Context is key
Mobile messaging has become one of the most dominant forms of communication in the digital age. In 2024 alone, WhatsApp users sent over 100 billion messages per day, while an estimated 15 million text messages were sent every minute, worldwide. With conversations taking place across platforms like iMessage, WhatsApp, WeChat, Telegram, Signal, and Slack, mobile messaging data is now a critical source of evidence in eDiscovery.
However, extracting meaningful insights from mobile messages in eDiscovery presents unique challenges. Unlike traditional emails or documents, these messages are often brief, fragmented, and highly contextual. A single message can mean little on its own, but when placed within the broader conversation, it can reveal key insights about intent, relationships, and decision-making.
The challenges of mobile messages in eDiscovery
To make mobile data useful in an investigation, it must be collected, processed, and structured in a way that preserves the full context of conversations. This requires overcoming a range of obstacles, including:
- Contextual isolation – Individual messages can be meaningless without the surrounding conversation.
- Diverse messaging platforms – Data must be extracted from multiple sources and standardised.
- Security & encryption – Many apps use end-to-end encryption, complicating collection.
- Complex extraction & conversion – Messages must be extracted from devices, cloud backups, or databases and converted into a format suitable for review.
- Data integrity & completeness – Ensuring no messages are missing or altered is crucial to maintaining evidential reliability.
- Messaging app updates – Changes to message formatting, storage structures, and encryption protocols require ongoing adaptation to ensure compatibility with eDiscovery tools.
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A structured approach to overcoming the challenges of mobile messages in eDiscovery
Step 1: Extracting mobile messages for eDiscovery
Unlike emails or documents, mobile messages aren’t stored in a standard, easily exportable format. Each messaging app manages data differently – some store messages locally on a device, while others rely on cloud backups or encrypted servers. As a result, specialised forensic tools are often required to extract messages from these various sources.
However, extraction is just the first step. Most forensic tools convert retrieved data into proprietary formats, which can only be accessed through the tool’s own (typically desktop-based) application. This creates several challenges:
- Heavy resource demands – Large data files can result in long load times and high processing requirements.
- Limited collaboration – Many forensic tools are designed for individual examiners, restricting team-wide access and review.
- Fragmented datasets – Proprietary formats make it difficult to integrate mobile messages with other evidential sources, limiting the ability to search and analyse across the full dataset.
Without further processing, extracted messages would remain isolated from the broader investigation, making it difficult to establish relevance, track conversations, or uncover key insights.
Step 2: Transforming raw data into a unified format
When dealing with mobile messages in eDiscovery, raw messaging data must be converted into a standardised format that preserves both content and context in order for it to be usable in an eDiscovery platform. This is particularly important when collecting messages from multiple sources, each with its own structure and metadata.
The transformation process ensures that:
- Messages are organised into a coherent, readable narrative, rather than appearing as isolated fragments.
- Threading and chronological order are preserved, so conversations can be understood in their original flow.
- Attachments and metadata are correctly linked, providing a full picture of interactions.
Beyond structuring the data, this stage also offers opportunities for data enrichment, such as:
- Matching phone numbers to names in address books, making it easier to identify participants.
- Transcribing voice notes, videos, and audio, so spoken content becomes searchable.
- Using Visual AI to analyse images and attachments, uncovering additional insights.
By standardising and enriching the data, investigators can work with complete, contextualised conversations rather than disconnected messages.
Step 3: Loading into an advanced eDiscovery platform
Once transformed, mobile messaging data can be integrated into an advanced eDiscovery platform – such as Reveal – where it can be searched, analysed, and reviewed alongside all other case data.
To preserve context, Reveal organises message threads into 24-hour blocks, allowing investigators to see conversations in their natural flow rather than as isolated messages. This makes it easier to assess discussions in their entirety and determine their relevance to an investigation.
With AI-powered search and analytics, teams can then:
- Run keyword searches across all evidential material simultaneously.
- Analyse communication patterns to uncover relationships between participants.
- Track conversation timelines to establish sequences of events and intent.
By integrating mobile messaging into the broader investigative workflow, Reveal ensures that once-disconnected messages become a structured, searchable, and insightful source of evidence.
Making mobile messaging data work for you
Mobile messaging is now a vital source of evidence, but its fragmented nature makes it one of the more challenging data types to handle. Without the right approach, key context can be lost, and critical insights missed.
At Salient, we specialise in extracting, transforming, and integrating mobile data into a searchable, structured, and context-rich format. Using advanced tools like Reveal, we help legal teams cut through complexity, uncover key insights, and accelerate investigations.
Need expert support with mobile messages in eDiscovery? Get in touch today.
