Eighth in a series of in-depth interviews with innovators and leaders in the fields of Risk, Compliance and Information Governance across the globe.
Markus Lindelow leads the IG and Content Classification Practice Group at Iron Mountain, the world’s largest information management company, where he’s been pioneering breakthrough analytic techniques for over a decade. He holds a Master of Science degree in Computer Information Systems from Saint Edwards University and consults across a broad set of industries. I interviewed him in November to discuss his thoughts on the evolution of metadata, content classification, AI, and how organizations are using the new pillars of data science to break down their silos, help customers get lean and discover the hidden value in their big data sets.
Markus, you work with all kinds of companies to help them better understand and address the often incomplete metadata tied to some of their most valuable information assets in the form of historical paper records and materials retained over decades. In many cases, institutional memory has been completely lost and they’re struggling to figure out whether to dispose of these business records, balancing costs of over retention with risks of untimely destruction. How does your team leverage diagnostic, predictive and prescriptive analytics to make sense of what little data they might have to make informed decisions?
Our content classification process focuses on making the best use of the available metadata. This means classifying records with meaningful metadata as well as analyzing the classified inventory in order to create classification rules for records with little or no metadata. We have identified a number of attributes within the data that tend to correlate with classification conclusions. We assess the classified records associated with an attribute to create a profile that may inform a rule to classify the unclassified records sharing that same attribute…