Data retention strategy – Part 1

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Last year I endured one of the least enjoyable experiences in adult life – I moved. Despite all of its many time-consuming and unpleasant aspects, for me one of the few positives to moving is the opportunity to sell, donate or throw away stuff I no longer use. Unopened boxes from previous moves were easy pickings since if I never opened the box whatever was in it must not be very useful. Other easy choices included worn-out or ill-fitting clothing, damaged furniture, outdated electronics, random kitchen gadgets I only used once, and whatever happened to be in my junk drawers. Reminiscing about my move, and especially ruminating on the stuff I kept after the move, got me thinking about data retention – and the importance of having a data retention strategy.

Do you hoard data, or is there a data retention strategy?

Without question organizations now collect, store, process, manage, analyze and govern more data than ever before. In fact, the era of big data seems to be fostering the false notion that we have an obligation to retain any data that we come across because of its potential usefulness. Instead of a “use it or lose it” attitude toward data, we have a “retain it and maintain it” attitude, which is making data hoarders of us all.

While enterprises have numerous processes for maintaining data, few have processes (or strategies) for removing unused or unnecessary data. Instead of mountains of data that are managed just because they’re there, we need to acknowledge that all data has an expiration date.

I've previously pondered whether it would be possible to measure the half-life of data that has a well-known primary purpose, after which point it should at least be archived, or possibly even deleted. Of course, complexities arise when data’s primary purpose is not well-defined or when the same data can be put to secondary purposes. As with many data-related discussions, the general does not apply well to the specific, by which I mean that we need to discuss not data in general, but specific data. More importantly, especially for us data management professionals, we also need to remember it’s not about the data – it’s about what, if any, business uses the enterprise still has for the data.

What say you?

Let’s crowdsource some recommended practices for data retention. This series doesn’t conclude until May, so post a comment below and share your perspectives on and experiences with data retention and data retention strategies, and I will incorporate your feedback into the series conclusion.

Read Part 2 of this series
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About Author

Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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