Dataset opportunity
Zeplug — Mobility Telemetry Dataset Opportunity
Large mobility telemetry dataset held by Zeplug, usable for Predictive Maintenance and Anomaly Detection.
Score
66.9
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
51%
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
$$$ — high AI buyer demand
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-05
Jungheinrich teste des batteries sodium-ion pour ses chariots
supplychainmagazine.fr ↗ - 📰press2026-06-05
L’agenda de la transition énergétique
greenunivers.com ↗ - 📰press2026-06-04
EnergyX, Wildcat Discovery Technologies team up to build ‘battery mecca’ in Texas
mining.com ↗ - 📰press2026-06-04
Colorado co-op delivers 100% renewables in March, a first
utilitydive.com ↗ - 📰press2026-06-04
Inthy accélère dans les camions électriques, renonce à l’hydrogène
greenunivers.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.
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Large
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Public web signals indicate Zeplug (mobility sector) holds a mobility telemetry dataset (time series). Detected via data_volume, geo_data, iot_data evidence across 1 sources. Dominant evidence: iot_data. ⚠ Diligence (valuable data, access to negotiate): Data includes GDPR-sensitive personal information related to EV charging sessions.; Company is an operating subsidiary of Zinc Invest, with significant investment from ICG Infra.; Data is generated from customer usage of charging infrastructure. · corporate: subsidiary of Zinc Invest.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Zeplug possesses a highly valuable and proprietary Mobility Telemetry Dataset, primarily composed of Time Series data derived from its extensive EV charging infrastructure. This unique data directly addresses the high market demand for predictive maintenance solutions, making it exceptionally compelling for Industrial AI and maintenance-optimization vendors. Its rarity and direct applicability to a critical, high-demand AI use-case position it as a significant opportunity for enhancing operational efficiency and reducing downtime in the rapidly expanding electric mobility sector.
See dimension details ↓- Right to License62
ownership=owned, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Dataset Specificity78
dominant 'iot_data', sector mobility, 2 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume74
4 evidence hits, explicit data-volume mention
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 Value74
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
The global automotive predictive analytics market, which relies on mobility telemetry data for AI-driven predictive maintenance, is projected to grow at a Compound Annual Growth Rate (CAGR) of 29.1% from 2025 to 2033.
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 Feasibility15
medium difficulty, subsidiary of Zinc Invest
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength65
3 evidence types, 4 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Corporate Independence50
subsidiary of Zinc Invest
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 Audit83
✓ good target — Zeplug operates a real operational business providing EV charging solutions, generating valuable telemetry data as a by-product, and does not appear to be actively selling this data or derived intelligence. Issues: Zeplug is a well-funded scale-up with 200-350 employees and over $240M in funding, making it a mid-sized company rather than a small SME, which is a slight devi
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 Zeplug's collection of IoT telemetry data from its charging infrastructure, detailing remote consumption monitoring and maintenance events, which is highly sought after by AI buyers for predictive maintenance and operational optimization.
Geospatial data
This data type confirms Zeplug's collection of geospatial data related to charging station installations, providing insights into strategic placement based on user parking habits, valuable for infrastructure planning and market analysis.
Data-volume signal
This evidence highlights Zeplug's ambitious plan to deploy over 100,000 charge points in Europe by 2025, signaling a significant and rapidly expanding data volume for future AI applications.
Coverage
Scanned sources
Deliverable
Premium dataset report
Zeplug Mobility Telemetry — a Large mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: $$$ — high AI buyer demand. Investment score 66.9/100 (confidence 0.51). Recommended action: Data Sharing Agreement.