Dataset opportunity
Metos — Sensor Telemetry Dataset Opportunity
Moderate sensor telemetry dataset held by Metos, usable for Predictive Maintenance and Anomaly Detection.
Score
70
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
49%
Action
Acquire
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 for farm machinery market = $1.42B in 2024, CAGR 17.8%.
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
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Metos holds a rich Sensor Telemetry Dataset structured as Time Series data, aggregated from on-farm hardware. This collection includes iot_data, geo_data, and an image_collection, providing a multi-modal foundation ideal for training Predictive Maintenance models to forecast equipment failure, optimize irrigation, or predict crop diseases.
The business value is underscored by the global predictive maintenance for farm machinery market, which reached $1.42 billion in 2024 and is projected to grow at a 17.8% CAGR. [8] While secondary use of this data requires careful contractual review with the farmers who own the generating hardware, its aggregated, real-world nature makes it an exceptionally valuable asset for any AI buyer aiming to capture a share of this rapidly expanding market. [8] ⚠ Diligence (valuable data, access to negotiate): Data is generated by hardware owned by farmers but aggregated on the FieldClimate platform; Company sells both hardware and data-driven intelligence (disease models); Secondary use of agricultural data requires careful contractual review with end-users · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Metos owns a proprietary, multi-decade collection of agricultural sensor telemetry, enriched with pest imagery and deep historical environmental data. This is a critical asset for any AI vendor developing predictive maintenance solutions for farm machinery, a market valued at $1.42 billion and growing at nearly 18% annually. The dataset's unique combination of time-series IoT data, imagery, and long-term geospatial records provides the ground truth needed to train robust AI that can anticipate equipment failure and optimize performance in diverse, real-world conditions.
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 Rarity82
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 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 growth of the predictive maintenance in agriculture market, which is projected to expand at a 17.8% CAGR. [8]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License36
ownership=mixed, licensing=rights_unclear
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, 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.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Live from Ag in Motion at the John Deere booth, Kristjan Hebert and Evan Shout are joined by Deanna Kovar, president of Deere's Ag and Turf Division. Kovar grew up on a Wisconsin dairy farm, started as a summer intern decades ago, and has since worked her way through nearly every corner of the company.... <a href="https://www.realagriculture.com/2026/07/the-truth-about-trust-and-tech-with-deanna-kovar-truth-about-ag-podcast-ep-63/">Read More</a></p>”
- “Terres Univia, Terres Inovia et la Fop estiment la production nationale de colza autour de 4,7 millions de tonnes en 2026, soit un niveau proche de 2025. Ce résultat est imputable à la hausse des surfaces, le rendement moyen étant en nette baisse sur un an.”
- “<p><img alt="" class="attachment-thumbnail size-thumbnail wp-post-image" height="150" src="https://d3hid44mqnfbhw.cloudfront.net/precisagms/wp-content/uploads/2026/07/SmartIrrigation-150x150.jpg" width="150" />New survey findings reveal how shifting priorities around water, costs, and resilience are reshaping investment decisions across modern agricultural operations.</p> <p>The post <a href="https://www.globalagtechinitiative.com/in-field-technologies/irrigation/what-growers-are-telling-the-global-irrigation-industry/">What Growers Are Telling the Global Irrigation Industry</a> appeared first”
IoT / sensor data
Metos provides extensive time-series telemetry from thousands of in-field sensors, capturing environmental variables like soil moisture and humidity that are essential for modeling equipment stress and predicting component failure.
Image collection
The collection includes high-resolution pest imagery from automated traps, offering a unique contextual layer for AI models to correlate environmental events with specific operational stresses on machinery.
Geospatial data
This dataset contains over 35 years of geospatial environmental data, providing the deep historical context across diverse climates and crop types needed to build and validate highly accurate, generalizable predictive 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.
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Deliverable
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Learn before you deal
- What you are entitled to sell3 min read
- 5 Mistakes That Drive Buyers Away3 min read
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