The Dark Data Debacle: A Corporate Adventure

During a recent venting session, where my colleague Sachi Desai was listening to me try to articulate my thinking on our next moves, he coined a term that perfectly encapsulated the enigma we were discussing– Dark Data.

Imagine Hyperscalers as modern-day explorers, akin to Indiana Jones, embarking on a quest for the holy grail of Artificial Intelligence - ASI. On their mission, they amass all the visible data but discover that there is this hidden data, they can see the influence but can't tap into it. It's scattered across experts' laptops or locked away in the minds of company veterans.

Dark Data represents the vast information that companies collect but fail to organize or utilize effectively. It's the corporate equivalent of a treasure map leading to a forgotten archive, where each piece of data could be the key to unlocking new innovations or solving complex problems. However, the disarray is palpable; these digital catacombs of chaos are where good intentions go to die, buried under the weight of disorganized information.

Moreover, even if companies could find and harness this data, there's a significant reluctance to share it. This knowledge, whether digital or cerebral, is often seen as a competitive advantage or simply too cumbersome to extract and share. The experts carrying this knowledge in their heads are like walking libraries, yet the books remain unopened, their pages unshared.

Recognizing Dark Data is akin to acknowledging dark matter or dark energy in the universe – we know it's there, influencing everything, yet we can't see or touch it directly. In the corporate cosmos, this data exists in abundance, lurking in the shadows of organizational memory.

The questions then become multifaceted: How do we find this Dark Data? How do we measure its impact? How can we organize it to turn it from a liability into an asset? These are not trivial queries; they require innovative approaches to data management, perhaps even new technologies or methodologies. 

And the ultimate question looms large: Will Dark Data be essential for the development of AGI (Artificial General Intelligence) or ASI (Artificial Superintelligence), or will these advanced forms of AI need to rediscover this knowledge as part of their own learning journey? This conundrum poses not just a challenge but also an opportunity for those willing to delve into the depths of their data to illuminate what has long been left in the dark.

To all the physicists out there, I extend my apologies for the egregious misuse of the terms "dark energy" and "dark matter" in the context of corporate data. I recognize that likening the mysteries of the cosmos to the chaos of unmanaged corporate data might be cringe worthy from your perspective. Please forgive our playful, scientifically sacrilegious, analogy, no disrespect intended. I just found it very helpful.

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Tami Craig Schilling Tim Williamson Giri Anantharaman Sachi Desai Philipp-Andreas Schmidt