Modern Structured Data Demands New Discovery Approaches

This week, attention turns to the evolving landscape of structured data and its implications for e-discovery. The nature of structured data is changing, prompting a re-evaluation of traditional discovery methodologies.

The increasing complexity and volume of structured data present ongoing challenges for legal and forensic professionals. Understanding these shifts is crucial for effective information governance and disclosure processes.

The Evolving Challenge of Structured Data

eDiscovery Today, through an article by Doug Austin, highlighted that modern structured data is different. The publication announced a Sapling Data webinar scheduled to discuss why this new form of structured data requires a new discovery approach.

This observation underscores a growing recognition within the e-discovery sector that the tools and techniques developed for earlier generations of structured data may no longer be adequate. The article itself does not elaborate on the specific differences or the nature of the new approaches, but it signals a significant area of focus for practitioners.

What Constitutes 'Modern' Structured Data?

While eDiscovery Today's announcement points to a need for new approaches, it does not detail what makes 'modern' structured data distinct from its predecessors. Historically, structured data has referred to information organised in a fixed field within a record or file, such as databases, spreadsheets, or financial records. This data is typically easy to search, sort, and analyse due to its predefined format.

The implication from the eDiscovery Today piece is that this definition may now be insufficient or that the characteristics of such data have evolved in ways that complicate traditional e-discovery processes. This could relate to the sheer volume, the interconnectedness of different structured data sources, the complexity of data models, or the integration of structured data with semi-structured or unstructured data types within enterprise systems.

What this means in practice

For UK practitioners involved in disclosure or forensic work, the assertion that modern structured data requires a new discovery approach carries significant weight. It suggests that current workflows and technologies may need adaptation to remain effective and compliant.

• Early Case Assessment (ECA) and Data Mapping: Practitioners should enhance their ECA processes to specifically address structured data sources. This includes a more granular understanding of database schemas, enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and other business-critical applications. Data mapping exercises should extend beyond identifying data custodians to understanding the underlying data structures and interdependencies.
• Proportionality and Scope: The complexity of modern structured data can impact proportionality. Identifying relevant data within vast, interconnected databases can be resource-intensive. Practitioners must work closely with clients and technical experts to define precise search parameters and extraction methodologies that balance the need for comprehensive disclosure with cost and time constraints. This may involve iterative discussions with the court or opposing parties to agree on a proportionate scope.
• Data Extraction and Processing: Traditional methods of exporting structured data, such as flat files or CSVs, may lose critical contextual information or relationships. New approaches might involve more sophisticated database connectors, API integrations, or specialised tools designed to preserve data integrity and relationships during extraction. The processing phase must then be capable of ingesting and indexing this complex data while maintaining its structured nature for effective review.
• Review Workflows: Reviewing structured data often requires different skills and tools than reviewing documents. For example, reviewing transactional data may necessitate an understanding of accounting principles or specific business logic. Review platforms may need to offer advanced filtering, aggregation, and visualisation capabilities tailored to structured datasets, rather than relying solely on keyword searching and linear review.
• Forensic Investigations: In forensic contexts, the integrity and completeness of structured data extractions are paramount. Investigators must be equipped to handle complex database forensics, including the recovery of deleted records, analysis of audit trails, and reconstruction of events from transactional data. This requires specialised expertise and tools beyond standard file system forensics.
• Expert Engagement: The increasing complexity of structured data means that engaging data experts, database administrators, or forensic accountants may become more routine. Litigators and in-house counsel should be prepared to collaborate closely with these specialists from the outset of a matter to ensure data is identified, preserved, collected, and analysed correctly.

The absence of specific details from eDiscovery Today regarding the 'new approach' means practitioners must proactively investigate and adapt. This involves staying abreast of technological advancements, understanding the specific data environments of their clients, and fostering interdisciplinary collaboration.

Looking Ahead

The eDiscovery Today article, while brief, serves as a timely reminder that the e-discovery landscape is not static. The continuous evolution of data types and storage mechanisms demands ongoing professional development and adaptation of practice. The upcoming Sapling Data webinar, as announced by eDiscovery Today, indicates that further insights into these new approaches are anticipated.

Practitioners should monitor developments in this area closely, particularly regarding new tools, methodologies, and best practices for handling complex structured data. The goal remains to ensure that disclosure obligations are met efficiently and effectively, and that forensic investigations yield accurate and defensible results, even as the underlying data becomes more intricate.