The types of Electronically Stored Information (ESI) are rapidly evolving due to changes in the way people communicate. Traditional data forms such as a standard email with recognizable metadata fields for sender, recipient, subject and body text are being replaced by internal group chat systems, tools and devices that store and display data in ways that make the collection of information more challenging in an e-discovery setting. Data is also differentiated by the concepts of traditional data versus big data. Big data does not refer to the size of the contents, but rather to the variety of the data and the environment in which it is stored. In many cases, the context of this data is difficult to work with or too expensive to store in traditional relational databases.
Problems with the retrieval of ESI
Suppose a legal hold has been placed on your entire internal messaging system. What types of data would be discoverable? How can the custodians be identified? If all of the system’s contents were simply copied into a review database, a team of contract attorneys could review these messages, but likely would have to wade through GIFs, emojis and other non-relevant content spanning months or even years to find the proverbial needle in a haystack. Now also imagine if the requested data was more technical in nature and had been directly downloaded from a smart watch, GPS system or fitness tracker. A printout of the data could show numbers or codes but with no recognizable pattern. This unstructured ESI is indecipherable to a review without additional context. The new challenge for e-discovery is to take all of these non-traditional forms of ESI and translate them into a format that will allow for an efficient, cost-effective review that captures the relevant data for the time period.
Until recently, it was an onerous process to retrieve such data; however, review tool vendors are beginning to roll out solutions. For example, Relativity has developed an application that allows it to interface with Slack to retrieve messages. This ability to interface with the system itself allows the user to sort the data by channel and review it in chronological order, thereby significantly reducing the time spent reviewing irrelevant content. Similar developments have been made by other software integration companies to import data from programs such as Confluence, Jira, Zendesk (a customer service software and support system) and Quip (a document collaboration software tool for multiple users). These companies collect the ESI in its native format as well as all relevant metadata. The programs can then separate tasks, to-do lists, calendars and projects and present them in a reviewable format.
It is up to the e-discovery practitioner to ensure that the request for information and provision of information to adverse parties covers the inclusion of all potential ESI sources as well as prepares for the possibility of a non-traditional ESI review. Please contact your sales representative to find out how Special Counsel can help solve your unique data requirements.
Traci Gray is a Project Manager for Special Counsel. Connect with Traci via emial today!