Dataset opportunity
Pauatech — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Pauatech, usable for Predictive Maintenance and Anomaly Detection.
Score
45
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 automotive predictive maintenance market = $22 billion in 2023, CAGR 18.6% (source: Market.us)
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.
- 🤝Data partnership
Integration with over 20 charging networks across the UK
source ↗
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Aggregated / third-party — licensing rights to clarify · PII/regulated
Buyer persona
Industrial AI & maintenance-optimization vendors
Pauatech holds a valuable Mobility Telemetry Dataset structured as a Time Series, which integrates geo_data, iot_data, and transaction_data from electric vehicle charging points. This granular, real-world operational data is specifically suited for developing and training Predictive Maintenance algorithms, enabling the anticipation of hardware failures and the optimization of network uptime by analyzing usage patterns and component stress.
The global automotive Predictive Maintenance market was valued at $22 billion in 2023 and is projected to grow at a CAGR of 18.6%. [1] Despite access complexities, such as data aggregation from multiple CPOs and shared ownership with fleet customers, this dataset represents a significant opportunity. Its value is heightened by the fact that it contains largely unmonetized raw behavioral data, a rare asset for AI buyers looking to gain a competitive edge in a rapidly expanding market. [1] ⚠ Diligence (valuable data, access to negotiate): Data is aggregated from multiple third-party Charge Point Operators (CPOs).; Ownership of granular charging telemetry may be shared with fleet customers.; Primary business is a roaming/payment solution, leaving raw behavioral data largely unmonetized. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Pauatech's ownership of a proprietary dataset detailing the real-world operational telemetry from over 43,000 electric vehicle charging connectors. The rich time-series and transactional data is a critical asset for industrial AI vendors developing predictive maintenance models to anticipate hardware failures and optimize uptime. In a market rapidly shifting towards electrification, this dataset provides a unique competitive edge for creating sophisticated maintenance-optimization solutions for fleet operators and service providers.
See dimension details ↓- 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 Demand90
AI buyer demand is exceptionally high, driven by the urgent need for training data in a market forecasted to grow at a powerful **18.6% CAGR**. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/regulated
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
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 License18
ownership=aggregated, licensing=rights_unclear
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 Orientation39
1 data-appetite signals (1 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high — 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. - 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. - ICP Audit50
⚠ review — Paua Tech's core business is selling a software platform and API for EV fleet charging management, which is a form of selling intelligence, making it a bad fit. Issues: Company's core product is a technology platform (SaaS) and API for managing and paying for EV charging, not a by-product of a non-data business. [3, 9, 11]; The company's business model is to aggregate data from third-party charge point operators and provide software/intelligence to fleet managers. [6, 9, 11]; They explicitly sell access to their data via APIs as a product for integration into fleet and finance systems. [3, 17]; This company is a technology/software vendor, a category explicitly excluded by the ICP. [14]
- Deep Qualification90
✓ pass — The target holds a coherent and valuable dataset as a byproduct of its core business, but data ownership is mixed and licensing is constrained by GDPR, posing significant hurdles to direct monetization.
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 time-series telemetry, including energy consumption, duration, and connector-level events, which is essential for training AI models to predict component failure and optimize charging infrastructure.
Geospatial data
It includes tabular geospatial data identifying high-demand charging hubs and usage patterns, allowing for strategic resource planning and network optimization based on real-world fleet behavior.
Transaction data
The dataset provides unified transactional data reflecting the economic activity across diverse fleet types, enabling the calculation of financial impact and ROI for predictive maintenance interventions.
Marketplace
Dataset details
Detailed schema & sample available on access request.
Coverage
Scanned sources
Deliverable
Premium dataset report
Pauatech Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global automotive predictive maintenance market = $22 billion in 2023, CAGR 18.6% (source: Market.us). Investment score 45.0/100 (confidence 0.49). Recommended action: Acquire.