Dataset opportunity
Satep — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Satep, usable for Predictive Maintenance and Anomaly Detection.
Score
69
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
Data Sharing Agreement
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 = $14.2B 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.
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
other
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Satep holds a valuable Time Series dataset comprised of extensive maintenance_logs, including iot_data and other industrial_data, from its nationwide operations in HVAC, plumbing, and electrical systems. This granular, real-world data on equipment performance and interventions provides a robust foundation for training high-accuracy Predictive Maintenance models, designed to anticipate failures in residential and commercial building systems before they occur.
The global Predictive Maintenance market is a significant and rapidly expanding sector, valued at USD 14.2 billion in 2025 with a projected CAGR of 27.9%. [1] Despite access complexities such as data distribution across 8+ subsidiaries, heterogeneous systems, and strict GDPR requirements for customer information, the dataset's unique scope and direct applicability to this high-growth market make it a rare and strategic asset for AI buyers aiming to secure a competitive advantage. ⚠ Diligence (valuable data, access to negotiate): Data is distributed across multiple regional subsidiaries (8+ companies); Contains residential customer information requiring strict GDPR compliance; Technical data likely stored in heterogeneous ERP/maintenance management systems · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Satep holds a proprietary dataset of maintenance logs from a large-scale network of industrial heating, ventilation, and air conditioning (CVC) systems. This high-rarity, time-series data is precisely what industrial AI vendors require to build and refine predictive maintenance algorithms. In a market growing at nearly 28% annually, this dataset provides a crucial competitive edge for optimizing asset performance and reducing operational downtime.
See dimension details ↓- Dataset Specificity74
dominant 'maintenance_logs', sector other, 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 exceptionally high, driven by the market's explosive growth, which is projected at a 27.9% CAGR as companies race to adopt data-driven maintenance strategies. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
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 License28
ownership=mixed, licensing=gdpr_sensitive
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, 1 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 Audit75
✓ good target — Satep is a holding company that acquires and consolidates a network of local HVAC installation and maintenance SMEs, making the underlying operational companies, rather than the holding itself, the source of valuable maintenance data. Issues: Satep itself is a holding company ('activités des sociétés holding') and does not seem to have direct operational activities. [1]; The actual operational business and data generation (maintenance logs) are within the numerous local SMEs that Satep has acquired. [8, 9, 10]; The target is fragmented; one would need to engage with the individual companies within the Satep network (e.g., Le Thiec, Axe Énergies, Rhin Climatisation) rat; The structure is complex, acting as a network or group rather than a single operational entity, which could complicate a data deal. [2, 3]
- Deep Qualification80
✓ pass — Satep is a services company in the energy transition sector, acting as a holding for a network of local installation and maintenance firms. It does not sell data as a core product. The 'Maintenance Logs Dataset' is a coherent byproduct of its activities, but data access is complex due to its distributed nature across 11+ subsidiaries and GDPR sensitivity from serving over 60,000 residential and professional clients.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
This evidence confirms the existence of maintenance logs from active heating, ventilation, and air conditioning (CVC) systems, providing the ground-truth data essential for training failure-prediction models.
IoT / sensor data
The company's work with modern heat pumps, solar solutions, and home automation indicates the generation of time-series IoT data, which is critical for correlating equipment behavior with maintenance events.
Industrial data
Satep's service to over 60,000 clients through a technical network demonstrates the dataset's potential scale and diversity, offering a robust foundation for building generalizable industrial AI solutions.
press
- “<figure><div><img src="https://imgproxy.divecdn.com/Z5nV05zIHgUViXl_VeRfPj5PkcO1-rOAmzBDh6VKKNM/g:nowe:3:128/c:4168:2354/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0xMzAzMzk3NDcyLmpwZw==.webp" /></div></figure><p>The Texas Energy Fund has allocated more than $4 billion as part of the state’s effort to boost grid reliability. The award to SPS covers a drone-based pole inspection program and live monitoring.</p>”
Marketplace
Dataset details
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
Satep Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $14.2B in 2025, CAGR 27.9% (source: Grand View Research). [1]. Investment score 69.0/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
From the marketplace
Explore live data opportunities
Omnifab — Maintenance Logs Dataset Opportunity
View opportunity →industrialApl Datacenter — Maintenance Logs Dataset Opportunity
View opportunity →otherAgriflight — Industrial Operations 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