Dataset opportunity
Earthmill — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Earthmill, usable for Predictive Maintenance and Anomaly Detection.
Score
72.1
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
The global Wind Turbine Predictive Maintenance AI market was valued at $1.24 billion in 2024 and is projected to reach $9.83 billion by 2033, growing at a CAGR of 22.8%. [8]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-15
Les députés RN reviennent à la charge sur le moratoire éolien et solaire
greenunivers.com ↗ - 📰press2026-06-15
L’énergie, le nerf de la guerre pour les data centers [Dossier]
greenunivers.com ↗ - 📰press2026-06-15
OKWind perd 24 M€, compte sur une recapitalisation
greenunivers.com ↗ - 📰press2026-06-15
« Certains réfrigérateurs dans les criées sont encore au fioul… » [Loïg Chesnais-Girard]
greenunivers.com ↗ - 📰press2026-06-15
Utility sector outlook deteriorates on affordability concerns: Fitch
utilitydive.com ↗
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.
Profile
Dataset profile
Type
Maintenance Logs 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
Earthmill holds a substantial Time Series dataset comprised of maintenance logs and IoT sensor data from its aggregated fleet of over 800 wind turbines. This collection of industrial and IoT data provides a detailed operational history, including records of component stress, performance degradation, and failure events. Its structure is ideal for training algorithms for the Predictive Maintenance use case, enabling models to forecast equipment failures before they occur.
This data is exceptionally valuable in a rapidly growing market; the global Wind Turbine Predictive Maintenance AI market was valued at $1.24 billion in 2024 and is projected to grow at a CAGR of 22.8%. [8] While access complexities exist, such as shared data ownership with turbine owners and the need for specific data-sharing clauses, the asset's rarity makes it a strategic acquisition. As a unique cross-manufacturer dataset, it offers a comprehensive foundation for developing robust, manufacturer-agnostic predictive models in a market of this market size. ⚠ Diligence (valuable data, access to negotiate): Data ownership is likely shared with individual turbine owners through O&M contracts.; Access to high-resolution sensor data may require specific data-sharing clauses in maintenance agreements.; The company acts as a fleet aggregator for 800+ turbines, creating a unique cross-manufacturer dataset. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Earthmill possesses a proprietary time-series dataset of maintenance logs from its fleet of 800+ turbines across the UK. This data is the essential ground truth for industrial AI vendors developing predictive maintenance algorithms. In a wind turbine AI market projected to reach nearly $10 billion by 2033, this dataset directly enables models that reduce downtime, boost performance, and capture a share of this rapidly growing sector.
See dimension details ↓- Dataset Specificity90
dominant 'maintenance_logs', 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 Demand92
The predictive maintenance market, which is the direct consumer of maintenance logs datasets, is projected to grow at an exceptional CAGR of 32.32% from 2026 to 2035, indicating a massive and rapidly increasing demand for the data needed to
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 Orientation56
2 data-appetite signals (2 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 Audit100
✓ good target — Earthmill is an excellent target, being an SME whose core business is the operational maintenance of over 800 wind turbines, which generates valuable, dormant maintenance and performance data as a by-product. Issues: The company was recently acquired (Feb 2026) by European Green Transition plc out of liquidation from its previous parent, which could complicate decision-makin; The new parent company, European Green Transition, also acquired a majority stake in Anemos Analytics, a
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This evidence indicates the presence of IoT sensor data used for condition monitoring, a critical input for AI models that predict failures and reduce operational costs.
Maintenance logs
This confirms the dataset's origin from a market leader's service operations on over 800 turbines, providing the large-scale, real-world maintenance logs needed to train and validate accurate AI models.
Industrial data
This evidence points to structured data on industrial repairs and upgrades, which is invaluable for training AI to recommend specific interventions that boost performance and extend asset lifespan.
Marketplace
Dataset details
Geographic coverage
UK
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, Logs
License
One-time license for internal use in developing predictive maintenance AI models for wind turbines.
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 dataset is highly valuable due to its proprietary nature and direct application in the rapidly growing wind turbine predictive maintenance market. Its real-time freshness and moderate volume of logs from over 800 turbines make it a critical asset for AI vendors.
Detailed schema & sample available on access request.
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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
Earthmill Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: The global Wind Turbine Predictive Maintenance AI market was valued at $1.24 billion in 2024 and is projected to reach $9.83 billion by 2033, growing at a CAGR of 22.8%. [8]. Investment score 72.1/100 (confidence 0.49). Recommended action: Acquire.
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