Dataset opportunity
Paua — Mobility Telemetry Dataset Opportunity
Large mobility telemetry dataset held by Paua, usable for Predictive Maintenance and Anomaly Detection.
Score
47.5
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
65%
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 to grow from $5.48 billion in 2025 to $23.39 billion by 2034, CAGR 17.5% (source: Global Market Insights Inc.)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-25
L'émission du Club Trajectoire Durable - Flottes d’entreprise : l’électrique à l’épreuve du réel
journalauto.com ↗ - 📰press2026-06-25
Royaume-Uni : des experts du climat réclament au gouvernement d'accélérer l’électrification du parc roulant
journalauto.com ↗ - 📰press2026-06-24
Nombreuses offres, petits prix… Citroën d'attaque pour le retour du leasing social
journalauto.com ↗ - 📰press2026-06-24
Avec le Peaq, Skoda veut prolonger son succès électrique
journalauto.com ↗ - 📰press2026-06-24
Virginie de Pierrepont est élue présidente de Mobilians et succède à Francis Bartholomé
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.
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Large
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Paua provides a comprehensive Mobility Telemetry Dataset featuring Time Series data from over 20 electric vehicle charging networks. The dataset includes `transaction_data`, `iot_data`, real-time `geo_data`, and `event_streams`, offering a granular view of charging behavior and hardware performance. This rich combination of sources is specifically structured to enable the development of Predictive Maintenance algorithms for identifying and forecasting potential failures in charging infrastructure and EV components.
The global Automotive Predictive Maintenance market represents a substantial and high-growth opportunity, projected to expand from $5.48 billion in 2025 to $23.39 billion by 2034, reflecting a CAGR of 17.5%. [10] While accessing this dataset requires navigating sub-licensing terms and adhering to high-security compliance for sensitive PII and location data, its rarity and direct applicability to this valuable market make it a strategic asset for AI buyers aiming to build a competitive advantage. [10] ⚠ Diligence (valuable data, access to negotiate): Aggregates data from 20+ different charging networks which may have restrictive sub-licensing terms; Contains sensitive PII including driver home addresses for home-charging reimbursement; Real-time telematics and location data requires high security compliance · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence confirms Paua possesses a proprietary stream of real-time EV charging activity and telemetry data, captured across public, home, and workplace environments. This unique dataset is a critical asset for industrial AI vendors developing predictive maintenance models for the rapidly expanding electric vehicle market. As the global predictive maintenance for vehicles sector is set to quadruple to over $23 billion by 2034, this data provides the raw fuel for algorithms that optimize fleet management, reduce downtime, and capture significant market share.
See dimension details ↓- Dataset Specificity100
dominant 'iot_data', sector mobility, 4 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 (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume70
6 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 Value94
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 high, driven by the fast-growing Predictive Maintenance for Vehicles market which is expanding at a 17.5% CAGR and requires rich, real-world telemetry data for developing advanced analytics models. [10]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility14
open/API access
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility48
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength89
5 evidence types, 6 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
ownership=mixed, 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 Audit58
⚠ review — Paua's core business is selling a software platform, analytics dashboard, and data APIs to help businesses manage electric vehicle fleets, making it a seller of intelligence, not a holder of dormant data. Issues: Company's core product is selling intelligence and data access, which is an explicit exclusion criterion.; Paua is a software/data aggregator, not a company with a 'real operational business' like owning fleets or physical charging hardware. The data is from third-pa; The com
- Deep Qualification90
✓ pass — Paua operates an EV charging payment platform, making it a holder of a valuable and coherent mobility telemetry dataset as a byproduct of its core business. However, this data contains sensitive PII and is aggregated from numerous networks, posing significant licensing and compliance challenges.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Downloads / exports
Tabular records of content downloads demonstrate direct engagement with fleet managers, providing a valuable source of lead generation and customer profiling for B2B service providers.
Event streams
Continuous time-series event streams capture real-time charging activity across a diverse network, forming the core dataset for training predictive maintenance algorithms.
Transaction data
Tabular transaction data links vehicle usage and driver activity to financial information, enabling models that optimize total cost of ownership and fleet spending.
Geospatial data
Geospatial data maps over 93,000 charging points across the UK, providing the essential location-based context needed for network optimization and route planning models.
IoT / sensor data
Time-series IoT data aggregates charging events from multiple sources including home and workplace, indicating a sophisticated capture mechanism essential for building a complete vehicle-level operational history.
Coverage
Scanned sources
Deliverable
Premium dataset report
Paua Mobility Telemetry — a Large mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance for Vehicles market to grow from $5.48 billion in 2025 to $23.39 billion by 2034, CAGR 17.5% (source: Global Market Insights Inc.). Investment score 47.5/100 (confidence 0.65). Recommended action: Data Sharing Agreement.