Dataset opportunity
Waat — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Waat, usable for Predictive Maintenance and Anomaly Detection.
Score
69.6
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
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 size (indicative estimate)
Global Predictive Maintenance Market = $8.7 Billion in 2023, CAGR 28.5%.
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.
- 📣Press / announcement
Raised €100M in September 2025 to accelerate digital expansion and tech leadership.
source ↗ - ✨Signal
Appointed a Director of Marketing and Digital to lead digital strategy and MyWAAT app evolution.
source ↗ - 📦Data product
MyWAAT app tracks consumption, charging history, and real-time station availability for 40k+ users.
source ↗
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Waat possesses a valuable Mobility Telemetry Dataset structured as Time Series evidence. This dataset integrates geo_data, IoT operational data from charging stations, and transaction_data, providing a comprehensive view of equipment usage and performance. This rich, multi-faceted data is ideal for developing a Predictive Maintenance AI use-case, enabling the anticipation of hardware failures across its network of 70,000 partner stations and optimizing repair schedules.
The global market for predictive maintenance is substantial and rapidly expanding, estimated at $8.7 Billion in 2023 and projected to grow at a CAGR of 28.5%. [11] Despite known access complexities, such as the presence of PII, third-party data agreements, and recent valuation changes, the rarity and depth of this real-world operational data offer a significant competitive advantage. For AI buyers, acquiring this dataset is a strategic investment to capitalize on a high-growth market. ⚠ Diligence (valuable data, access to negotiate): Data contains PII including user charging locations, habits, and payment details (GDPR sensitive).; Ownership of data from 70,000 partner stations is likely aggregated and subject to third-party agreements.; Company recently raised €100M, increasing valuation and potentially complicating data acquisition terms. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Waat owns a proprietary, multi-modal dataset detailing the real-world performance of a large-scale EV charging network. The data combines granular time-series sensor readings with rich geographic and transactional context from over 70,000 points and 40,000 users. For AI vendors in the rapidly growing $8.7 billion predictive maintenance market, this is a critical asset for training models that can predict hardware failure, optimize uptime, and reduce operational costs for charging infrastructure.
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 Demand95
AI buyer demand is exceptionally high, driven by the market's rapid growth at a 28.5% CAGR, which creates an urgent need for granular, real-world telemetry data to build effective predictive models. [11]
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 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 Orientation73
3 data-appetite signals (3 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 Audit92
✓ good target — Waat is a good target as it's an operational business installing and managing EV charging stations, generating valuable telemetry data as a by-product of its core service, not as its primary product. Issues: The company is classified as an 'Entreprise de Taille Intermédiaire (ETI)' which is on the larger side of an SME, with 100-199 employees. [1, 13]; Customer reviews are mixed, with some clients reporting issues with service reliability and customer support, which could imply operational challenges. [2, 4, 5
- Deep Qualification90
✓ pass — Waat is an operator of EV charging infrastructure, not a data seller. The 'Mobility Telemetry Dataset' is a coherent byproduct of its core service. A recent €100M fundraising confirms its growth trajectory but data access is constrained by GDPR and a complex ownership structure involving end-users and property owners.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “Après avoir dévoilé sa nouvelle Megane E-Tech, Renault révèle enfin ses tarifs. La berline compacte, qui affiche maintenant une autonomie de 500 km, s'affiche à partir de 37 500 euros. Cela peut même descendre à 29 260 euros avec les aides maximales.”
- “<figure><div><img src="https://imgproxy.divecdn.com/--2mNr43uurbxoELE6Som48pydUz6N0f74OHW13q-V8/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9Ub3lvdGFBdXJpc0h5YnJpZEJ1cm5hc3RvblVLLmpwZw==.webp" /></div></figure><p>Legacy automakers have the added complexity of having to run different supply chains for internal combustion engine, hybrid and EV powertrains.</p>”
- “En recul sur le premier semestre 2026, le constructeur sud-coréen relativise ses performances en mettant en avant la qualité de son mix de ventes, la forte progression de l'électrique et un ambitieux calendrier de lancements. Hyundai se prépare à relancer la dynamique de la marque dès la fin de l'année.”
IoT / sensor data
This evidence indicates the presence of granular time-series sensor data from EV chargers, capturing power curves and energy consumption, which is essential for training algorithms to detect failure signatures and predict maintenance needs.
Geospatial data
The dataset includes tabular location data for over 70,000 charging points, enabling models to correlate performance and failure rates with specific geographic and site-type variables like residential versus corporate usage.
Transaction data
This evidence points to detailed transactional records from over 40,000 users, which provides a powerful proxy for usage frequency and load, allowing for more accurate, behavior-driven maintenance predictions.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Waat Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market = $8.7 Billion in 2023, CAGR 28.5% (source: Market.us). Investment score 69.6/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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