Short Messages (SMS) are one of three classes of text messages found on mobile devices and messaging platforms and have been in existence since 1992. The other two categories of "text messages" are MMS (which include multimedia attachments) and chats. SMS pose unique challenges within eDiscovery as they exist within structured databases versus living as loose files on standard operating system platforms.
This means that they have to be extracted from their place of residency and normalized for purposes of review, hosting and production. Not all commonly used eDiscovery platforms possess a seamless way to present textual based information in an intuitive and visually attractive manner. The 800 pound gorilla in the forensic acquisition world for collection and parsing of text based data from mobile devices has long been Cellebrite, however there are no shortage of competitive products hitting the market monthly and yearly.
When dealing with messaging and collaboration platforms, collection agents and eDiscovery vendors need to be versed in languages and platforms such as Java, SQL, C++ and Python. Collecting the data is one thing, but parsing or normalizing the data is the key in order for practitioners to be able to render and review the textual content at the heart of the dispute or matter.
In the past 7-10 years, the world of forensics has morphed from just collecting data to mastering the normalization of data for inclusion into the most regularly used eDiscovery processing and review platforms. Along with the technical challenges of dealing with structured data concerns from an investigator's standpoint is the necessity of understanding that these data sources are now far more relevant and fertile in terms of "where the dead bodies are buried" as compared to email and loose file reviews.
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