As with any publicly traded company, any M&A activity is required to go through a certain level of scrutiny prior to obtaining approval. ESI Analyst was recently leveraged to identify relevant communications requested as part of this process. The data in question surrounded text and multimedia-based communications between executives leveraging the popular smartphone app called “WhatsApp”.
WhatsApp Messenger is a cross-platform messaging and Voice over IP (VoIP) service owned by Facebook. It allows users to send text messages and voice messages, make voice and video calls, and share images, documents, user locations, and other media. WhatsApp runs on mobile devices and is also accessible from Windows and Mac desktop computers. Given the multitude of documents and data WhatsApp can share, it is a replacement for email given its overall capabilities. Secure and encrypted group conversations can be easily created, and documents and data shared among all of the participants.
ESI Analyst is uniquely positioned to accommodate review and analysis of WhatsApp data as well as other similar collaborative messaging tools given that it treats each message as an “item”, and common communications between participants as a “thread”, not as documents.
ESI Analyst is uniquely positioned to accommodate review and analysis of WhatsApp data as well as other similar collaborative messaging tools given that it treats each message as an “item”, and common communications between participants as a “thread”, not as documents. Given that multiple smartphones had been collected from each individual, it required that the individual messages be de-duplicated against each other in order to provide a collection of unique threaded conversations. Each conversation displayed multimedia and images inline, much like they were originally rendered on the phone, making for easy review and tagging for potential production.
This simple and easy to use analytical process enabled by ESI Analyst allowed for rapid identification of relevant messages and their parent threads, isolating them by targeted participants using ESI Analyst’s Actor matching technology. Non-relevant threads could be easily identified based upon participants in the various conversations. Whereas relevant threads were rapidly identified as well as searched for keywords.
The analysis resulted in production of multiple text-based conversations that included the related multimedia and documents included in the threads, which were based on twenty-four hour periods of time. Specific Actor conversations were easily targeted using ESI Analyst’s Actor filters resulting in expedited review and identification of the relevant conversational data. Secondary production requests were then easily accommodated using custom tagging and exclusionary filters within the application interface.
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