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.
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.