The use of mobile devices, chat applications and collaboration and productivity tools have delivered significant efficiencies and opportunities for businesses. Often referred to in the e-discovery and investigations fields as emerging data sources, these systems and others, such as social media and cloud-based file sharing platforms, have been adopted by millions of users around the globe. Along with that growth, they are becoming more common as sources of evidence in disputes and investigations.
In India, the most common data type that has grown in importance and scale is from mobile devices. Personal devices are widely used for work purposes across enterprises in India, even more so since the shift to remote work at the start of the pandemic. From an investigations perspective, there are many challenges that mobile device data presents in the collection, processing, review and analysis phases. The most common challenges include:
- Wide variety of devices, operating systems and software versions that impact the technical steps and tools needed to extract data from a device.
- Encryption standards across devices and/or encrypted messages sent and received from the device, which can become impossible to access without a decryption key.
- Additional variables in how device history and timestamps are recorded and stored, making it difficult to understand the chronology of activity on a device or access certain information if the device was not configured to store history beyond a certain duration.
- Use of ephemeral messaging tools may prohibit investigators from tracking or discovering certain communication activity.
- Text messages and chat applications are often difficult to parse formats that require extensive scrubbing and analysis before they can be loaded into an analytics platform for review and fact finding.
If a team is not adequately prepared for the challenges that can arise when mobile and chat data come into scope in an investigations, significant delays and downstream problems can result. Often bespoke solutions are required or new workflows must be developed to navigate any issues stemming from unexpected data sources or formats.
Our e-discovery, investigations and emerging data sources experts in India and around the globe have been solving for these problems in recent years and have worked to stay ahead of the emerging data sources curve. This has included developing workflows that provide legal teams with the power to integrate chat strings into broader e-discovery data sets and processes. By expecting and proactively addressing the nuances and technical constraints within mobile and chat data, the legal team is able to view chats in context and transform indecipherable communication logs into easy-to-follow conversations. This approach not only saves time but also provides powerful insights material to a case.
Additionally, sophisticated analytical tools can be used to further aid the investigatory process, especially for early case assessment and fast fact finding. For example, analytics can reveal:
- Communication participants and frequency. The communications on a 1:1 channel or within a selected group of individuals might be very different than in a chat group and analytics can identify key differences that may be relevant to a matter.
- Sentiment analysis. Understanding the tones of communications can provide a better understanding of the relationship between participants or their possible intentions.
- Key or codeword identification. A heat map or concept analysis exercise may uncover new code words used in particular communications or other patterns that can lead investigators toward additional relevant information.
- Call logs or information from other data sources can be analysed alongside chat and mobile data to pinpoint if, when, and/or why discussions were moved from conventional forums to off-the-record channels.
Emerging data sources have become a forcing function for e-discovery and investigations teams to consider new or revised workflows. Taking an analytics-led approach that is designed for the nuances of new data formats will help teams establish workflows that are flexible and robust enough to meet the dynamic nature of collaboration, chat and cloud data.
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