Dataset opportunity
Sparkcharge — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Sparkcharge, usable for Predictive Maintenance and Anomaly Detection.
Score
76.1
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
Global Vehicle Predictive Maintenance market is projected to reach $12.3 billion by 2033, growing at a CAGR of 20.5% (2026-2033). [15]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-12
Renaut, Stellantis et Volkswagen unissent leurs voix pour infléchir le "Made in Europe"
journalauto.com ↗ - 📰press2026-06-12
Véhicule de fonction : les règles du jeu se précisent pour les modèles électriques écoscorés
journalauto.com ↗ - 📰press2026-06-12
Bornes : une autre association alerte sur l’opacité tarifaire de la recharge
journalauto.com ↗ - 📰press2026-06-11
Distribution automobile : l’heure délicate des successions familiales
journalauto.com ↗ - 📰press2026-06-11
Stellantis dope une Charger avec une batterie solide
journalauto.com ↗
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
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Sparkcharge possesses a valuable Mobility Telemetry Dataset, presented as a Time Series modality. This dataset is generated directly from Sparkcharge's proprietary physical hardware, the Roadie and PowerHub systems, capturing real-world `event_streams`, `geo_data`, and `iot_data`. Its core strength for the Predictive Maintenance use case lies in the high-resolution battery discharge and health telemetry collected across a diverse range of EV models, providing a rich foundation for developing and training predictive algorithms.
The global Vehicle Predictive Maintenance market is projected to reach $12.3 billion by 2033, expanding at a CAGR of 20.5%. [15] While access to this dataset requires negotiation, as a portion is already utilized for SparkAI's operational optimization, this complexity underscores its rarity and strategic value. The dataset's unique origin and detailed telemetry offer a distinct competitive advantage for an AI buyer aiming to build a superior predictive maintenance solution in a rapidly growing market. [15] ⚠ Diligence (valuable data, access to negotiate): Data is generated by proprietary physical hardware (Roadie, PowerHub); SparkAI already utilizes a portion of the data for operational optimization; Dataset includes high-resolution battery discharge and health telemetry across diverse EV models · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Sparkcharge's evidence proves ownership of a large-scale, proprietary dataset capturing millions of on-demand electric vehicle charging events. This unique time-series and telemetry data is a critical asset for AI vendors building predictive maintenance models for EV batteries and charging hardware. In a vehicle predictive maintenance market projected to exceed $12 billion, this dataset provides the real-world signals needed to predict battery degradation, optimize fleet operations, and create high-value AI 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 Demand85
The automotive predictive maintenance market, which fundamentally relies on mobility telemetry data, is projected to grow at a robust CAGR of 23.9% between 2023 and 2033, indicating very strong and increasing buyer demand.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
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 License92
ownership=owned, licensing=clean
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, 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 Audit83
⚠ review — SparkCharge's core business is selling mobile EV charging hardware and a bundled 'Charging-as-a-Service' (CaaS) which includes a software platform for managing charging operations, making it a seller of intelligence and a poor fit. Issues: The company's primary product is 'Charging-as-a-Service' (CaaS), which is a bundled offering of hardware, energy, and software. [3, 9, 12]; The CaaS offering includes a software platform with real-time monitoring, data insights, and reporting automa
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 contains granular IoT sensor telemetry from the company's mobile charging hardware, offering direct evidence of energy delivery and battery health for modeling component-level performance.
Event streams
This evidence confirms a large-scale event stream detailing over 6.3 million kWh delivered, which includes valuable vehicle-specific charging profiles and usage patterns essential for training robust AI models.
Geospatial data
The dataset includes tabular geospatial data identifying precisely where and when fleet vehicles require off-grid charging, enabling models that predict energy demand and optimize logistics.
Marketplace
Dataset details
Geographic coverage
Global
Time range
Rolling 12 months
Update frequency
Real-time
Delivery
API
Formats
JSON, Time Series
License
One-time license for predictive maintenance model development and training, with restrictions on redistribution of raw data.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This dataset's value is driven by its high-resolution, proprietary EV battery telemetry, crucial for the rapidly growing predictive maintenance market. The real-time freshness and exclusive nature of this data, sourced directly from hardware, position it as a premium asset.
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
Sparkcharge Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Vehicle Predictive Maintenance market is projected to reach $12.3 billion by 2033, growing at a CAGR of 20.5% (2026-2033). [15]. Investment score 76.1/100 (confidence 0.49). Recommended action: Acquire.
From the marketplace
Explore live data opportunities
Flo — Knowledge Base Dataset Opportunity
View opportunity →industrialJbs Tech — Maintenance Logs Dataset Opportunity
View opportunity →mobilityGobolt — Mobility Telemetry Dataset Opportunity
View opportunity →Data Academy
Learn before you deal
- Acquire Rare, Compliant Data3 min read
- Is Your Data Worth Money?3 min read
- What is a Dataset Worth?3 min read