Dataset opportunity
Actamarine — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Actamarine, usable for Predictive Maintenance and Anomaly Detection.
Score
72.9
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
56%
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 in Maritime Market = $433 Million in 2024, CAGR 21.6%.
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.
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Actamarine possesses a Mobility Telemetry Dataset structured as Time Series data, derived from its developer_portal, event_streams, industrial_data, and iot_data sources. This dataset, containing detailed vessel telemetry and system logs, is directly suited for developing high-fidelity Predictive Maintenance models to anticipate equipment failures in maritime assets.
The global market for Predictive Maintenance in the maritime sector is estimated at $433 Million in 2024, with a projected 21.6% CAGR, indicating intense buyer demand. [1] Despite access complexities, such as proprietary logs requiring hardware-level extraction or confidentiality agreements, the rarity and high operational value of this industrial_data make it a crucial asset for AI buyers seeking a competitive advantage in this rapidly growing market. ⚠ Diligence (valuable data, access to negotiate): Operational data may be subject to specific charterer confidentiality agreements (e.g., wind farm developers).; Vessel telemetry and DP2 system logs are proprietary but might require hardware-level extraction.; Safety and transfer records are high-value but involve operational sensitivity. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Actamarine owns a proprietary telemetry dataset capturing continuous engine performance and operational data from its specialized offshore support vessels. This high-rarity, real-world data is essential for industrial AI vendors developing predictive maintenance models for the maritime sector. In a market growing at over 21% annually, this dataset provides the ground truth needed to predict component failures, optimize vessel uptime, and capture a significant share of the rapidly expanding maritime AI opportunity.
See dimension details ↓- Dataset Specificity90
dominant 'iot_data', sector mobility, 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 Volume58
4 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 a fast-growing market with a 21.6% CAGR for predictive maintenance solutions in the maritime sector. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility40
open/API access
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility4
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength74
4 evidence types, 4 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License70
ownership=company_owned, 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 Orientation22
0 data-appetite signals (0 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high — 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 Audit100
✓ good target — Acta Marine is an ideal target as it's a maritime support SME with a fleet of over 40 vessels, generating proprietary telemetry data as a by-product of its core business, and does not currently sell this data.
- Deep Qualification80
✓ pass — Actamarine is a vessel operator for the offshore wind industry, generating valuable telemetry and operational data highly relevant for predictive maintenance. However, data ownership is likely shared with or restricted by their clients (e.g., RWE, Vestas) under long-term charter agreements, and no public documents clarify data resale rights.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Developer portal
This confirms the data originates from vessels chartered by leading industry developers and contractors, validating its relevance for high-stakes commercial maritime applications.
IoT / sensor data
The dataset contains continuous time-series data streams directly from vessel IoT systems, capturing critical engine performance and positioning information essential for training predictive maintenance algorithms.
Industrial data
Evidence of over 50,000 gangway connections demonstrates the dataset's high volume and operational density, providing a rich source of events to model component wear-and-tear and operational efficiency.
Event streams
The data includes performance tracking during adverse weather conditions, offering rare and valuable event streams for modeling equipment stress and predicting failures at the edge of operational limits.
Marketplace
Dataset details
Detailed schema & sample available on access request.
Coverage
Scanned sources
Deliverable
Premium dataset report
Actamarine Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance in Maritime Market = $433 Million in 2024, CAGR 21.6% (source: Market.us). Investment score 72.9/100 (confidence 0.56). Recommended action: Acquire.
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