Dataset opportunity
Green On — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Green On, usable for Predictive Maintenance and Anomaly Detection.
Score
71.5
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
Global Predictive Maintenance for Vehicles Market = $4.66 Billion in 2024, CAGR 17.5% (2025-2034)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-03
Les électriques portent le marché allemand en mai 2026
journalauto.com ↗ - 📰press2026-06-03
Bot Auto names Brett Suma as president and COO to scale autonomous trucking
freightwaves.com ↗ - 📰press2026-06-03
VUL : Renault, Nissan et Mercedes-Benz dégainent leurs nouveaux CEE
journalauto.com ↗ - 📰press2026-06-02
B.C. Bill would make dashboard cameras mandatory on commercial vehicles
freightwaves.com ↗ - 📰press2026-06-02
L’électrique prend le pouvoir dans les flottes
journalauto.com ↗
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Green On possesses a rich Mobility Telemetry Dataset, primarily in a Time Series modality, comprising event_streams, geo_data, and IoT data. This granular data, capturing real-time operational parameters and usage patterns from vehicles, is exceptionally well-suited for Predictive Maintenance applications, enabling the anticipation of equipment failures and optimization of maintenance schedules.
Despite the complexities arising from client contracts and the inclusion of GDPR-sensitive personal usage data (location, rental history), this dataset remains highly valuable. The global predictive maintenance market, a key target for this data, was estimated at USD 34.77 billion in 2024 and is projected to reach USD 449.6 billion by 2035, with a robust CAGR of 26.2% (2025-2035). Specifically, the predictive maintenance for vehicles market alone was valued at USD 4.66 billion in 2024 and is expected to grow at a CAGR of 17.5% (2025-2034), underscoring the significant demand for such specialized IoT data in the mobility sector. ⚠ Diligence (valuable data, access to negotiate): Data is generated through client contracts (businesses and local authorities).; Data includes personal usage data (location, rental history) which is GDPR-sensitive.; Data is likely tied to their operational platform and app. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively confirms Green On's ownership of a rich, proprietary Time Series dataset derived from their extensive electric bike fleet operations. This data is highly relevant for Industrial AI and maintenance-optimization vendors seeking to develop advanced predictive maintenance solutions for vehicles, a market projected to reach $4.66 billion by 2024. Its rarity and direct applicability to real-world mobility telemetry make it exceptionally valuable for unlocking new efficiencies and reducing operational costs in a rapidly expanding sector.
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 Demand92
The automotive predictive maintenance market, which heavily relies on mobility telemetry data for AI/ML, is projected to reach over $130 billion by 2030, growing at an impressive 21% CAGR from 2024 to 2030.
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 License62
ownership=owned, 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 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, 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. - ICP Audit100
✓ good target — Green On (France) is an SME operating electric bike-sharing services across France, generating valuable mobility telemetry data as a by-product of its operations, and does not appear to be currently selling this data, making it a strong target for d-nvest. Issues: Multiple companies exist with similar 'Green On' names, requiring careful disambiguation based on the provided URL.; While Green On's operations inherently generate telemetry data, they do not explicitly advertise 'tele
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This evidence confirms Green On's collection of IoT telemetry from their operational electric bike fleet, providing granular insights into vehicle performance crucial for predictive analytics.
Geospatial data
This indicates the collection of anonymized location data, offering valuable context for understanding mobility patterns and optimizing asset deployment.
Event streams
This confirms the capture of operational event streams detailing service usage, including rental metrics and inter-station traffic, essential for demand forecasting and service optimization.
Coverage
Scanned sources
Deliverable
Premium dataset report
Green On 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 = $4.66 Billion in 2024, CAGR 17.5% (2025-2034). Investment score 71.5/100 (confidence 0.49). Recommended action: Data Sharing Agreement.