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Why Data Minimization Is An Important Concept In The Age of Big Data

MFG Archive

Bernard Marr explains why in a world of Big Data companies and organizations need to consider practicing Data Minimization.


Data is a good thing, and as we know you can never have too much of a good thing, right? And as Bernard Marr asks in his recent piece, “bigger is always better, right?”


As big data continues to grow and become more easily accessible, Marr raises the point that bigger is not always better and that data minimization is an important concept and practice.  Referred to as a “tsunami” of data, the idea of cutting down your data may seem counter intuitive.  But Marr raises the point that without storing and utilizing only necessary data, companies and organizations are putting themselves at risk.


Data continues to become more accessible and collectable, making it easier to begin data hoarding.  Barr explains, “the dangers of data hoarding are similar to those of physical hoarding: mounds of useless junk that make it very difficult to find what we need when we need it.  It costs money and time, and can become dangerous.”


In terms of basic costs, data minimization is cheaper. “All data storage costs money, and no business has an infinite budget – so no business can go on collecting and storing data indefinitely.” But Marr also raises an interesting point about the risk associated with collecting and storing excessive amounts of data, especially in the cases of personally identifiable data.  Companies and organizations are responsible for the data they are storing and if personally identifying data is lost or breached, businesses and organizations can be faced with charges of criminal negligence.


In a recent From The Field piece, Lauren Shaughnessy, director of measurement and learning at Habitat for Humanity Greater San Francisco, touched on this exact point and emphasized the importance of collecting lean and relevant data.  She explained that by asking a few key questions you can translate the answers into a formal theory of change, which will serve to guide what data your organization should collect.  It may not be until you have to sort through your abundance of data that you even begin to uncover or realize the potential redundancy or ambiguity of your data.  


Especially in the case of nonprofits with limited access to the resources required to weed through troves of data, the need to prioritize and streamline data collection, and later analysis, is imperative to meet goals and avoid travelling down a rabbit hole of data.  


Though for some, big data is regarded as a necessity, and as Amazon CEO Jeff Bezos states, “We never throw away data.” While it seems that big data collection and storage is a luxury for the companies and organizations that can afford it, the risk of storing personally identifying data is a universal cause for concern.  But one could speculate that as we continue to see a rise in open data, the need for organizations and companies to collect and store massive amounts of data will lessen.  Through open data, organizations and companies could glean and utilize required data via external sources and, in turn, avoid the database pileup.


We ask you to consider your current relationship with and use of data and let us know are you #teambigdata or are you #teamdataminimization?



Read Forbes’ article here.  


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