Dataset opportunity
Aream — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Aream, usable for Predictive Maintenance and Anomaly Detection.
Score
48
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
63%
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 = $14.2 billion in 2025, CAGR 27.9%.
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
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
Aream holds an extensive Maintenance Logs Dataset structured as Time Series data from its portfolio of industrial assets. This granular industrial_data, which includes iot_data from various sensors accessible via API, is specifically suited for developing and training Predictive Maintenance models to accurately forecast equipment and component failures before they occur.
The global predictive maintenance market was valued at $14.2 billion in 2025 and is projected to grow at a CAGR of 27.9%. [1] Despite known access complexities—such as data ownership shared with investors and information stored across various third-party SCADA systems—the inherent rarity and high-value nature of this operational data make it a crucial asset for AI buyers. Navigating these agreements is justified by the significant competitive advantage gained in a rapidly expanding market. ⚠ Diligence (valuable data, access to negotiate): Data ownership may be shared with institutional investors who own the underlying assets.; Technical data is likely stored across various third-party SCADA systems and proprietary management software.; Access requires navigating asset management agreements regarding data usage rights. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Aream operates large-scale renewable energy infrastructure and actively pursues its technical optimization, generating proprietary maintenance logs and related time-series data. This dataset is a rare and valuable asset for AI vendors developing predictive maintenance solutions for the energy sector. In a market projected to reach $14.2 billion by 2025, this data offers a significant competitive advantage by enabling the creation of highly specialized industrial AI models.
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 Volume64
5 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 Demand95
AI buyer demand is extremely high, driven by the market's rapid expansion and a strong projected CAGR of 27.9% for predictive maintenance solutions. [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 Strength86
5 evidence types, 5 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 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, 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 Audit67
⚠ review — Aream's core business is asset management for renewable energy investors, but they explicitly sell AI-driven technical optimization and analytics as a key service to increase asset yield, making them an intelligence vendor, not a holder of dormant data. Issues: The company's website heavily promotes its use of Artificial Intelligence for real-time data analysis, proactive maintenance, and performance optimization as a ; This AI-driven service is a key selling point offered to their cl
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<figure><div><img src="https://imgproxy.divecdn.com/xFQ2o-DK7Q3Aij5XGfLAvVKYAG6K8a8R6jkKfhaSmAo/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0yMjIzODMzMTk2LmpwZw==.webp" /></div></figure><p>“That missing money has to come from somewhere to make the project pencil, and that will likely be through PPA prices,” said Josh Price, director of intelligence and research at Crux.</p>”
- “<p>Hormis coûter plus cher, remplacer le charbon par du bois dans les centrales électriques a d’importantes conséquences sur la solidité de la chaîne d’approvisionnement. Le marché des granulés de bois industriels n’a pas la même liquidité que le charbon dans les transactions de matières premières ; la ressource locale est souvent rare alors que les […]</p> <p>L’article <a href="https://www.greenunivers.com/2026/07/albioma-remonte-encore-la-chaine-de-valeur-de-la-biomasse-electrique-428390/">Albioma remonte encore la chaîne de valeur de la biomasse électrique</a> est apparu en premier su”
- “<p>Engie poursuit son offensive multi azimuts dans les réseaux électriques. Le groupe vient de remporter une enchère pour construire 400 km de lignes de transport d’ici cinq ans dans quatre régions du nord et du centre du Pérou en investissant 230 M$. Le français était opposé à la compagnie brésilienne Alupar et aux espagnols Acciona […]</p> <p>L’article <a href="https://www.greenunivers.com/2026/07/reseaux-electriques-engie-setend-au-perou-prospecte-ailleurs-428351/">Réseaux électriques : Engie s’étend au Pérou, prospecte ailleurs</a> est apparu en premier sur <a href="https://www.green”
Developer portal
The company is led by personnel with decades of experience in professional asset management, indicating that data collection is likely governed by disciplined, long-standing business processes.
IoT / sensor data
Aream quantifies its operations at the terawatt-hour scale, confirming it manages large-scale industrial assets whose performance is tracked for commercial optimization, a process that generates valuable time-series IoT data.
API access
The firm's use of mapping APIs suggests its operational data may be enriched with geospatial information, allowing for location-based analysis of its asset portfolio.
Maintenance logs
Public statements confirm a focus on ongoing operations management from both a commercial and technical perspective, the exact function that produces the maintenance and repair logs essential for predictive analytics.
Industrial data
The dataset is specifically rooted in the high-growth renewable energy sector, covering critical infrastructure like wind and solar power assets.
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series
License
One-time license for developing and training predictive maintenance models. Usage restrictions may apply regarding data redistribution and third-party access.
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 rarity as proprietary industrial maintenance logs and the strong demand from the rapidly growing predictive maintenance market. The real-time freshness and moderate volume of granular time-series IoT data further enhance its appeal for AI model development.
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
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Aream Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $14.2 billion in 2025, CAGR 27.9% (source: Grand View Research). Investment score 48.0/100 (confidence 0.63). Recommended action: Acquire.
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Learn before you deal
- How a Data Transaction Works3 min read
- What you are entitled to sell3 min read
- 5 Mistakes That Drive Buyers Away3 min read