Dataset opportunity
Tankpool24 — Mobility & Geospatial Dataset Opportunity
Moderate mobility & geospatial dataset held by Tankpool24, usable for Geo AI and Routing & Forecasting.
Score
48
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
58%
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 Geospatial Analytics market = $108.03 billion in 2026, CAGR 12.72%.
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 & Geospatial Dataset
Modality
Tabular
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
Medium
Accessibility
Restricted
Legal
Aggregated / third-party — GDPR-sensitive (PII review)
Buyer persona
Geospatial-AI & mobility-analytics teams
Tankpool24 holds a significant Tabular Mobility & Geospatial Dataset derived from its extensive network of fueling stations. The data includes granular `transaction_data`, `iot_data` from fuel pumps, and precise `geo_data`, offering a real-world view of commercial vehicle movement and fueling patterns, making it exceptionally well-suited for training and validating Geo AI models for logistics and supply chain optimization.
The business value is substantial, operating within the global geospatial analytics market, which is projected to reach $108.03 billion in 2026 with a CAGR of 12.72%. [2] While access must be negotiated due to complexities like distributed ownership rights and the presence of PII, the rarity and high-fidelity nature of this operational data make it a premium asset for AI buyers seeking a distinct competitive edge in route planning and demand forecasting. ⚠ Diligence (valuable data, access to negotiate): Data is aggregated from a network of independent mid-sized petroleum entrepreneurs; Dataset includes PII such as driver card IDs and specific vehicle mileage; Ownership rights may be distributed between the central GmbH and regional partners · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Public evidence confirms Tankpool24 possesses a proprietary dataset linking high-frequency vehicle transactions to precise locations across its European network. This asset is critical for Geospatial-AI and mobility-analytics teams looking to build advanced predictive models for commercial fleet behavior. In a geospatial analytics market projected to surpass $100 billion, this data provides the ground truth needed for superior route optimization, demand forecasting, and competitive intelligence.
See dimension details ↓- Dataset Specificity90
dominant 'geo_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 Rarity58
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume64
5 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 Geo AI
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand92
AI buyer demand is extremely high, driven by the rapid growth of the geospatial analytics market, which is expanding at a 12.72% CAGR. [2]
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 Strength77
4 evidence types, 5 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License10
ownership=aggregated, 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 Orientation50
2 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. - ICP Audit67
⚠ review — Tankpool24's core business is selling fleet management and telematics software services, which are derived from the operational data, making it a bad fit as it already sells intelligence. Issues: Company's core business is selling intelligence/software derived from data (fleet management, telematics, consumption analysis).; The company is a consortium of 15-19 medium-sized mineral oil companies, which complicates its SME status and data ownership.; It offers data export and consumption analysis directly to customers, indicating they already monetize the data's value.
- Deep Qualification80
✓ pass — Tankpool24 is a data holder with a highly coherent mobility dataset, but data access is complex due to a partner-based ownership structure and the presence of PII.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Geospatial data
The dataset contains explicit location data tied to a physical network of over 2,200 fueling stations across Europe, essential for any geospatial analysis of fleet movement.
Downloads / exports
The company operates a mobile app for both Android and iOS, establishing a direct channel for collecting first-party user data and behavior signals.
Transaction data
The company captures granular transactional data for each refueling event, including vehicle mileage, quantity, date, and time, which is foundational for building detailed mobility profiles.
IoT / sensor data
Data is collected via modern IoT infrastructure, including digital driver cards, ensuring a high-fidelity, secure stream of time-series data directly from vehicles and drivers.
Marketplace
Dataset details
Detailed schema & sample available on access request.
Want this data?
Request access — we broker a secure deal room. Operator-reviewed, no automatic sharing.
This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Tankpool24 Mobility & Geospatial — a Moderate mobility & geospatial dataset (Tabular modality) in the mobility domain. Primary AI use-case: Geo AI. Market signal: Global Geospatial Analytics market = $108.03 billion in 2026, CAGR 12.72% (source: Mordor Intelligence). Investment score 48.0/100 (confidence 0.58). Recommended action: Data Sharing Agreement.
From the marketplace
Explore live data opportunities
Gallaghertransport — Regulatory Records Dataset Opportunity
View opportunity →mobilityPaua — Mobility Telemetry Dataset Opportunity
View opportunity →mobilityTrucksters — Mobility Telemetry Dataset Opportunity
View opportunity →