By Tony Song*

Tony Song

In every minute, over 12,986,111 texts messages are sent around the world.1 Each day, there is an output of over 2.5 quintillion bytes of data.2 By 2020, for every person on earth, 1.7 MB of data will be created per second. Data is today’s most valuable currency – it is borderless, vast and multi-purposive. With the ever-increasing ubiquity of smartphones, laptops and cloud computing, data is also more portable than ever.

The natural by-product of this is the unprecedented level of electronically-stored information (ESI) that follows us everywhere we go on our phones, wallets and now even our watches. This has consequences for legal cases, increasing the cost of collecting, sorting and producing all potentially relevant ESI for eDiscovery.

This is especially so in the International Commercial Arbitration context, not just because of the vast reserves of data held by large corporations, but also due to the differing rules, procedures and nuances of their own country’s discovery processes. As the complexity and size of these cases inevitably balloons, the cost-effective and speedier outcomes of arbitration are at risk of attrition. Rising document volumes and reduced time frames for production urge firms to be up to speed with the technology and process. This further translates to the most time-efficient and cost-effective solutions for clients.

Perhaps the most obvious means to do so is to follow rules and case management techniques to ensure arbitration is streamlined. The Chartered Institute of Arbitrators Protocol provides guidelines, encouraging early consideration of the scope of discovery and a consultation with the tribunal of these parameters so as to minimise irrelevant documents.

Another approach is to resort to the increasing developments in LegalTech applications. Although Artificial Intelligence today is still a means away from its oft-preached science-fiction rendition, it is powerful, relying on machine learning to run repetitive tasks and search through vast quantities of data. Through predictive coding – where algorithms are combined with predictive analytics – it is now possible to quickly spot the most relevant documents. This will see eDiscovery evolve from screening for keywords to iterative computer learning that ranks the relevance of documents. This new method of technology-assisted review is now becoming a well-established trend, as LegalTech begins to catch up with other industries such as FinTech and BioTech, which have already begun shifting their business models en masse to a product service hybrid. LegalTech will likely follow cases like Brown v BCA Trading and others 

[2016] EWHC 1464 (Ch), which established a precedent that predictive coding be mandated in certain cases. With firms such as Herbert Smith Freehills already growing their eDiscovery service this year to leverage the potential of technology and data analytics, firms that likewise invest in innovative solutions today adding value to the clients are able to future-proof their firms for tomorrow.



* Tony Song is an ADC intern currently studying International Studies/Law at UNSW. He is also a Court Monitor for the NSW Courts.

1 Domo, Data Never Sleeps 6.0 <>.

2 Domo, Data Never Sleeps 5.0 <>.