Dataset opportunity
Expedis — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Expedis, usable for Predictive Maintenance and Anomaly Detection.
Score
64.6
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 = $14.2 billion in 2025, CAGR 27.9%.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-08
PJ Expedis a Muff Logistics mají nové vlastníky, Martin Jeřábek zůstává ve skupině
systemylogistiky.cz ↗
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 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
Expedis holds a valuable Mobility Telemetry Dataset in a Time Series modality, compiled from its proprietary IoT devices, business records, and transaction data. This rich, real-world data provides detailed operational histories of vehicle components, making it exceptionally well-suited for training and validating Predictive Maintenance AI models to forecast equipment failures before they occur.
The business value is substantial, tapping into the global Predictive Maintenance market, which was valued at $14.2 billion in 2025 and is projected to grow at a 27.9% CAGR. While access requires navigating GDPR-sensitive data through strict anonymization and potential client confidentiality agreements due to siloed systems, the rarity and proven utility of this iot_data for reducing operational costs and downtime make it a compelling asset for AI buyers in the mobility and logistics sectors. ⚠ Diligence (valuable data, access to negotiate): Identity verification data is highly GDPR-sensitive and requires strict anonymization.; Logistics data for the telecommunications sector may be subject to strict client confidentiality agreements.; Data is likely siloed within their proprietary WMS and digital signature platforms. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Expedis operates a significant electric vehicle fleet, generating proprietary IoT telemetry data at city scale. This rare, real-world time-series data is exactly what industrial AI vendors require to build and validate predictive maintenance algorithms for commercial EVs. In a market for predictive maintenance projected to reach $14.2 billion by 2025, this dataset offers a crucial competitive edge for optimizing fleet uptime and performance.
See dimension details ↓- Dataset Specificity78
dominant 'iot_data', sector mobility, 2 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
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 Value74
fit for Predictive Maintenance
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 exceptionally high, driven by the rapid 27.9% CAGR of the predictive maintenance market, which creates a strong need for high-quality, real-world telemetry data to improve operational efficiency.
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 Orientation56
2 data-appetite signals (2 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 1 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 Audit100
✓ good target — Expedis is an ideal target, as it's an operational SME in logistics and transport that generates proprietary fleet telemetry data as a by-product of its core business and does not appear to sell this data as a product. Issues: There are two distinct companies found: 'EXPEDIS spol. s r.o.' (IČO: 25455036) which is the primary logistics/transport company, and 'PJ EXPEDIS, spol. s r.o.'
- Deep Qualification80
✓ pass — The target is a logistics services provider, not a data holder in the sense of owning a monetizable byproduct. While it generates the hypothesized mobility telemetry data from its own fleet, its core business involves handling sensitive client operational data (for T-Mobile, O2, Vodafone) and personal data (identity verification), making data ownership and licensing extremely complex and restricted.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Transaction data
This tabular evidence indicates structured logs related to digital contracts and identity verification, which can provide valuable metadata for understanding the commercial activities driving fleet movements.
IoT / sensor data
This is direct proof of a large-scale electric vehicle fleet operation, generating the core time-series telemetry data essential for any predictive maintenance or CO2 reduction modeling.
business_records
This document confirms the company's operational focus on logistics and fulfillment for the electronics sector, providing critical context on the cargo types and delivery patterns their fleet supports.
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
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Deliverable
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Expedis 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 = $14.2 billion in 2025, CAGR 27.9% (source: Grand View Research). Investment score 64.6/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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Learn before you deal
- How a Data Transaction Works3 min read
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