Sunday Read: Hades and Data Storage – #16
Series: "Mythology": Where does “dead” data go? How do we manage archival, off-chain storage, dark data, and compliance?
“Mythology” Series:
Format: Each week we present a concise mythological story and draw direct parallels to contemporary AI concepts.
Goal: Highlight how modern technological dilemmas mirror ancient Greek tales, sparking interest about both subjects.
1. Mythological reference
In Greek mythology, Hades is the ruler of the Underworld, a hidden realm where souls linger after life. Far from a mere place of torment, his domain is often depicted as a vast repository of departed spirits. Myths like the abduction of Persephone illustrate how stepping into this unseen territory can have profound consequences on the world above—return trips are rare and seldom straightforward.
2. AI Parallel
The underworld of data storage
Modern organizations amass enormous troves of “dead” data—unused system logs, obsolete customer records, archived transaction histories—that sit out of sight, much like souls in Hades’s Underworld. This neglected information, sometimes called dark data, may pose risks if overlooked and can even harbor unexpected value:
Archival and backups: Just as Hades securely keeps the departed in his domain, off-site or cold storage preserves data beyond active usage.
Dark data: Like wandering shades, forgotten data can lurk within systems, accruing storage costs and liability.
Compliance: Regulations such as GDPR demand vigilance over personal data, including the “lost” bits residing in the metaphorical Underworld.
Lesson: mindful data guardianship
Hades wields immense authority over the Underworld; similarly, organizations must recognize and respect the hidden power of their archived data. In Dark Data: Why What You Don’t Know Matters, David J. Hand cautions that “unseen data can undermine even the most carefully crafted analyses,” implying that unused or unaccounted-for information can skew results or create blind spots. Likewise, Bill Inmon, the “father of the data warehouse,” reminds us in Data Lake Architecture that “a data lake is not a graveyard—it’s an evolving environment where data can be discovered and reused.”
By acknowledging these insights, businesses can map, audit, and periodically revisit their data stores, turning potential liabilities into strategic assets. As Martin Kleppmann explains in Designing Data-Intensive Applications, “we should design data systems that handle not only the success scenario, but also partial failures and eventually consistent states.” Applied here, it means planning for historical data retrieval—and the potential pitfalls of reanimating old records—just as one would for active, mission-critical databases.
3. Reflections and questions to consider
Dark data vs. data value
Which archived information is worth resurrecting, and which should remain dormant?
Compliance and ethical responsibility
Are we implementing the right protocols to securely manage and protect off-chain or long-term data under regulations like GDPR and CCPA?
Retrieval and accessibility
Which processes ensure that historical data remains discoverable and usable without overwhelming current systems?
Cost vs. benefit
Do the expenses of maintaining extensive backups pay off in terms of potential insights—or are we simply housing data we’ll never need?
4. References
Homer, The Odyssey
Adrienne Mayor, Gods and Robots: Myths, Machines, and Ancient Dreams of Technology
David J. Hand, Dark Data: Why What You Don’t Know Matters
Bill Inmon, Data Lake Architecture
Martin Kleppmann, Designing Data-Intensive Applications
GDPR (General Data Protection Regulation)