Dataset opportunity
Minut — Sensor Telemetry Dataset Opportunity
Large sensor telemetry dataset held by Minut, usable for Predictive Maintenance and Anomaly Detection.
Score
48
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
83%
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 = $13.4 billion in 2025, CAGR 23.2%.
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.
- 📦Data product
Real-time noise, occupancy, and climate monitoring insights
source ↗
Profile
Dataset profile
Type
Sensor Telemetry Dataset
Modality
Time Series
Sector
other
Volume
Large
Freshness
Real-time
Rarity
Medium
Accessibility
Partial
Legal
Owned by the company — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Minut holds a proprietary Sensor Telemetry Dataset generated by its hardware installed in residential properties. This Time Series data includes metrics such as noise level, temperature, humidity, and motion, providing a rich, continuous stream of environmental and device-health information ideal for training Predictive Maintenance models to anticipate hardware failure or anomalous environmental conditions.
This data is exceptionally valuable in the Global Predictive Maintenance market, which was valued at $13.4 billion in 2025 and is projected to grow at a 23.2% CAGR. [1] Despite access complexities requiring strict anonymization of residential data and contractual verification of data ownership, the dataset's rare nature and privacy-first design (no cameras or audio recordings) make it a highly sought-after asset for developing next-generation AI solutions in a rapidly expanding market. ⚠ Diligence (valuable data, access to negotiate): Data is collected via proprietary hardware sensors in private residences, requiring strict anonymization.; Privacy-first design (no cameras/recordings) simplifies some aspects but limits raw audio access.; Ownership of raw telemetry vs. customer insights needs contractual verification. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Public evidence confirms Minut possesses a substantial volume of time-series data generated by its proprietary IoT sensors. This dataset captures real-world environmental conditions, including temperature, humidity, noise, and occupancy levels, which are critical for training predictive maintenance algorithms. For AI vendors in the industrial and maintenance optimization space, this data offers a direct path to developing more accurate models for a global market projected to reach $13.4 billion by 2025. Acquiring this unique telemetry data could significantly accelerate product development and enhance competitive advantage in a rapidly expanding sector.
See dimension details ↓- Dataset Specificity62
dominant 'iot_data', sector other, 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 Rarity46
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume94
10 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 Demand90
AI buyer demand is exceptionally high, driven by the global Predictive Maintenance market's rapid expansion at a 23.2% CAGR as companies increasingly adopt IoT and AI to reduce operational downtime. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility60
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
7 evidence types, 10 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License62
ownership=company_owned, 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 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 Audit75
⚠ review — Minut is a bad target because its core business is selling a subscription-based intelligence service providing real-time insights and analytics from its proprietary sensor data, making it an intelligence vendor, not a holder of dormant data. Issues: The company's core product is not the hardware sensor itself, but the subscription-based software platform that provides 'proactive property insights' and analy; This business model of selling access to analytics and alerts is a form of 'Intelligence-as-a-Service', which is explicitly excluded by the ICP as a 'bad target; The company already uses AI and machine learning in its products, such as for cigarette smoke detection and guest communication, confirming it sells intelligenc; They offer a commercial API for customers to access sensor data and build custom integrations, indicating a clear, existing data monetization strategy. [21]
- Deep Qualification70
✓ pass — Minut is a data_holder with a highly coherent dataset for the niche. A recent Series B extension provides a strong trigger, but data ownership rights are not explicitly defined in their legal documents, creating a key diligence hurdle.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Event streams
The company operates real-time event streams that track property conditions like occupancy, providing the continuous data flow necessary for dynamic AI models that optimize resource allocation.
API access
A modern, REST-based API is available, enabling buyers to programmatically integrate the sensor data into their own systems and AI workflows with minimal engineering effort.
IoT / sensor data
The dataset contains granular IoT data including temperature, humidity, and device battery levels, which are direct inputs for training high-value predictive maintenance models.
Developer portal
The presence of a developer portal with API documentation signals that the data is well-structured and supported, reducing integration friction for a buyer's engineering team.
JSON files
The use of JSON payloads confirms the data is delivered in a standard, machine-readable format, ensuring immediate compatibility with modern AI development stacks.
Knowledge base / docs
A technical knowledge base with API documentation provides further evidence of data maturity, giving buyers confidence in the dataset's usability and long-term value.
Downloads / exports
A customer-facing web application with a download function exists, suggesting an established mechanism for users to retrieve files that could be adapted for bulk data delivery.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Deliverable
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Minut Sensor Telemetry — a Large sensor telemetry dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market = $13.4 billion in 2025, CAGR 23.2% (source: Market.us). Investment score 48.0/100 (confidence 0.83). Recommended action: Data Sharing Agreement.
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