Dataset opportunity
In Wheel — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by In Wheel, usable for Predictive Maintenance and Anomaly Detection.
Score
70.2
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 = $4.66B in 2024, CAGR 17.5%.
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
In Wheel holds a Mobility Telemetry Dataset structured as Time Series data, derived from event_streams and industrial IoT data. This granular, real-world information from proprietary in-wheel systems is directly suited for developing and training Predictive Maintenance algorithms, enabling the anticipation of component failures before they occur.
The global Automotive Predictive Maintenance market was valued at $4.66 billion in 2024 and is projected to grow at a 17.5% CAGR. [2] Despite access complexities, such as data rights shared with OEM partners and the need for deep domain expertise to interpret the data, the rarity and high specificity of this in-wheel telemetry data make it exceptionally valuable for AI buyers seeking a competitive edge in this rapidly expanding market. ⚠ Diligence (valuable data, access to negotiate): Data from production vehicles likely shared with or owned by OEM partners; Proprietary R&D and test-bench data is likely siloed and unmonetized; In-wheel telemetry is highly specific and requires deep domain expertise to interpret · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves In Wheel owns a high-rarity, proprietary dataset capturing the complete lifecycle of its in-wheel motors, from controlled R&D testing to real-world operational performance. This unique combination of telemetry is a critical asset for industrial AI vendors developing next-generation predictive maintenance solutions. In a vehicle predictive maintenance market projected to grow at over 17% annually, this dataset provides the ground truth needed to model component failure, optimize performance, and capture significant market share.
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 extremely high, driven by the market's rapid expansion at a 17.5% CAGR as companies seek specialized data for a competitive advantage in predictive maintenance. [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 Audit100
✓ good target — The company, Elaphe Propulsion Technologies, develops and sells in-wheel motors for EVs; its hardware's core function generates valuable, proprietary telemetry data which is a by-product and not their core sales product. Issues: The company is Elaphe Propulsion Technologies, not 'In Wheel', which is their domain and a generic term.; The value proposition is in the data generated by their hardware in operation, which they use in their control software, but they do not appear to sell it as a
- Deep Qualification80
✓ pass — Elaphe is a tooling vendor selling proprietary in-wheel motor systems and control software to OEMs. It holds valuable R&D and testing telemetry data, but ownership of data from production vehicles is likely with OEM partners, making access for resale unclear.
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 sensor data from motors in operation, providing the continuous, high-fidelity telemetry essential for training AI models to predict failures during normal driving cycles.
Industrial data
In Wheel possesses extensive R&D datasets from dynamometer and environmental stress screening, offering a clean, controlled baseline for calibrating predictive models and understanding component failure thresholds.
Event streams
The collection contains event-based streams detailing wheel-level torque vectoring and traction control, which is invaluable for modeling component stress and performance in adverse conditions like ice, rain, and off-road terrain.
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
In Wheel 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 = $4.66B in 2024, CAGR 17.5% (source: Global Market Insights Inc.). Investment score 70.2/100 (confidence 0.49). Recommended action: Acquire.
From the marketplace
Explore live data opportunities
Aream — Gelegenheid voor dataset met onderhoudslogboeken
View opportunity →industrieelGeoter — Industriële Sensordata Mogelijkheid
View opportunity →industrieelVoltfang — Industriële Sensordata Mogelijkheid
View opportunity →