In the archetypal hero journey, once ordinary characters, such as Luke Skywalker, Frodo Baggins, Arya Stark, Katniss Everdeen or even the android, Data, leave what they know to pursue a quest, facing many extraordinary, fantastic and sometimes supernatural predicaments or dilemmas in an unknown world. After facing and emerging victorious from a diverse set of trials, they ultimately return stronger—a master of both the known and unknown worlds—with the objects of the quest either literally or figuratively in hand.
In the case of Data, his quest was learning to be more human. Two or so decades later (in human time), the personification of data has taken on a life of its own, to such an extent that data are considered the lifeblood of today’s organizations. Indeed, the quest for data resilience can be likened to the quest for an elixir of eternal life for organizations, especially once it is realized that data resilience is key to a modern organization’s sustainability. Delete all data from any of today’s large organizations anywhere in the world—or even smaller organizations—and watch them die either a rapid death or a slow and painful one.
The predicaments making up the ordinary data professional’s quest can be diverse. For example, whether they work in data infrastructure, data management, data culture or data architecture, they each have challenges they must systematically overcome to develop true data resilience. And the more one continues into the great unknown on this quest for data resilience, the grislier the monsters and skeletons that are hidden in the detail. As ever, this raises the stakes involved in pursuing and attaining data resilience.
It is important to understand that data success does not necessarily only mean data uptime (to some, this is the definition of data resilience, specifically as it relates to cloud storage). Data success also does not necessarily only mean having clean data or delivering defined measures of operational performance to management. And even less, data success does not necessarily only mean deploying analytics or artificial intelligence. Instead, data success is all of these things together, and so much more.
The data value chain, from the point of data production to the point of data consumption and ultimately of decision-making and everything that supports it, must operate continuously without trial or tribulation. This can only be achieved by active management of the potential risk to the organization posed by the predicaments that exist along the organization’s data value chain as well as the active management of the entire data value chain itself.
The subject of a true hero’s quest is epic—think the Holy Grail, the Golden Fleece or even Medusa’s head. For the data professional, the quest is equally epic; there is no bolder quest in today’s organizational data landscape than the quest for a (near) perfect resilient data value chain, true data resilience and, therefore, organizational sustainability.
Editor’s note: For further insights on this topic, read Guy Pearce’s recent Journal article, “Data Resilience Is Data Risk Management,” ISACA Journal, volume 3, 2021.
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