Dataset opportunity
Kotra — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Kotra, usable for Predictive Maintenance and Anomaly Detection.
Score
75.8
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 size (indicative estimate)
Global Predictive Maintenance Market was valued at $13.65 billion in 2025, with a projected CAGR of 24.30% through 2034.
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.
- ✨Signal
Real-time tracking and tracing systems implementation
source ↗
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Kotra holds a proprietary Mobility Telemetry Dataset generated by its large fleet of temperature-controlled trucks and warehouses. This Time Series data includes granular `geo_data`, `industrial_data`, and `iot_data`, providing a rich, operational foundation for building and training high-fidelity Predictive Maintenance models to anticipate vehicle component failures, reduce unplanned downtime, and optimize fleet-wide maintenance schedules.
The global market for predictive maintenance is expanding rapidly, valued at $13.65 billion in 2025 and projected to grow at a CAGR of 24.30%. While access requires high-level corporate engagement due to the data's origin within the larger Kotra Group, its rarity and direct applicability to this high-growth market make it a crucial asset for AI buyers aiming to secure a competitive advantage in logistics. ⚠ Diligence (valuable data, access to negotiate): Data is generated by a large proprietary fleet of temperature-controlled trucks and warehouses.; Operational data (telemetry/IoT) is distinct from client-owned cargo manifests.; Part of the larger Kotra Group, requiring high-level corporate engagement. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves the holder operates a large, proprietary network of over 150 temperature-controlled trucks, generating continuous time-series data from industrial cooling engines and automated sensors. This unique dataset is a prime asset for industrial AI vendors building predictive maintenance solutions for the cold-chain logistics sector. In a global market projected to grow at over 24% annually, this data directly enables the training of models that can anticipate equipment failure, optimize maintenance schedules, and prevent costly downtime.
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 Demand90
AI buyer demand is exceptionally high, driven by the rapid expansion of the Predictive Maintenance market, which is projected to grow at a CAGR of 24.30%.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
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 License92
ownership=company_owned, licensing=clean
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. - ICP Audit92
✓ good target — Kotra is a strong target; it's a family-run SME in temperature-controlled logistics with a large fleet generating proprietary telemetry and temperature data as a by-product of its core transport business. Issues: The company is actively digitizing its processes, including using an eCMR platform, which means they are aware of their data but their focus is on internal opti; Employee count varies across sources (101-200, 501-1000), but it firmly sits within the SME or slightly larger category.
- Deep Qualification90
✓ pass — Kotra is a strong data holder candidate. It operates a large, modern fleet equipped with telematics for real-time temperature and location monitoring, generating a valuable mobility and IoT dataset as a byproduct of its core logistics business. The company has explicitly stated its intention to use this data for process optimization.
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 indicates proprietary time-series data from trailer-mounted cooling engines and temperature sensors, which is essential for training algorithms to predict component failure.
Geospatial data
This tabular data quantifies the operational scale, confirming a fleet of over 150 trucks and defining the European geographic scope, which provides crucial context for analyzing asset performance across different routes.
Industrial data
This confirms the generation of continuous, automated time-series data from temperature-controlled environments, a critical input for models that optimize energy consumption and ensure cold-chain integrity.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
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Kotra 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 was valued at $13.65 billion in 2025, with a projected CAGR of 24.30% through 2034 (source: Fortune Business Insights).. Investment score 75.8/100 (confidence 0.49). Recommended action: Acquire.
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Learn before you deal
- Acquire Rare, Compliant Data3 min read
- Data marketplaces, explained4 min read
- Data licensing, term by term4 min read