Dataset opportunity
Pro Vigil — Sensor Telemetry Dataset Opportunity
Moderate sensor telemetry dataset held by Pro Vigil, 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
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 Predictive Maintenance market was valued at USD 14.2 billion in 2025, with a projected CAGR of 27.9% (2026-2033).
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
Sensor Telemetry Dataset
Modality
Time Series
Sector
other
Volume
Moderate
Freshness
Real-time
Rarity
Medium
Accessibility
Partial
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Pro Vigil holds a significant Sensor Telemetry Dataset derived from its remote surveillance operations, featuring rich Time Series data. The collection includes extensive `iot_data` and `event_streams` from deployed cameras and sensors, making it exceptionally well-suited for developing Predictive Maintenance models to forecast equipment failures before they happen.
The business value is substantial, addressing the global Predictive Maintenance market, which was valued at USD 14.2 billion in 2025 and is projected to grow at a CAGR of 27.9%. [3] While access requires navigating PII anonymization in video data and potentially shared data ownership, the rarity and high volume of labeled event data in this fast-growing market provide a crucial advantage for creating advanced AI solutions. [3] ⚠ Diligence (valuable data, access to negotiate): Video data contains PII (faces, license plates) requiring anonymization.; Data ownership may be shared with site owners (construction/dealerships) via service contracts.; High volume of labeled 'event' data (crime vs. false alarm) is likely proprietary. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Pro-Vigil owns a massive-scale sensor telemetry dataset, capturing hundreds of millions of real-world equipment events and system responses across industrial sectors. This data is a critical asset for AI vendors building predictive maintenance solutions, enabling them to train robust anomaly detection algorithms that anticipate equipment failure. In a global market projected to grow at nearly 28% annually, this unique collection of ground-truth data offers a significant competitive advantage for optimizing industrial operations.
See dimension details ↓- Dataset Specificity74
dominant 'iot_data', sector other, 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 Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
AI buyer demand is exceptionally high, driven by the need for vast, real-world sensor and event data to capitalize on the 27.9% CAGR of the predictive maintenance market. [3]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility48
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 Feasibility66
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 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 — 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 — The company's core business is selling AI-powered remote video monitoring and security as a service, which is a form of intelligence product, making it a bad fit. Issues: Company's core product is selling intelligence/analytics derived from sensor data (video feeds), not a byproduct of a different operational business. [9, 14, 17; The company explicitly markets itself as an 'AI video surveillance company' and a provider of 'remote video monitoring solutions'. [9, 10, 14]; The business model is 'surveillance as a service' on a subscription basis, which falls under the exclusion criteria. [4, 15]; Their long-term goal is to extract meaningful insights from video feeds for customers, confirming they are in the business of selling intelligence. [16]
- Deep Qualification80
✓ pass — Pro-Vigil is a strong data holder candidate, selling AI-powered remote security services and generating a valuable byproduct of event and equipment telemetry data. While data ownership terms are not publicly available, the dataset's direct relevance to predictive maintenance is confirmed by their own equipment health monitoring service.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Downloads / exports
The company publishes industry reports and case studies, demonstrating deep domain expertise in sectors like freight & logistics, which provides valuable context for the primary sensor data.
Event streams
The dataset contains hundreds of millions of time-stamped motion alerts from construction and automotive sites, providing the high-volume event data essential for training predictive models.
Image collection
The holder maintains a log of thousands of verified security interventions, which serves as a high-value, labeled dataset for training and validating threat detection algorithms.
IoT / sensor data
The data includes a log of over 5.2 million deployed audio/visual deterrents, offering a unique time-series signal of system responses to detected events, crucial for modeling equipment behavior.
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
Pro Vigil Sensor Telemetry — a Moderate sensor telemetry dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at USD 14.2 billion in 2025, with a projected CAGR of 27.9% (2026-2033) (source: Grand View Research). [3]. Investment score 45.0/100 (confidence 0.58). Recommended action: Data Sharing Agreement.
From the marketplace
Explore live data opportunities
Sresolar — Maintenance Logs Dataset Opportunity
View opportunity →industrialGiga Storage — Industrial Sensor Dataset Opportunity
View opportunity →industrialTokamakenergy — Industrial Sensor Dataset Opportunity
View opportunity →Data Academy
Learn before you deal
- 5 Mistakes That Drive Buyers Away3 min read
- Why Buy External Data?3 min read
- Buying Data Without Mistakes3 min read