Dataset opportunity
Eletrabus — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Eletrabus, usable for Predictive Maintenance and Anomaly Detection.
Score
69.9
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
Partnership (group-level)
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 = $13.65B in 2025, CAGR 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.
Profile
Dataset profile
Type
Maintenance Logs 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
Eletrabus holds a valuable Time Series dataset comprised of maintenance_logs and iot_data from its fleet of electric buses and retrofitted systems. This granular, real-world industrial_data is structured for direct application in training Predictive Maintenance algorithms, enabling AI buyers to forecast component failures, optimize maintenance schedules, and reduce operational downtime for electric vehicle fleets.
The business value is anchored in the global Predictive Maintenance market, which was valued at USD 13.65 billion in 2025 and is projected to grow with a CAGR of 24.30%. [1] Despite access complexities such as shared data ownership or the need for group-level approval, the rarity of this specialized e-mobility data, combined with the market's high growth, makes it a strategic asset for AI developers seeking a competitive advantage. ⚠ Diligence (valuable data, access to negotiate): Data ownership may be shared with municipal transport authorities or private fleet operators; Telemetry access depends on the specific integration level of the e-Bus or e-Retrofit systems; Subsidiary of a regional transport conglomerate, requiring group-level approval · corporate: subsidiary of Grupo ABC (Setti & Braga).
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Eletrabus possesses a unique, high-rarity dataset detailing the real-world operation of over 1,000 electric buses. This proprietary data is a critical asset for industrial AI vendors developing predictive maintenance solutions, a market projected to reach $13.65B by 2025. The dataset's combination of IoT performance, battery health, and comparative analytics provides the ground truth needed to train models that optimize fleet uptime and reduce operational costs in the rapidly growing electric mobility sector.
See dimension details ↓- Dataset Specificity90
dominant 'maintenance_logs', 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 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 high due to the rapid growth of the Predictive Maintenance market (CAGR 24.30%), which heavily relies on specialized industrial time series data to train effective models. [1]
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 Feasibility15
medium difficulty, subsidiary of Grupo ABC (Setti & Braga)
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 License70
ownership=company_owned, licensing=rights_unclear
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
subsidiary of Grupo ABC (Setti & Braga)
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 Audit92
✓ good target — Eletrabus is a Brazilian manufacturer of electric buses and drive systems that also performs maintenance and retrofitting, making its operational and maintenance logs a valuable, untapped data asset. Issues: The exact number of employees could not be definitively determined to confirm SME status, although 88 employees were mentioned on one platform. [9]; The company's website (eletrabus.com.br) appears to be down or inaccessible, requiring reliance on third-party sources. [2, 4, 5, 7]
- Deep Qualification60
⚠ needs review — Eletrabus is a Brazilian manufacturer of electric buses, not a transport operator. While its vehicles plausibly generate valuable IoT and maintenance data for predictive maintenance, this data is generated by and for its customers (e.g., municipal transport authorities), making ownership and resale rights a primary obstacle. [data is owned by the company's customers]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
The dataset includes real-time IoT performance data from a fleet of over 1,000 electric buses, providing the raw signals on speed, location, and energy use essential for modeling operational stress patterns.
Industrial data
This evidence confirms access to proprietary industrial data on battery health (SOH), including crucial metrics like discharge cycles and temperature, which is the most sought-after information for predicting component failure in electric vehicles.
Maintenance logs
The dataset contains unique comparative analytics contrasting the performance of electric retrofits against traditional combustion vehicles, offering a powerful tool for quantifying the total cost of ownership and maintenance advantages for enterprise clients.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
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Eletrabus Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $13.65B in 2025, CAGR 24.30% (source: Fortune Business Insights). Investment score 69.9/100 (confidence 0.49). Recommended action: Partnership (group-level).
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