7 eDiscovery trends to watch out for in 2024

By Professor Danny Myburgh

Technology and data management have drastically transformed, with software and technology evolving with the growing complexity and diversity of modern business requirements.

Like all disciplines, eDiscovery has migrated from traditional software installed on local machines to cloud-based and Software as a Service (SaaS) solutions that host vast amounts of data. These platforms have developed in sophistication as data needs have increased. The future of eDiscovery lies in continuous technological progression, such as advanced data analysis, AI integration, enhanced cloud technologies, and predictive analytics. Heading into 2024, here are nine eDiscovery tech trends we believe will shape this evolving landscape.

  1. SaaS and cloud-based solutions are leading the way

With benefits like cost reduction and scalability, SaaS and cloud technologies enable quick, uncomplicated data access, shifting away from cumbersome on-premises IT infrastructures. These solutions are able accelerate electronic data review (EDR), deduplicate data, tag content and help review teams to identify relevant data. As a bonus, SaaS technologies automatically provide regular updates and upgrades of features, functionality, speed enhancements, and security.

  1. Unstructured data has outpaced structured data – and technology is keeping up

In eDiscovery, the preference for structured data is clear, given its manageability and security benefits, which streamline sorting and reduce review time and costs. However, as we all know, the majority of data is unstructured and available from virtually any location. From MS Office documents, chat conversations and group discussions, recorded video meetings, Google Docs, emails, and text messages, the variety and complexity of storing data increases as the volumes grow, presenting the real challenge of organising and preserving all of it. From an eDiscovery perspective, this data needs to be reviewed, categorised and tagged for relevancy and confidentiality, something that is incredibly difficult without sophisticated EDR and eDiscovery platforms developed specifically for unstructured data.

  1. The complexities of eDiscovery are escalating

The days of sorting through piles of paper files are not entirely gone but have been substantially eclipsed by electronically stored information (ESI). This shift from paper to digital data has spurred a significant document review, analysis, and evaluation revolution.

Organisations continually produce more data than ever, and the data type constantly evolves. New platforms, tools, apps, and technologies are spewing out data at an unbelievable rate. Organising, managing, retrieving, and reviewing this data constantly challenges eDiscovery. Organisations will need to address these challenges through appropriate technologies and tools.

  1. AI adoption in eDiscovery review is here

AI-powered technologies are assisting businesses in finding patterns, trends, and anomalies in massive data sets. In eDiscovery, the review of these data sets requires the same AI-powered technologies. While computer-aided review is not new, it has significantly evolved from the initial search options provided and Technology Assisted Review (TAR) 1.0.

As data volume and complexity increase, AI is transforming eDiscovery, automating data processes, and enabling more efficient review and analysis. Its adoption is becoming essential, and its use in identifying sensitive information is helping organisations meet privacy regulations. However, some of the potential challenges that organisations face with the increasing use of AI in eDiscovery include the need for specialised expertise to manage and interpret the output generated by AI tools and the potential for bias or errors in the algorithms used by AI systems. Additionally, data privacy and security concerns may arise if AI-powered eDiscovery tools process or store sensitive information.

  1. Predictive coding is gaining traction

As data volumes have grown, traditional review is no longer practical. Predictive coding, which leverages machine learning for document review, is joining human review with technology in a way that provides confidence and efficiency, guaranteeing that relevant and vital documents are found and reviewed far more quickly to provide rapid and precise identification of relevant documents based on patterns. This results in a faster, less expensive process that compliments manual document review.

  1. Increased use of ECA automation

An often-overlooked step in the eDiscovery process is Early Case Assessment (ECA), which enables review teams to properly and rapidly analyse a case’s risks, expenses, and outcomes. Automating ECA has significantly increased in recent years as businesses explore ways to improve process accuracy and efficiency. Automated ECA solutions use cutting-edge algorithms and machine learning to examine big data sets to find relevant facts, trends, and patterns. The resulting smaller and more relevant data collections assist in reducing review time, improving review accuracy, and reducing costs associated with review.

  1. ROI from technology is essential in times of economic uncertainty

The economic climate is unstable, unpredictable, and volatile. Organisations are under more pressure to control costs and boost return on investment (ROI) in every possible area. In the eDiscovery process, organisations are naturally scrutinising their legal spending. Adopting new technologies and eDiscovery solutions is an effective way for businesses to reduce costs quickly, efficiently, and consistently.

Partner with the eDiscovery experts

LexTrado has, after careful evaluation, selected a number of international software platforms which, in combination, provide a cost-effective solution to common e-discovery procedural challenges. In this way, Lextrado delivers improved services to clients by freeing attorneys and staff for higher-value activities, while providing improved control over eDiscovery through snapshot reviews and automated audit trails.