Dataset opportunity
Magil — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Magil, usable for Predictive Maintenance and Anomaly Detection.
Score
72.5
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
49%
Action
Partnership (group-level)
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 $14.2 billion in 2025, projected to grow at a CAGR of 27.9% from 2026 to 2033.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-08-04
Little Mountain's third social housing building now built, with all of Holborn's promised units reaching 100% completion this fall
dailyhive.com ↗ - 📰press2026-08-03
Balfour Beatty tops out $385M Miami Beach hotel
constructiondive.com ↗ - 📰press2026-07-31
HS2 renegotiates contracts with construction giants
constructionenquirer.com ↗ - 📰press2026-07-29
City council approves Vancouver’s tallest tower project
constructioncanada.net ↗
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
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Magil holds a valuable Industrial Sensor Dataset structured as Time Series modality, incorporating real-world industrial_data and iot_data from its construction projects. This collection provides a robust foundation for developing and validating high-fidelity Predictive Maintenance models, as it captures the operational stress and performance of heavy equipment under authentic field conditions.
The business opportunity is significant, with the global Predictive Maintenance market valued at $14.2 billion in 2025, and projected to expand at a CAGR of 27.9%. [1] While access requires navigating complexities such as contractually shared data ownership and siloed data platforms, the rarity and specificity of this dataset offer a distinct competitive advantage for AI buyers in a rapidly growing market. ⚠ Diligence (valuable data, access to negotiate): Data ownership may be contractually shared with project developers (e.g., Brivia Group).; High commercial confidentiality regarding project costs and proprietary construction methodologies.; Data is siloed across various CDE platforms (Procore, Revizto) and legacy systems. · corporate: subsidiary of Fayolle Group.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Magil owns a proprietary stream of structured industrial data, including time-series signals from advanced Asset Information Models (7D BIM) that form the basis of a digital twin. This dataset is ideal for training sophisticated predictive maintenance algorithms, enabling AI vendors to model asset failure and optimize operational efficiency. In a global market projected to grow at nearly 28% annually, this rare, real-world data offers a significant competitive advantage for developing and validating next-generation industrial AI solutions.
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 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 extremely high, driven by the rapid expansion of the Predictive Maintenance market, which is projected to grow at a 27.9% CAGR. [1]
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 Feasibility15
medium difficulty, subsidiary of Fayolle Group
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 License70
ownership=owned, licensing=rights_unclear
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
subsidiary of Fayolle Group
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation73
3 data-appetite signals (3 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 4 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 Audit83
✓ good target — Magil is a large construction and engineering firm whose core business is building, not selling data; it heavily uses modern tech like LiDAR, BIM, and site management platforms, generating a wealth of operational data as a by-product, making it a strong target. Issues: The company has between 580-854 employees, which is larger than a typical SME.; It is a subsidiary of Fayonne Ltd., which may complicate decision-making.; The company is already very tech-forward, using terms like 'Big Data', so they may have internal data strategies in development.
- Deep Qualification90
✓ pass — Magil is a construction contractor that does not sell data but holds valuable, dormant datasets. Its use of modern technologies like IoT, BIM, and CDE platforms generates extensive industrial data, including sensor and equipment logs. However, data access is complex due to shared ownership with clients and partners, and its distribution across platforms like Procore.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This evidence indicates the generation of structured time-series data from multi-dimensional asset information models (7D BIM), which is highly valuable for building the digital twins that power predictive maintenance platforms.
Image collection
The company captures high-resolution LiDAR scans and 360-degree imagery of its industrial assets, providing crucial visual context to enrich time-series data for more accurate anomaly detection models.
Industrial data
This confirms a deliberate, systematic process for collecting structured Big Data, signaling high data quality and a reliable lineage that is critical for training enterprise-grade AI 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.
Coverage
Scanned sources
Deliverable
Premium dataset report
Magil Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at $14.2 billion in 2025, projected to grow at a CAGR of 27.9% from 2026 to 2033 (source: Grand View Research). [1]. Investment score 72.5/100 (confidence 0.49). Recommended action: Partnership (group-level).
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