Dataset opportunity
Solareur — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Solareur, usable for Predictive Maintenance and Anomaly Detection.
Score
71.9
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
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 was valued at $13.65 billion in 2025 and is projected to grow at a CAGR of 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.
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
Solareur holds a Time Series Maintenance Logs Dataset derived from its role as an EPC partner for third-party solar assets. The dataset contains granular `industrial_data` and `iot_data` streams from operational hardware, providing the high-fidelity, real-world records essential for training robust Predictive Maintenance AI models.
The business value targets the global Predictive Maintenance market, a valuable sector estimated at $13.65 billion in 2025 with a projected CAGR of 24.30%. [4] While rights to aggregate and anonymize this client data require verification in O&M contracts, Solareur's direct access to hardware and data streams as an EPC partner ensures data integrity. This offers a rare opportunity to acquire high-quality iot_data for this high-growth application, justifying the access diligence. ⚠ Diligence (valuable data, access to negotiate): Data is collected from solar assets owned by third-party clients (SMEs and investors); Rights to aggregate and anonymize monitoring data for AI training must be verified in O&M contracts; Company operates as an EPC partner, meaning they have direct access to the hardware and data streams · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Solareur possesses a proprietary, high-rarity dataset combining detailed maintenance logs with real-time IoT data from its industrial solar parks. This unique time-series data is a critical asset for industrial AI vendors developing predictive maintenance solutions. In a market projected to grow at over 24% annually, this dataset offers a rare opportunity to train and validate algorithms on real-world renewable energy operations, a sector undergoing massive expansion.
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 Demand90
AI buyer demand is extremely high, driven by the rapid growth of the Predictive Maintenance market which is expanding at a 24.30% CAGR. [4]
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.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Initialement concentrée sur la prospection foncière pour les développeurs solaires, la plateforme Ferme Solaire change de nom à l’occasion d’une diversification. Elle s’appellera désormais Solmeria et va  </p> <p>L’article <a href="https://www.greenunivers.com/2026/07/solmeria-ex-ferme-solaire-veut-proposer-des-projets-enr-a-lunite-428542/">Solmeria (ex Ferme Solaire) veut proposer des projets EnR à l’unité</a> est apparu en premier sur <a href="https://www.greenunivers.com">GreenUnivers</a>.</p>”
- “<p>Les représentants du personnel d’Urbasolar montent au créneau. Dans un communiqué publié hier, les membres du comité social et économique (CSE), portés par le syndicat des cadres CFE-CGC critiquent les mesures proposées par la direction qu’ils jugent « dérisoires » au regard des revenus et de la trésorerie du propriétaire du développeur-producteur photovoltaïque de Montpellier, l’énergéticien suisse Axpo. […]</p> <p>L’article <a href="https://www.greenunivers.com/2026/07/les-representants-syndicaux-durbasolar-prets-a-la-greve-428482/">Les représentants syndicaux d’Urbasolar”
- “<p>Chaque semaine, GreenUnivers sélectionne les principaux événements professionnels de la transition énergétique. Des rendez-vous qui ont lieu en France et ailleurs dans les secteurs des énergies renouvelables, de l’hydrogène, de la rénovation ou encore de la mobilité électrique. Août 26 La Ref, les Rencontres des entrepreneurs de France, Medef, Paris 28 Universités d’été de l’économie […]</p> <p>L’article <a href="https://www.greenunivers.com/2026/07/lagenda-de-la-transition-energetique-280-424554/">L’agenda de la transition énergétique</a> est apparu en premier sur <”
IoT / sensor data
The company generates time-series data from the real-time monitoring of solar equipment performance, which is essential for training models to detect anomalies and optimize energy production.
Maintenance logs
Solareur creates structured maintenance logs from technician reports on field interventions, providing the critical ground-truth data needed to label failure events for predictive models.
Industrial data
This evidence confirms the data's origin from large, industrial-scale solar park construction and operation, ensuring its complexity and relevance for robust AI applications.
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
JSON, CSV
License
One-time license for use in predictive maintenance AI model training and development. Rights to aggregate and anonymize client data require verification in O&M contracts.
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, real-time time-series maintenance and IoT data from solar assets, directly feeding the high-growth global predictive maintenance market. Its granular nature is critical for training robust AI models in a sector projected for significant expansion.
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
Solareur 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 was valued at $13.65 billion in 2025 and is projected to grow at a CAGR of 24.30% (source: Fortune Business Insights). [4]. Investment score 71.9/100 (confidence 0.49). Recommended action: Acquire.
From the marketplace
Explore live data opportunities
Dhg — Industrial Sensor Dataset Opportunity
View opportunity →mobilityAccess Freight — Industrial Operations Dataset Opportunity
View opportunity →industrialAsicnorth — Industrial Sensor Dataset Opportunity
View opportunity →Data Academy
Learn before you deal
- Why Buy External Data?3 min read
- Buying Data Without Mistakes3 min read
- Your Expertise is Gold for AI3 min read