The relationship between information governance and eDiscovery has been poorly defined in the legal world. Typically, legal firms view eDiscovery as a byproduct of forcible legal action; an individual process only addressed when a party “legally anticipates litigation.”
However, the mindset of viewing eDiscovery in a bubble separate from information governance costs businesses big in the way of inefficiency, over-collection, and disjointed organizational goals.
Defining the Relationship
Past models, such as the EDRM model, have been used to define the relationship between eDiscovery and information governance. EDRM represents information governance as a discipline that feeds into the eDiscovery process—a feature inherently misaligned with the mission of information governance.
The more recent IGRM model comes closer by including legal, compliance, IT, and business perspectives in the equation, but fails to show the full life cycle of information governance and its relationship to eDiscovery. Joining the two by presenting IGRM as the “other half” of the EDRM coin provides a clearer picture of process complexities, but still falls short by depicting eDiscovery as a natural progression of information life cycle.
Big Data Information Life Cycles
To truly define the relationship between information governance and eDiscovery, businesses must use technology to make sense of the noise and offer a clearer view of data life cycles. Organizations must first use this technology to help distinguish which data is transient and which data is necessary for business decision-making.
Fortunately, data analytics and machine learning technology have progressed far enough to enhance efficiency of information governance processes, including categorization, improving access to data, and supporting data destruction under retention policies.
These analytic reviews should begin early in the information life cycle. As soon as information is created or received, analytics can automatically classify documents into categories based on content and prepare them for future analytic processing, even at scale. However, analytic technology alone can’t solve every business’s information woes. Analytics can’t be applied en masse to random data sets; processes for applying technology must be efficient and scalable to remain financially viable.
Above all, the long-term impacts of these analytics tools must be assessed, both on the business and individual user levels. Data security and privacy should remain a priority throughout these processes too—sacrificing security for the sake of efficiency is not a viable solution. With new models being devised and new technologies to apply, legal firms are in a good position to tackle the broader problem facing discovery: how to define the relationship between information governance and discovery in a way that leads to better efficiency throughout the data life cycle.
Written by Dean Van Dyke, Vice President, Business Process Optimization
Dean Van Dyke is the Vice President of Business Process Optimization for iBridge. He brings more than 18 years of customer relations, business process outsourcing, lean six sigma, program/project management, records management, manufacturing, and vendor management experience to iBridge. Mr. Van Dyke was the former head of Microsoft’s corporate records and information management team and served honorably for over fourteen years in the U.S. Navy and Army National Guard. He received his Bachelor of Science in Business Administration from the University of South Dakota and his Master’s in Business Administration from Colorado Technical University.