The Problem
The 'Space Data Centre' Catch 22
Satellites Carry Unpriceable Risk. ORBintel makes it Predictable.
The space insurance market is volatile and high-stakes:
Only 2% of the 13,000 civilian satellites in orbit today (2023) are covered by 3rd-party insurance. A single major loss can destabilize the industry: in 2023, $1.3B vs. $500M premiums.
Satellite operators face their own challenges:
Compute and data inefficiencies—ranging from on-board processing to transmission scheduling—exacerbate operational risk.¹ Performance inefficiences extend across every digital industry, and key agencies are seeking answers to this critical issue—NIST, national statistical agencies, and DARPA, which in 2025 issued the "ML2P" RFP calling for a solution to this urgent problem.²
The symptom of a larger crisis:
Data Centres consuming critical energy and natural resources at alarming rates, yet the compute and data has no measurable cost/benefit value. Data Centre operators, investors, customers, neighbours, regulators, and local government managers need visibility. No systematic method exists to calculate the true cost/benefit to generate sustainable profitability.
Digital Inventory Yield Drives Infrastructure Valuation
When the problem is solved, inventory rack yield quantifies leasehold profitability
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Changing intangible data into auditable operating assets
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Converting stored data into leaseable real estate, with measurable ROI
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Unearthing buried treasure—turning it into liquid gold
With validation, the space industry achieves significant risk mitigation:
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Coverage moves from satellites (high-risk) to the now-auditable data in data centres (low-risk)
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Risk profiles become manageable, premiums drop, and systematic services profitability grows
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The market experiences healthy Jevons growth⁴—a market incentive to cut debris risk
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Data owners, leveraging inexhaustible data streams, drive continuous growth
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Continuous growth activates incentives to link actuarial networks
Imagine Every Data Centre Capturing Lease Revenue from Idle Assets
Imagine Insurance Industry Growth to 100% of the Space Market
This is the impact of Compute Cost/Benefit Intelligence
Next: The "Data Operating Asset" Opportunity
Money Leaks When Value-Add is Invisible
Modern organizations generate vast amounts of data, fueling business operations and decisions, but lack a way to understand what it costs to systematically produce, refine, and operate compute systems to profitably generate useful data. When data moves through systems, it creates congestion, wasted effort, and unprofitable decisions.
The Bottleneck at the Root of Modern Commerce
Three interlocking operational problems create the root bottleneck:
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Data cannot be reliably converted into operating assets.
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Accurate costs cannot be calculated fast enough to assess profitability.
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Data quality cannot be continuously improved without value add signals.
Organizational Misalignment
There are interlocking cultural barriers - we all speak different languages:
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Data Architects and Engineers need cost metrics to optimize cleaning.
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Data Scientists need to optimize performance to achieve extreme accuracy.
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Business Teams need assets with operating value to optimize for profitability.
Intangibility is the Operating Barrier
Intangible assets are assets that lack physical substance. Customer preferences, patents, actionable insights and predictions, the tacit knowledge of employees in the culture of a business.
Intangible assets have immense business value, often as the driver of decision-making, but that value is hard to measure, manage and operationalize. Intangible assets such as data are hard to use, store, and transfer: as they derive value from knowledge, rights, and future benefits; the value can be subjective and context-dependent; and is vulnerable to technological change and shifts in consumer behavior.
The significance of Data is that it is a fundamental asset: Its value influences and drives the efficiency, operating cost and value-add of compute - the cost to deliver electronically-generated goods and services.
The challenge with data is that it does not possess intrinsic value, so cannot be used to systematically measure and improve the quality of itself. It's awkward to clean and prepare, and therefore difficult to use to profitably standardize and streamline data-driven systems.
There is no good solution, only high-touch boutique work-arounds.
"There is no standard to measure the value of data.” ³
The Need for Rapid Intrinsic Monetization
Today, compute and data costs are difficult to attribute in operation, making compute and data profit and loss calculations largely guesswork. As a result, teams rely on valuation methods that were never designed to improve industrial systems, only used as workarounds to approximate value after the fact.
Without cash-denominated metrics, even basic improvement methods cannot be applied. Data enters operations no daily cash value, leaving systems unable to distinguish signal from waste or prioritize improvement.
Assets need an operating value to make profitable decisions
References
¹ Dan Goldin & Ryan Duffy (2025) recently posted a detailed review of the satellite compute challenge: https://www.peraspera.us/realities-of-space-based-compute/
² DARPA's ML2P RFP (2025) calls for compute cost/benefit calculators: https://www.darpa.mil/research/programs/mapping-machine-learning-physics
³ Mike Fleckenstein, Chief Data Strategist, MITRE: A Review of Data Valuation (MIT: 2023): https://hdsr.mitpress.mit.edu/pub/1qxkrnig/release/1
⁴ (1865) The Coal Question, this UK Royal Commission investigated whether an increase in the efficient use of British coal resources would improve the conservation of the resource. The commission concluded that efficiency lowers cost per unit, which drives greater overall demand, and this can offset the savings. Coal is a finite resource. Data is not, making efficiency the mechanism to sustainably drive growth in the digital age: https://en.wikipedia.org/wiki/The_Coal_Question
- See our blog for data valuation reviews: https://orbintel.blogspot.com/