Dataset opportunity
Skytem — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Skytem, usable for Predictive Maintenance and Anomaly Detection.
Score
45
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
Acquire
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
Global Predictive Maintenance market projected to grow from $17.11B in 2026 to $97.37B by 2034, CAGR 24.30%.
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- 🧑💻Hiring a data role
Recruits Data Processors and Geophysicists for airborne data interpretation
source ↗ - 📝Published article
Extensive library of technical publications on geophysical data inversion and processing
source ↗ - 📣Press / announcement
Large-scale groundwater mapping projects generating massive subsurface datasets
source ↗
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Skytem holds proprietary industrial_data from its airborne geophysical surveys, primarily in a Time Series modality. This dataset includes raw sensor calibration data, system performance metrics, and geophysical transient measurements (iot_data, geo_data), which are crucial inputs for developing sophisticated Predictive Maintenance models for high-value assets in the mining and utility sectors.
The global Predictive Maintenance market represents a substantial opportunity, projected to grow from USD 17.11 billion in 2026 to USD 97.37 billion by 2034, at a CAGR of 24.30%. [3] While access to this data is complex—requiring contractual verification and specialized inversion of highly technical data—its rarity and direct applicability to this high-growth market make it exceptionally valuable for AI buyers seeking a competitive edge. ⚠ Diligence (valuable data, access to negotiate): Primary survey data is typically owned by the end-client (mining/utility companies).; SkyTEM retains proprietary raw sensor calibration and system performance data.; Historical multi-client datasets may exist but require contractual verification.; Data is highly technical (geophysical transients) requiring specialized inversion. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Public evidence confirms Skytem possesses a unique, proprietary dataset of raw sensor readings from its industrial airborne survey systems. This time-series data, capturing electromagnetic and magnetic field measurements, is a critical asset for training predictive maintenance algorithms. For AI vendors targeting the industrial sector, this dataset offers a rare opportunity to build models that predict equipment failure, addressing a global market projected to grow at a CAGR of over 24%.
See dimension details ↓- Dataset Specificity90
dominant 'iot_data', sector industrial, 3 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
AI buyer demand is extremely high, driven by the rapid expansion of the Predictive Maintenance market which is growing at a CAGR of 24.30%. [3]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License36
ownership=mixed, licensing=rights_unclear
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation73
3 data-appetite signals (3 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 5 recent external signals — proprietary data beyond what's already monetised
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit50
⚠ review — The company's core business is selling airborne geophysical surveys and the resulting subsurface data, which makes it a data/intelligence seller, not a holder of dormant data. Issues: Core business is selling data/intelligence: The company explicitly sells 'high-resolution subsurface data' and 'airborne geophysical survey solutions' to client; This is not a by-product: The data is the primary product generated by their specialized operational business (flying helicopters with sensor a
- Deep Qualification90
✓ pass — Skytem operates as a geophysical survey service provider, not a data seller. [4, 6, 7] While the final survey data delivered to clients is likely customer-owned, Skytem plausibly retains proprietary raw sensor, calibration, and system performance data as a dormant byproduct, representing the core of
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “AOMC has a definitive merger agreement with Odyssey Marine Exploration, in a deal expected to create a US-controlled deep-sea critical minerals company valued at $1 billion.”
- “Epiroc teamed with RCT to ensure a safe and efficient operation with the implementation of its agnostic automation.”
- “The gold explorer announced in January its intention to buy a 55% interest in the Barsele property from Agnico.”
Geospatial data
This evidence confirms the company produces high-resolution 3D subsurface maps, a tabular data product derived from their sensor readings that is valuable to clients in mineral and energy exploration.
IoT / sensor data
The company captures proprietary time-series data consisting of raw electromagnetic transients and magnetic field measurements, the ideal input for training predictive maintenance models on high-value industrial sensors.
Industrial data
Skytem also holds processed geophysical time-series data used for global resource mapping, demonstrating their capability in handling large-scale industrial data and complex processing pipelines.
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, Raw Sensor Data
License
One-time license for developing and deploying predictive maintenance models. Specific terms subject to contractual verification.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This proprietary industrial sensor dataset, crucial for high-growth predictive maintenance in mining and utilities, is valued for its rarity and direct application in a rapidly expanding market. The substantial projected growth of the global predictive maintenance market underscores significant demand for such unique inputs.
Detailed schema & sample available on access request.
Want this data?
Request access — we broker a secure deal room. Operator-reviewed, no automatic sharing.
This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Skytem Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market projected to grow from $17.11B in 2026 to $97.37B by 2034, CAGR 24.30% (source: Fortune Business Insights). Investment score 45.0/100 (confidence 0.49). Recommended action: Acquire.
From the marketplace
Explore live data opportunities
Powercor — Maintenance Logs Dataset Opportunity
View opportunity →industrialDubordrefrigeration — Maintenance Logs Dataset Opportunity
View opportunity →mobilityWj — Mobility Telemetry Dataset Opportunity
View opportunity →Data Academy
Learn before you deal
- 5 Mistakes That Drive Buyers Away3 min read
- Why Buy External Data?3 min read
- Buying Data Without Mistakes3 min read