of eDiscovery functionality more effectively and comprehensively.

By integrating IBM content analytics technology into its eDiscovery Analyzer, richer early case assessment capabilities can be generated, letting companies create better legal strategies before going any further into the search and discovery process, saving time and money.

Digital Information Management & API Support

Additionally, the update comes chock-full of new IBM offerings, which will make managing digital information more manageable. Users can now expand support for processing, analyzing, searching and holding multiple forms of ESI support, including the ability to export processed and analyzed ESI in the industry-standard EDRM XML format.

IBM’s eDiscovery software also includes extended API support, which allows users to explore, expand and implement the customizable options available in individual software components.

Ultimately, IBM’s eDiscovery software seeks to provide its customers with a more comprehensive set of tools so that they can more effectively manage information based on its value, comply with global legal obligations for data, discover data efficiently and routinely dispose of information at the end of its lifecycle.