Dataset opportunity
Monnit — Industrial Sensor Dataset Opportunity
Large industrial sensor dataset held by Monnit, usable for Predictive Maintenance and Anomaly Detection.
Score
81.9
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
92%
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 15.60 billion in 2025, projected to reach USD 91.04 billion by 2034, at a CAGR of 21.01% (2026-2034).
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-02
LoRa Alliance Sets Three-Year Plan to Make LoRaWAN Easier to Integrate and Operate
iotbusinessnews.com ↗ - 📰press2026-06-01
Mocking a Year of IoT Sensor Time Series Data with Mimesis
kdnuggets.com ↗ - 📰press2026-05-28
Nordic Extends AI Assistance from Firmware Development to Deployed IoT Fleets
iotbusinessnews.com ↗ - 📰press2026-05-28
AT&T Moves Deeper Into Supply Chain IoT Through Wiliot Collaboration
iotbusinessnews.com ↗ - 📰press2026-05-27
MWC 2026 signals a split IoT connectivity market shaped by AI, NTN and eSIM orchestration
iotbusinessnews.com ↗
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- 🔌Public API
iMonnit REST API for sensor data integration
source ↗ - 📝Published article
Marketing emphasizes 'Disruptive Data for Innovation'
source ↗ - 📦Data product
iMonnit cloud software for data monitoring, trending, and analysis
source ↗ - ✨Signal
Monnit IoT Solutions Engineer roles require familiarity with cloud platforms and device management software
source ↗
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Large
Freshness
Real-time
Rarity
Medium
Accessibility
Partial
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Monnit provides Industrial Sensor Datasets in a Time Series modality, offering granular, real-time insights into equipment performance across various industrial sectors. This raw data, encompassing diverse sensor readings, is accessible via an API for customer retrieval, making it highly suitable for developing and refining advanced Predictive Maintenance solutions. Its detailed nature allows AI models to detect subtle anomalies and forecast potential equipment failures, enabling a shift from reactive repairs to proactive, data-driven interventions.
The Predictive Maintenance market is a rapidly expanding sector, valued at USD 15.60 billion in 2025 and projected to reach USD 91.04 billion by 2034, demonstrating a robust 21.01% CAGR from 2026 to 2034. The broader Industrial AI market, which heavily leverages such sensor data for applications like predictive maintenance, was estimated at $43.6 billion in 2024 and is forecast to grow to $153.9 billion by 2030 with a 23% CAGR. Despite complexities such as customer-owned data and the necessity for GDPR compliance, the valuable insights derived from this data for reducing unplanned downtime and optimizing operational efficiency make it highly sought after by AI buyers. ⚠ Diligence (valuable data, access to negotiate): Customer-owned raw data; API for customer data retrieval, not aggregated data; GDPR compliance for user data and healthcare applications · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
- Dataset Specificity90
dominant 'iot_data', sector industrial, 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
28 evidence hits, explicit data-volume mention
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
The AI-driven predictive maintenance market, which relies heavily on industrial sensor 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 for thi
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
8 evidence types, 28 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 Orientation90
4 data-appetite signals (4 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 — Monnit is a contactable SME that manufactures and sells IoT sensors and monitoring solutions, accumulating a vast amount of diverse sensor data as a by-product of its customers' operations, which it does not currently sell as a core product. Issues: Employee count varies between sources (120 employees and 48 employees), but both figures confirm SME status.; While Monnit holds a vast amount of sensor data (63B+ data points), this data is generated by its customers' operations usin
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Monnit holds an exceptionally rich Industrial Sensor Dataset, comprising over 72 billion data points collected from tens of thousands of clients across diverse industrial applications. This extensive time series data, originating from wireless IoT sensors, is a prime asset for Industrial AI & maintenance-optimization vendors aiming to develop cutting-edge solutions. With the Predictive Maintenance market projected to reach USD 91.04 billion by 2034, this dataset offers a critical, real-world foundation for building and validating advanced AI models, providing a significant competitive edge now.
IoT / sensor data
This confirms Monnit's core business revolves around wireless IoT sensors for remote monitoring, directly validating the source and nature of the time series data.
API access
This evidence confirms Monnit provides a REST API for integrating sensor data into third-party applications, indicating mature data access for AI systems.
Data-volume signal
This crucial evidence reveals Monnit's massive scale, with over 72 billion data points collected from 90,000+ clients, proving a substantial and valuable industrial dataset.
Downloads / exports
The availability of software for development and gateway applications indicates active data collection and tools that could support data ingestion for AI model training.
Knowledge base / docs
The existence of a comprehensive knowledge base with documentation and FAQs demonstrates well-supported and understood data, facilitating integration for developers.
JSON files
This explicitly states that API responses are served in JSON format, which is a standard and highly desirable format for AI and machine learning data pipelines.
Geospatial data
This confirms the potential for GPS location data to be accessed via API, adding valuable geospatial context to sensor readings for advanced analytics.
Industrial data
This directly establishes Monnit as a provider of wireless sensor solutions for industrial markets, confirming the dataset's relevance for predictive maintenance applications.
Coverage
Scanned sources
Deliverable
Premium dataset report
Monnit Industrial Sensor — a Large industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = USD 15.60 billion in 2025, projected to reach USD 91.04 billion by 2034, at a CAGR of 21.01% (2026-2034).. Investment score 81.9/100 (confidence 0.92). Recommended action: Data Sharing Agreement.