Time takes its toll on everything, and enterprise data is no exception. As databases expand and multiply, a growing number of organizations are facing the prospect of data decay.
Data decay is any data that's not useful, states Kathy Rudy, chief data and analytics officer for technology research and advisory firm ISG. “This can include not only data that's outdated, but incomplete, inaccurate or duplicative.”
Data never sleeps
If a house isn't properly maintained, decay can claim it within just a few years, observes Goutham Belliappa, vice president of AI engineering at IT services and consulting firm Capgemini Americas. “Data decay occurs in much the same way, when a lack of maintenance and continuous attention lead to irrelevant data sets that are no longer useful or are disorganized.”
A typical example of data decay is when a sales or prospecting contact list fails to reflect the fact that key individuals have shifted roles or moved to a different company. “Interacting with decayed lists like this can waste up to 70% of an organization’s prospecting efforts,” Belliappa says. “On the other hand, if some of that energy were diverted to contact list curation, the interaction efficiency could increase by over 300%.”
Data decay can also occur when files are improperly catalogued, particularly when the individuals responsible for retiring a vintage data group are unaware that the asset even exists, notes Robert Audet, director and data management leader at business and technology consulting firm Guidehouse. The same holds true when it's unclear exactly who is responsible for retiring specific data assets.
Since decay is all but inevitable for many types of data, enterprises should consider deploying management and mastering strategies that are designed to keep pace with the fluid nature of enterprise databases. “Data entropy results in over 70% of B2B data decaying per year,” Belliappa observes. “For example, if B2B contacts are not managed for one year, less than one-third of the contacts will be relevant.”
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