Dataset opportunity
Onomondo — Mobility Telemetry Dataset Opportunity
Large mobility telemetry dataset held by Onomondo, usable for Predictive Maintenance and Anomaly Detection.
Score
45
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
77%
Action
License
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 = $14.2B in 2025, CAGR 27.9%.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-16
Onomondo launches its own SGP.32 eSIM IoT Remote Manager to prevent vendor lock-in undermining cellular IoT
iotbusinessnews.com ↗
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.
- 🔌Public API
Onomondo Developer API for network management and real-time insights
source ↗
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Large
Freshness
Real-time
Rarity
Medium
Accessibility
Open / API
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Onomondo provides a Mobility Telemetry Dataset structured as Time Series data, containing `iot_data` and `geo_data` from a vast network of connected assets. This technical network telemetry, accessible via APIs and event streams, is directly applicable for Predictive Maintenance use cases, enabling AI buyers to build models that forecast equipment failure by analyzing real-time operational and connectivity patterns across a global fleet.
The global market for Predictive Maintenance was valued at $14.2 billion in 2025 and is projected to expand at a CAGR of 27.9%. [1] While access requires extraction from Onomondo's core infrastructure, the dataset's value is rooted in its proprietary global network performance metadata across 680+ carriers, offering a rare and valuable resource for building high-performance AI models. The data's non-PII nature also facilitates a more straightforward licensing process. ⚠ Diligence (valuable data, access to negotiate): Data consists of technical network telemetry rather than PII, facilitating licensing.; Proprietary layer resides in the global network performance metadata across 680+ carriers.; Access requires extraction from their core connectivity management infrastructure. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Onomondo possesses a continuous stream of global IoT telemetry from mobile assets, delivered as structured time-series data. For Industrial AI vendors, this dataset is the essential fuel for building and validating predictive maintenance models that can anticipate equipment failure. In a market projected to reach $14.2B by 2025, this data offers a direct path to creating high-value solutions that pinpoint operational risks in vehicle fleets and industrial machinery before they cause outages.
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 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 Volume100
12 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 high, driven by the rapid expansion of the global Predictive Maintenance market, which is projected to grow at a CAGR of 27.9%. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility90
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 Feasibility84
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength100
5 evidence types, 12 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, 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 Audit50
⚠ review — Onomondo's core business is selling IoT connectivity and a platform for its management, not the data itself, making it a technology vendor and a bad fit. Issues: Core business is selling a connectivity platform/service, which is a form of tooling/SaaS, not selling dormant data. [2, 3, 14]; The company's value proposition is providing the infrastructure for IoT data transmission, not holding or owning the data that passes through its network. [11, ; Onomondo explicitly offers customers 'freedom to leave', emphasizing that the customer owns the SIMs and associated keys, which is contrary to the ICP's goal of; The company provides tools for customers to monitor and manage their own data traffic, positioning itself as a platform/intelligence provider, which is an exclu
- Deep Qualification70
✓ pass — Onomondo operates a global IoT connectivity platform, making it a data_holder of valuable, non-PII network telemetry. While the data is a plausible byproduct and highly relevant for predictive maintenance, ownership of this telemetry is mixed and licensing rights are unclear without a master service agreement, requiring direct negotiation.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This is the core time-series dataset, capturing real-time telemetry from global IoT fleets and mobile assets, which is the essential ground truth for training and validating predictive maintenance algorithms.
API access
The existence of a developer-ready API and cloud connectors confirms the data is structured for programmatic access, enabling seamless integration into a buyer's existing AI workflows.
Downloads / exports
Downloadable technical reports show a history of analyzing device performance to reduce costs, indicating the underlying data is rich with features relevant to asset lifespan and maintenance optimization.
Geospatial data
The dataset is enriched with global location data from over 680 networks, providing the crucial geographic context needed to model asset performance across diverse operating environments from highways to remote sites.
Event streams
The data includes discrete event streams specifically designed to identify network issues, offering a direct source of labeled anomalies ideal for training fault-detection models.
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
Premium dataset report
Onomondo 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 = $14.2B in 2025, CAGR 27.9% (source: Grand View Research). [1]. Investment score 45.0/100 (confidence 0.77). Recommended action: License.
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