Dataset opportunity
Thefirstmile — Industrial Sensor Dataset Opportunity
Large industrial sensor dataset held by Thefirstmile, usable for Predictive Maintenance and Anomaly Detection.
Score
47.5
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
65%
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 valued at $15.10B in 2025, projected to grow at a 31.1% CAGR.
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.
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Large
Freshness
Real-time
Rarity
Medium
Accessibility
Partial
Legal
Mixed ownership — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Thefirstmile holds a substantial Industrial Sensor Dataset composed of Time Series data from its operational systems. This includes granular industrial_data and iot_data streams, alongside business records and geo-data, providing a rich, proprietary foundation for developing and training high-fidelity Predictive Maintenance models for industrial equipment and logistics.
This data is situated within the global Predictive Maintenance market, a sector valued at $15.10 billion in 2025 and projected to grow at a remarkable 31.1% CAGR. [5] While access requires negotiation—as logistics data needs extraction and some granular data ownership is contractually shared—the core, unmonetized aggregate national material flow represents a rare and valuable asset, justifying the access diligence for a significant market opportunity. [5] ⚠ Diligence (valuable data, access to negotiate): Data is partially exposed via the 'Recycling Data Studio' for clients, but the aggregate national material flow remains unmonetized.; Ownership of granular waste composition data may be contractually shared with clients.; Logistics and route optimization data is proprietary but requires extraction from operational systems. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Thefirstmile owns a unique stream of real-world operational data from its UK-wide industrial recycling and waste management services. The dataset contains rich IoT and time-series signals from sensor-equipped assets, ideal for training sophisticated predictive maintenance models. For industrial AI vendors, this data offers a direct path to developing and validating algorithms in a market projected to grow at over 30% annually, addressing urgent needs for maintenance-optimization and operational efficiency.
See dimension details ↓- 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 Volume70
6 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 Demand98
AI buyer demand is exceptionally high, driven by the rapid market expansion for Predictive Maintenance solutions, which is growing at a 31.1% CAGR. [5]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility56
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 Strength89
5 evidence types, 6 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 Orientation22
0 data-appetite signals (0 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 Audit58
⚠ review — The company's core business is selling waste management services that include a data and analytics platform for their customers, making them a seller of intelligence, not a holder of dormant data. Issues: The company's core product is waste management services, which is a good operational business. [3, 9]; However, they actively productize the resulting data, offering customers a 'Recycling Data & Carbon Reporting Studio' with analytics on waste, carbon impact, an; This platform provides 'data-driven insights' and is a key feature of their service, meaning they are already selling intelligence derived from the data they co; Their vision is to become a 'leading circular resource platform, connecting customers, collection networks, processing capabilities, and data', which confirms t
- Deep Qualification90
✓ pass — The target is a data holder whose core business is waste management, not data sales. The data, a byproduct of its logistics and processing operations, is highly coherent with the 'Industrial Sensor Dataset' label and the 'Industrial Monitoring' niche. A recent acquisition by a private equity firm to scale the platform serves as a strong trigger for a data-centric strategic shift.
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 time-series data generated by IoT sensors on waste management assets, such as smart bins, providing granular usage and weight measurements valuable for training predictive maintenance algorithms.
Downloads / exports
This tabular data tracks customer engagement with recycling resources, offering a proxy for customer segmentation and interest in specific industrial waste streams.
Industrial data
This evidence points to structured time-series data detailing waste collection volumes across more than 50 recycling streams and specific sites, essential for building resource allocation and optimization models.
Geospatial data
This is logistical and geospatial data from a UK-wide collection fleet with a 99.96% success rate, critical for training route optimization and vehicle maintenance models.
business_records
This dataset includes structured compliance and carbon reporting documents, enabling AI models to correlate operational performance with ESG factors and regulatory frameworks.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Thefirstmile 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 valued at $15.10B in 2025, projected to grow at a 31.1% CAGR (source: Market Research Future). [5]. Investment score 47.5/100 (confidence 0.65). Recommended action: License.
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