The first half of 2017 has been a busy one for courts adjudicating matters relating to eDiscovery. This includes more rulings on predictive coding and a solid trend line pointing to an underlying question in complex, document-intensive eDiscovery cases— has a party acted reasonably by choosing not to use advanced review techniques? Arguably it has been even busier on the global front with organizations gearing up for the General Data Protection Regulation, new data and electronic communication types becoming sources of evidence, and ransomware attacks attacking unsuspecting businesses and law firms.
In response, the savvier companies and their outside counsel are reshaping the way they approach eDiscovery.
AI and analytics become business as usual
Artificial intelligence (AI) is undoubtedly today’s eDiscovery darling, and many lawyers’ first foray into this area has typically been through technology-assisted review (TAR) or predictive coding to automate parts of this costly and time-intensive process. Forward-thinking legal teams are moving beyond TAR, which is now the norm, and wading further into the AI space helping validate and support this year’s bevy of glitzy product unveilings. We believe one AI approach, in particular, is worth calling out as it represents a marked improvement over previous generation systems – big data analytics provide actionable insight across an organization’s entire casework, not just a single matter. This technology can automatically classify and identify data relevant to new cases and eliminate repeat reviews of the same documents over again. In addition, it can even increase quality control and reduce the risk of inadvertently exposing privileged or sensitive data.
Practice makes perfect
So, the saying goes. In eDiscovery, repeatable workflows can take a big step forward in lowering cost, increasing efficiencies and optimizing results. But from a case-to-case basis, muscle memory is difficult to flex, and legal teams often find themselves having to reinvent the wheel. Analytics-based workflows optimize the eDiscovery process by applying learnings from prior and existing matters to brand new cases. This in turn enables legal teams to gain deep insight into their data, save significant cost and time at the review stage, and ensure a higher level of quality with each new case.
Cybersecurity and data privacy take center stage
As recent months have proven, no entity is immune from ransomware gangs, careless employees exposing records, and hackers. This makes securing sensitive information, such as personally identifiable information (PII) — account numbers, social security numbers, telephone numbers and the like–more critical and challenging than ever before. New PII detection techniques are drawing upon big data analytics but subject matter expertise and traditional review tools still have a vital role to play in identifying and securing PII within an organization to pre-empt risk.
Privilege review becomes more precise
And more precise means less legal spend. In eDiscovery, document review accounts for 70 or more cents of every eDiscovery dollar sent, and privilege review is one of the costliest parts of the process. Based on our analysis of over 10 billion review decisions across thousands of matters, more than 90 percent of documents identified as being “potentially privileged” through traditional approaches such as keywords do not end up being withheld—creating substantial and unnecessary review costs. Further, an alarming volume of privileged data still slips through the cracks—creating risk. A new analytics-based approach to privilege review goes far beyond simple text searching. Instead of returning proportionately high volumes of documents matching keyword sets, algorithms identify documents using multiple factors that more closely conform to the principle definitions of privilege. The result is a more targeted and precise “potentially privileged” dataset. This approach derives context from a combination of keywords, metadata, and human input to create a highly accurate privilege recognition framework, achieved through a combination of rules-based tagging and machine learning.
eDiscovery analytics find a home outside of litigation
Heavy data growth, heightened regulatory scrutiny, global and remote workforces, and stronger reliance on third-party vendors and suppliers are all adding considerable burden and complexity to an organization’s compliance program. Core to these challenges is catching unmonitored electronic communications which increasingly contain evidence of risk, such as chat, social media and the like. Such types of communications are not traditionally captured in a traditional enterprise risk management (ERM) approach that screens systems, including transactional and financial software, for possible violations.
Therefore, forward-thinking legal and compliance teams are adopting an innovative approach to more sustainable compliance: to detect and resolve potential compliance infractions before they turn into legal liabilities, they are leveraging insights from prior legal cases using big data analytics, as well as traditional eDiscovery review technologies and subject matter expertise.