Dataset opportunity
Mobilityhouse — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Mobilityhouse, 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
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 for vehicles market size was estimated at $4.66 billion in 2024, with a projected CAGR of 17.5% (2025-2034).
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
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
The Mobility House holds a valuable Mobility Telemetry Dataset composed of Time Series data from EV charging `event_streams` and `iot_data`. This granular data on charging sessions, battery performance, and vehicle behavior is specifically structured for developing Predictive Maintenance algorithms, enabling the anticipation of battery degradation and charging hardware failures.
The global automotive predictive maintenance market was valued at $4.66 billion in 2024 and is projected to grow at a CAGR of 17.5%. [2] This significant market size and rapid growth underscore the high value of this data. Despite access complexities like shared data ownership and GDPR sensitivity, the unique, rare insights for optimizing EV fleet operations and battery lifecycle make this dataset a compelling asset for AI buyers. ⚠ Diligence (valuable data, access to negotiate): Data ownership is shared between EV owners, fleet operators, and OEMs (e.g., Renault); GDPR sensitivity regarding individual charging locations and habits; Complex regulatory environment for energy trading and grid data · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Mobilityhouse possesses a proprietary, large-scale time-series dataset capturing the real-world performance of electric vehicles, charging stations, and stationary battery storage systems. This data directly serves the high-demand predictive maintenance use case for industrial AI vendors, enabling them to build and refine algorithms that predict component failure and optimize battery health. In a vehicle predictive maintenance market projected to grow at over 17% annually, this rare dataset offers a significant competitive advantage for developing next-generation maintenance solutions.
See dimension details ↓- Dataset Specificity90
dominant 'iot_data', 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 very high, driven by the rapidly growing automotive predictive maintenance market which is projected to expand at a 17.5% CAGR. [2]
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 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 Audit67
⚠ review — The company's core business is selling intelligent software (ChargePilot®) and energy market services (Cascade EV Aggregator), not holding dormant data, making it a bad fit. Issues: Core business is selling intelligence/software: The company's main products are ChargePilot®, a smart charging and energy management system, and Cascade EV Aggr; Core business is selling a service based on data: They use their technology to offer services like Vehicle-to-Grid (V2G), flexibility aggregation for energy mar; Already monetizing data: The company actively uses vehicle and charging data to provide its core services, such as optimizing charging costs and selling aggrega; Has a public API for data exchange: They offer a 'Charging Data Push-API' to send billing-relevant charging session records to customers, indicating they alread
- Deep Qualification90
✓ pass — The target is a strong data holder, operating a smart charging and energy management platform (ChargePilot®) that generates valuable EV telemetry data. Data ownership is mixed and subject to GDPR, but their privacy policy allows for the use and disclosure of anonymized, aggregated data.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
The company captures proprietary time-series data detailing the battery usage and bidirectional charging of electric vehicles, which is foundational for AI vendors building component degradation models.
Event streams
The dataset contains event streams from thousands of charging stations, providing granular data on load profiles and charging times essential for predicting infrastructure stress and maintenance needs.
Industrial data
Mobilityhouse collects industrial data on stationary battery storage, including discharge cycles and capacity, offering a valuable proxy for modeling long-term vehicle battery health.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
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Deliverable
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Mobilityhouse Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global predictive maintenance for vehicles market size was estimated at $4.66 billion in 2024, with a projected CAGR of 17.5% (2025-2034) (source: Global Market Insights Inc.). Investment score 48.0/100 (confidence 0.49). Recommended action: Acquire.
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