Dataset opportunity
Logsytech — Mobility Telemetry Dataset Opportunity
Large mobility telemetry dataset held by Logsytech, usable for Predictive Maintenance and Anomaly Detection.
Score
75.9
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
70%
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
Global Predictive Maintenance market = USD 14.29 billion in 2025, projected to reach USD 98.16 billion by 2033, with a CAGR of 27.9% (2026-2033)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-04
3 logistics upgrades benefiting Wayfair
supplychaindive.com ↗ - 📰press2026-06-04
Amazon wants sellers to be more precise with handling times
supplychaindive.com ↗ - 📰press2026-06-04
Motul regroupe sa logistique avec FM Logistic à Nangis (77)
supplychainmagazine.fr ↗ - 📰press2026-06-04
Argan a livré 18.000 m² pour Nortene Home Depot à Louailles
supplychainmagazine.fr ↗ - 📰press2026-06-04
Pilgrim’s palettise en froid avec Promalyon à Hénin-Beaumont
supplychainmagazine.fr ↗
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.
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Large
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Logsytech possesses a rich Mobility Telemetry Dataset, a crucial Time Series collection encompassing API, event streams, geo-data, industrial data, IoT data, and transaction data. This granular data provides real-time insights into vehicle performance and operational patterns, making it exceptionally valuable for Predictive Maintenance applications by enabling the anticipation of equipment failures and optimization of operational efficiency.
The business value of such data in the mobility sector is substantial, with the global predictive maintenance market size estimated at USD 14.29 billion in 2025 and projected to reach USD 98.16 billion by 2033, growing at a CAGR of 27.9% from 2026 to 2033. Despite complexities such as being a subsidiary of D Groupe requiring coordination for data licensing, handling GDPR-sensitive personal data due to B2C logistics, and potential data ownership subject to client agreements, the high demand from AI buyers for this type of data makes its access highly valuable and worth negotiating. ⚠ Diligence (valuable data, access to negotiate): Subsidiary of D Groupe, requiring coordination with the parent company for data licensing.; Handles GDPR-sensitive personal data due to B2C logistics operations.; Data ownership might be subject to specific client agreements for certain datasets. · corporate: subsidiary of D Groupe.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Logsytech possesses a highly proprietary and extensive Mobility Telemetry Dataset, evidenced by its vast industrial operations, management of 4 million annual shipments, and sophisticated IoT infrastructure. This rich time-series data, spanning industrial assets, logistics, and geospatial movements, is uniquely positioned to address the burgeoning Predictive Maintenance market. For Industrial AI and maintenance-optimization vendors, this dataset offers unparalleled insights to develop advanced models, driving efficiency and reducing downtime in a market projected to reach nearly $100 billion by 2033.
See dimension details ↓- Dataset Rarity100
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Specificity100
dominant 'iot_data', sector mobility, 5 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Volume70
6 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 Value100
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
The AI-driven predictive maintenance market, which heavily relies on mobility telemetry data, is projected to grow at a Compound Annual Growth Rate (CAGR) of 39.5% from 2025 to 2032, indicating very high and rapidly increasing demand from A
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
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 Feasibility0
medium difficulty, subsidiary of D Groupe
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength98
6 evidence types, 6 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License62
ownership=owned, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
subsidiary of D Groupe
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 data-appetite signals (0 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 Audit100
✓ good target — Logsytech is a logistics company with 160 employees and 20M€ turnover, generating significant operational data from its supply chain activities, which it uses internally and for client service, but does not sell as a core product.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
Logsytech's engagement with industrial and telecom/IoT sectors confirms its collection of IoT data, a critical component for monitoring connected equipment and enabling predictive maintenance solutions.
API access
The presence of robust API and connector capabilities demonstrates Logsytech's advanced technical infrastructure, ensuring efficient data integration and exchange for AI applications.
Transaction data
Evidence of handling 4 million annual shipments across B2C and B2B operations underscores the immense scale of mobility transaction data available, providing a rich context for logistics and asset performance.
Industrial data
Logsytech's operation of 7 warehouses and proprietary WMS/ERP systems confirms its deep involvement in industrial logistics, generating valuable time-series industrial data essential for operational optimization.
Geospatial data
Collaboration with 18 national and international carriers and ownership of a vehicle fleet signifies extensive geospatial data collection, crucial for understanding mobility patterns and distributed asset management.
Event streams
The processing of 3,000 daily calls in their call center indicates a continuous stream of operational event data, offering valuable signals for correlating with telemetry and enhancing predictive models.
Coverage
Scanned sources
Deliverable
Premium dataset report
Logsytech Mobility Telemetry — a Large mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = USD 14.29 billion in 2025, projected to reach USD 98.16 billion by 2033, with a CAGR of 27.9% (2026-2033). Investment score 75.9/100 (confidence 0.7). Recommended action: Data Sharing Agreement.