The Data Graveyard Problem
Why Most Enterprise Data Sits Unused - And What to Do About It
Organizations collect data obsessively and use it sparingly. Warehouses fill with tables nobody queries. Lakes accumulate datasets nobody remembers creating. The gap between data collected and data used is vast - and growing.
This is the data graveyard: the burial ground for data that was expensive to collect, painful to store, and never delivered value.
How Graveyards Form
“Collect everything” mentality. Storage is cheap, so why not keep it all? This logic ignores the costs of cataloging, securing, governing, and eventually making sense of accumulated data.
Project-based data creation. Each initiative creates datasets for its specific needs. When the project ends, the data remains - orphaned, undocumented, owned by no one.
Fear of deletion. What if we need it later? This hypothetical future need justifies permanent retention of data that has no present use.
Missing metadata. Data without context is data without value. When documentation isn’t maintained, datasets become incomprehensible artifacts.
Organizational fragmentation. Different teams create similar datasets independently. Nobody knows what exists across the organization.
The Costs of Graveyards
Graveyards aren’t just wasteful - they’re harmful:
Storage costs compound. Cheap per gigabyte, expensive at petabyte scale. And the data keeps growing.
Security surface expands. Every dataset is a potential breach. Forgotten data is data you can’t secure properly.
Compliance risk accumulates. Regulations require knowing what data you have. Graveyards make compliance expensive or impossible.
Quality degrades. When nobody uses data, nobody notices quality problems. Graveyard data rots.
New projects reinvent. Teams build new datasets because finding and understanding existing data is too hard. The graveyard grows.
The AI Relevance
AI projects make the graveyard problem acute:
Training data requirements. AI needs data, but not just any data - data that’s documented, governed, and fit for purpose.
Graveyard data is risky. Undocumented data may have bias, quality problems, or compliance issues that surface only after models are trained.
Discovery burden. Before you can use data for AI, you have to find it. Graveyards make discovery expensive.
Provenance gaps. AI governance requires knowing where training data came from. Graveyard data lacks this traceability.
Remediation Approaches
Audit and retire. Systematically inventory data assets. For each: is it used? is it documented? is it governed? Retire what fails these tests.
Establish ownership. Every dataset needs an accountable owner. No owner = no dataset. This forces intentionality.
Require documentation. Data creation must include metadata. Undocumented data doesn’t enter production systems.
Sunset by default. Data should have retention policies. Default to deletion unless there’s a positive case for retention.
Consolidate discovery. Create a single place to find what data exists. If it’s not in the catalog, it doesn’t exist for practical purposes.
Prevention Over Remediation
Cleaning up graveyards is expensive. Better to prevent them:
Fund governance from the start. Data projects should include metadata, documentation, and ownership - not as afterthoughts.
Measure data utilization. What percentage of your data assets are actually queried? Set targets. Track trends.
Create deletion culture. Celebrate data retirement as good hygiene, not loss. Make sunset a normal part of the lifecycle.
Connect collection to use. Before collecting data, require a documented use case. “Might be useful someday” isn’t a use case.
The goal isn’t zero data - it’s intentional data. Every dataset exists for a reason, is documented, has an owner, and earns its keep.
What percentage of your organization’s data has been queried in the past year? What would change if you measured that?
Related: 04-atom—data-governance, 04-atom—data-provenance, 04-molecule—data-cascades-concept