Harnessing Analytical Insights and Illuminating the Physical Realm of Dark Data – An Interview with Markus Lindelow of Iron Mountain

Harnessing Analytical Insights and Illuminating the Physical Realm of Dark Data – An Interview with Markus Lindelow of Iron Mountain

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…

If, for example, there are 100 cartons associated with pickup order XYZ, 90 of those cartons have been classified, and furthermore all 90 are classified to ABC100, can we create a rule to classify to ABC100 the 10 unclassified cartons belonging to pickup order XYZ? Clients may need to weigh the risk when applying this type of classification rule and the process may include a random sampling of cartons for physical inspection in order to verify the classification.

There’s usually a disconnect between the needs of information managers and legislatures which set retention periods for records. We see this in regulations where the granularity of both fixed and event based retention triggers complicates the practical management of records. Over the years, strategies like “big buckets” have attempted to lessen this challenge but even the best efforts are imperfect and carry their own risks. What can be done to better bridge the divide between the need for due diligence in retaining records and the business case for a more practical solution?

There are two pieces to the puzzle of records management: classification and retention. A records retention schedule needs to be straightforward enough to implement so that users can apply record codes to records. But the retention periods for the record classes need to be specific enough so that some types of records are not being over or under-retained because they are being grouped with other records…

Read the entire interview and more in my new book on leadership in the information age, Tomorrow’s Jobs Today.

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